The document summarizes a new algorithm for noise reduction and quality improvement in SAR interferograms using inpainting and diffusion. It presents an inpainting-diffusion algorithm to improve interferogram quality and DEM accuracy. It then describes applying the Complex Ginzburg-Landau equation to the inpainting scheme for SAR interferogram restoration. The algorithm uses inpainting to fill in discarded phase values below a threshold of coherence. It evaluates the algorithm's performance using Signal-to-Noise Ratio on an interferogram of Ariano Irpino, Italy.
Survey on Image Integration of Misaligned ImagesIRJET Journal
The document discusses methods for integrating misaligned images to improve image quality under low lighting conditions. It reviews previous works that combine images like flash/no-flash pairs to transfer details and color, but have limitations when images are misaligned. The paper proposes a new method using a long-exposure image and flash image that introduces a local linear model to transfer color while maintaining natural colors and high contrast, without deteriorating contrast for misaligned pairs. It concludes that handling misaligned images remains a challenge with existing methods and further work is needed.
Evaluating effectiveness of radiometric correction for optical satellite imag...Dang Le
One of our published researches in ACRS 31st in Hanoi.
It has been used for our project in processing optical satellite imagery to detect environmental pollution.
Canny Edge Detection Algorithm on FPGA IOSR Journals
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the high-level implementation of the Canny algorithm using Simulink. The design and system-level block diagram of the implementation on an FPGA is shown, including loading an input image and displaying the output. Simulation and synthesis results are presented, showing the resource utilization on a Spartan 3E FPGA board. The implementation provides real-time edge detection to interface an FPGA with a monitor.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
De-speckling of underwater ultrasound images ajujohnkk
This document summarizes a research paper on enhancing the quality of underwater images using a speckle reducing anisotropic diffusion algorithm. The key points are:
1) Underwater images suffer from issues like limited visibility, low contrast, and speckle noise which degrades image quality. Speckle reducing anisotropic diffusion (SRAD) is proposed to reduce speckle noise while preserving edges.
2) SRAD is based on partial differential equations and minimum mean square error filtering. It allows diffusion within homogeneous regions but inhibits diffusion across edges.
3) The document outlines the SRAD algorithm which uses a coefficient of variation to control diffusion based on local intensity gradients, reducing noise while sharpening edges.
Geometric wavelet transform for optical flow estimation algorithmijcga
This paper described an algorithm for computing the optical flow (OF) vector of a moving objet in a video sequence based on geometric wavelet transform (GWT). This method tries to calculate the motion between two successive frames by using a GWT. It consists to project the OF vectors on a basis of geometric wavelet. Using GWT for OF estimation has been attracting much attention. This approach takes advantage of the geometric wavelet filter property and requires only two frames. This algorithm is fast and able to estimate the OF with a low-complexity. The technique is suitable for video compression, and can be used for stereo vision and image registration.
Noise Removal in SAR Images using Orthonormal Ridgelet TransformIJERA Editor
Development in the field of image processing for reducing speckle noise from digital images/satellite images is a challenging task for image processing applications. Previously many algorithms were proposed to de-speckle the noise in digital images. Here in this article we are presenting experimental results on de-speckling of Synthetic Aperture RADAR (SAR) images. SAR images have wide applications in remote sensing and mapping the surfaces of all planets. SAR can also be implemented as "inverse SAR" by observing a moving target over a substantial time with a stationary antenna. Hence denoising of SAR images is an essential task for viewing the information. Here we introduce a transformation technique called ―Ridgelet‖, which is an extension level of wavelet. Ridgelet analysis can be done in the similar way how wavelet analysis was done in the Radon domain as it translates singularities along lines into point singularities under different frequencies. Simulation results were show cased for proving that proposed work is more reliable than compared to other de-speckling processes, and the quality of de-speckled image is measured in terms of Peak Signal to Noise Ratio and Mean Square Error
Survey on Image Integration of Misaligned ImagesIRJET Journal
The document discusses methods for integrating misaligned images to improve image quality under low lighting conditions. It reviews previous works that combine images like flash/no-flash pairs to transfer details and color, but have limitations when images are misaligned. The paper proposes a new method using a long-exposure image and flash image that introduces a local linear model to transfer color while maintaining natural colors and high contrast, without deteriorating contrast for misaligned pairs. It concludes that handling misaligned images remains a challenge with existing methods and further work is needed.
Evaluating effectiveness of radiometric correction for optical satellite imag...Dang Le
One of our published researches in ACRS 31st in Hanoi.
It has been used for our project in processing optical satellite imagery to detect environmental pollution.
Canny Edge Detection Algorithm on FPGA IOSR Journals
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the high-level implementation of the Canny algorithm using Simulink. The design and system-level block diagram of the implementation on an FPGA is shown, including loading an input image and displaying the output. Simulation and synthesis results are presented, showing the resource utilization on a Spartan 3E FPGA board. The implementation provides real-time edge detection to interface an FPGA with a monitor.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
De-speckling of underwater ultrasound images ajujohnkk
This document summarizes a research paper on enhancing the quality of underwater images using a speckle reducing anisotropic diffusion algorithm. The key points are:
1) Underwater images suffer from issues like limited visibility, low contrast, and speckle noise which degrades image quality. Speckle reducing anisotropic diffusion (SRAD) is proposed to reduce speckle noise while preserving edges.
2) SRAD is based on partial differential equations and minimum mean square error filtering. It allows diffusion within homogeneous regions but inhibits diffusion across edges.
3) The document outlines the SRAD algorithm which uses a coefficient of variation to control diffusion based on local intensity gradients, reducing noise while sharpening edges.
Geometric wavelet transform for optical flow estimation algorithmijcga
This paper described an algorithm for computing the optical flow (OF) vector of a moving objet in a video sequence based on geometric wavelet transform (GWT). This method tries to calculate the motion between two successive frames by using a GWT. It consists to project the OF vectors on a basis of geometric wavelet. Using GWT for OF estimation has been attracting much attention. This approach takes advantage of the geometric wavelet filter property and requires only two frames. This algorithm is fast and able to estimate the OF with a low-complexity. The technique is suitable for video compression, and can be used for stereo vision and image registration.
Noise Removal in SAR Images using Orthonormal Ridgelet TransformIJERA Editor
Development in the field of image processing for reducing speckle noise from digital images/satellite images is a challenging task for image processing applications. Previously many algorithms were proposed to de-speckle the noise in digital images. Here in this article we are presenting experimental results on de-speckling of Synthetic Aperture RADAR (SAR) images. SAR images have wide applications in remote sensing and mapping the surfaces of all planets. SAR can also be implemented as "inverse SAR" by observing a moving target over a substantial time with a stationary antenna. Hence denoising of SAR images is an essential task for viewing the information. Here we introduce a transformation technique called ―Ridgelet‖, which is an extension level of wavelet. Ridgelet analysis can be done in the similar way how wavelet analysis was done in the Radon domain as it translates singularities along lines into point singularities under different frequencies. Simulation results were show cased for proving that proposed work is more reliable than compared to other de-speckling processes, and the quality of de-speckled image is measured in terms of Peak Signal to Noise Ratio and Mean Square Error
Radiometric Calibration of Digital ImagesSean Thibert
This document summarizes Sean Thibert's capstone project to develop a radiometric calibration method for an off-the-shelf digital camera integrated into a UAV system. It describes conducting two tests using different calibration models - a linear model and an exponential model. The exponential model provided a better fit. A Python script was created to apply the calibration equations to images. Applying the calibration improved vegetation index results by correcting for issues like shadows and negative reflectance values. Further refinement of the method is needed, but it shows promise as a time and cost effective calibration solution.
Lift using projected coded light for finger tracking and device augmentationShang Ma
We present Lift, a visible light-enabled finger
tracking and object localization technique that allows users to
perform freestyle multi-touch gestures on any object’s surface in
an everyday environment. By projecting encoded visible patterns
onto an object’s surface (e.g. paper, display, or table), and
localizing the user’s fingers with light sensors, Lift offers users a
richer interactive space than the device’s existing interfaces.
Additionally, everyday objects can be augmented by attaching
sensor units onto their surface to accept multi-touch gesture
input. We also present two applications as proof of concept.
Finally, results from our experiments indicate that Lift can
localize ten fingers simultaneously with an average accuracy of
1.7 millimeter and an average refresh rate of 84 Hz with 31
milliseconds delay on WiFi and 23 milliseconds delay on serial
communication, making gesture recognition on non-
instrumented objects possible.
2-Dimensional Wavelet pre-processing to extract IC-Pin information for disarr...IOSR Journals
Abstract: Due to higher processing power to cost ratio, it is now possible to replace the manual detection methods used in the IC (Integrated Circuit) industry by Image-processing based automated methods, to detect a broken pin of an IC connected on a PCB during manufacturing, which will make the process faster, easier and cheaper. In this paper an accurate and fast automatic detection method is used where the top view camera shots of PCBs are processed using advanced methods of 2-dimensional discrete wavelet pre-processing before applying edge-detection. Comparison with conventional edge detection methods such as Sobel, Prewitt and Canny edge detection without 2-D DWT is also performed. Keywords :2-dimensional wavelets, Edge detection, Machine vision, Image processing, Canny.
Developing a classification framework for landcover landuse change analysis i...Carolina
The document discusses developing a land cover/land use classification framework for Chile using remote sensing data. It describes using mathematical morphology techniques like morphological attribute profiles to integrate spatial context into spectral classifications, which significantly improved classification accuracy in three test subsets of a Landsat image - forests (61.3% to 80.8%), urban (75.5% to 92.2%) and agriculture (62.2% to 89.2%). However, the high-dimensional feature space requires specialized machine learning methods and high-performance computing.
Denoising and Edge Detection Using SobelmethodIJMER
The main aim of our study is to detect edges in the image without any noise , In many of the images edges carry important information of the image, this paper presents a method which consists of sobel operator and discrete wavelet de-noising to do edge detection on images which include white Gaussian noises. There were so many methods for the edge detection, sobel is the one of the method, by using this sobel operator or median filtering, salt and pepper noise cannot be removed properly, so firstly we use complex wavelet to remove noise and sobel operator is used to do edge detection on the image. Through the pictures obtained by the experiment, we can observe that compared to other methods, the method has more obvious effect on edge detection.
IRJET - Underwater Object Identification using Matlab and MachineIRJET Journal
This document discusses underwater object identification using MATLAB and machine learning. It begins with an abstract that outlines using image processing techniques like color correction and enhancement to improve underwater image quality and resolution for object detection. The methodology section then describes the process, which includes image acquisition, preprocessing like color conversion and noise removal, feature extraction to determine object type, and using a NodeMCU to send data to the cloud. It tests this approach by capturing images of fish underwater and classifying them by type. The results show enhanced, higher quality images compared to the originals. In conclusion, this method effectively removes color distortions and increases contrast to identify underwater objects using deep learning frameworks.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document describes an image denoising technique called the TWIST (Transform With Iterative Sampling and Thresholding) method. It begins with background on common types of image noise like Gaussian, salt-and-pepper, and quantization noise. It then discusses related work using eigendecomposition and the Nystrom extension for denoising. The proposed TWIST method uses the Nystrom extension to approximate the filter matrix with a low-rank matrix, allowing efficient processing of the entire image. It performs eigendecomposition on sample pixels to estimate eigenvalues and eigenvectors, then iterates this process with thresholding to denoise the image while preserving edges.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image fusion is the process of combining two or more images with specific objects with more precision. It is very common that when one object is focused remaining objects will be less highlighted. To get an image highlighted in all areas, a different means is necessary. This is done by the Image Fusion. In remote sensing, the increasing availability of Space borne images and synthetic aperture radar images gives a motivation to different kinds of image fusion algorithms. In the literature a number of time domain image fusion techniques are available. Few transform domain fusion techniques are proposed. In transform domain fusion techniques, the source images will be decomposed, then integrated into a single data and will be reconstructed back into time domain. In this paper, singular value decomposition as a tool to have transform domain data will be utilized for image fusion. In the literature, the quality assessment of fusion techniques is mainly by subjective tests. In this paper, objective quality assessment metrics are calculated for existing and proposed techniques. It has been found that the new image fusion technique outperformed the existing ones.
Speckle noise reduction from medical ultrasound images using wavelet threshIAEME Publication
This document proposes a method for reducing speckle noise from medical ultrasound images using wavelet thresholding and anisotropic diffusion. It first takes the logarithm of noisy images to convert speckle noise from multiplicative to additive. It then applies median filtering, followed by wavelet denoising using soft thresholding of detail coefficients. Finally, it uses SRAD filtering to enhance contrast while retaining edges. The proposed method is tested on three images and is found to improve SNR and PSNR compared to other filters based on statistical measurements.
Vicarious radiometric calibration refers to techniques used to calibrate remote sensing data without relying on onboard calibrators. Field spectroradiometers can be used to collect ground reflectance spectra and atmospheric parameters needed for vicarious calibration. Accurate vicarious calibration allows correction of instrument drift over time and comparison of datasets from different sensors, enabling monitoring of climate variables. Portable spectroradiometers like ASD's FieldSpec models are well-suited for rapid collection of calibration target and atmospheric data in the field.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Parameterized Image Filtering Using fuzzy LogicEditor IJCATR
The principal source of blur in digital images arise during image acquisition (digitization) or transmission. The
performance of imaging sensors is affected by a variety of factors, such as the environmental conditions during image acquisition.
Blurry images are the result of movement of the camera during shooting (not holding it still) or the camera not being capable of
choosing a fast enough shutter speed to freeze the action under the light conditions. For instance, in acquiring images with a camera,
light levels and sensor temperature are major factors affecting the amount of blur in the resulting image.
Blur was implemented by first creating a PSF filter in MatLab that would approximate linear motion blur. This PSF was then
convolved with the original image to produce the blurred image. Convolution is a mathematical process by which a signal, in this case
the image, is acted on by a system, the filter, in order to find the resulting signal. The amount of blur added to the original image
depended on two parameters of the PSF: length of blur (in pixels), and the angle of the blur. This thesis work is going to provide a
new, faster, and more efficient noise reduction method for images corrupted with motion blur. This new filter has two separated steps
or phases: the detection phase and the filtering phase. The detection phase uses fuzzy rules to determine whether a image is blurred or
not. When blurry image is detected, Then we use fuzzy filtering technique focuses only on the on the real blurred pixels.
4.[23 28]image denoising using digital image curveletAlexander Decker
This document summarizes research on using curvelet transforms for image denoising. It begins by discussing limitations of wavelet transforms for image processing, including lack of directionality and shift sensitivity. Curvelet transforms overcome these issues by providing high directional specificity and approximate shift invariance. The document proposes using digital implementations of curvelet, ridgelet, and contourlet transforms to denoise images corrupted by different types of noise. It describes the steps taken, which include applying the transforms after adding noise, then calculating peak signal-to-noise ratio and mean squared error to compare reconstruction quality. The transforms are found to provide better denoising performance than wavelet transforms as measured by these metrics.
This document provides an overview of digital image processing. It discusses what image processing entails, including enhancing images, extracting information, and pattern recognition. It also describes various image processing techniques such as radiometric and geometric correction, image enhancement, classification, and accuracy assessment. Radiometric correction aims to reduce noise from sources like the atmosphere, sensors, and terrain. Geometric correction geometrically registers images. Image enhancement improves interpretability. Classification categorizes pixels. The document outlines both supervised and unsupervised classification methods.
A Secure Color Image Steganography in Transform Domain ijcisjournal
Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
Homomorphic Filtering of Speckle Noise From Computerized Tomography (CT) Imag...CSCJournals
Adaptive filters are needed to accurately remove noise from noisy images when the variance of noise present varies. Linear filter such as Exponential filter becomes effective in removing speckle noise when homomorphic filtering technique is used. In this paper, an Adaptive Centre- Pixel-Weighed Exponential Filter for removing speckle noise from CT images was developed. The new filter is based on varying the centre-pixel of the filter kernel based on the estimated speckle noise variance present in a noisy CT image. Ten samples of 85x73 CT images corrupted by speckle noise level ranging from 10% to 30% were considered and the new technique gave a reasonably accurate speckle noise filtering performance with an average Peak Signal to Noise Ratio (PSNR) of 70.2839dB compared to 69.0658dB for Wiener filter and 64.3711dB for the Binomial filter. The simulation software used in the paper is Matrix Laboratory (Matlab).
Nonlinear Transformation Based Detection And Directional Mean Filter to Remo...IJMER
In this paper, a novel two stage algorithm for the removal of random valued impulse noise
from the images is presented. In the first stage the noise pixels are detected by using an exponential
nonlinear function. The transformation of the pixels increases the gap between noisy and noise free
candidates which leads to an efficient detection. In the second stage, the directional differences between
the pixels in the four main directions are calculated. The mean values of the pixels which lie in the
direction of minimum difference are calculated and the noisy pixel values are replaced with the mean
value of the pixels lying in the direction of minimum difference. Experimental results show that proposed
method is superior to the conventional methods in peak signal to noise ratio.
Survey Paper on Image Denoising Using Spatial Statistic son PixelIJERA Editor
This document summarizes research on image denoising using spatial statistics on pixel values. It begins with an abstract describing an approach that uses adaptive anisotropic weighted similarity functions between local neighborhoods derived from Mexican Hat wavelets to improve perceptual quality over existing methods. It then reviews literature on various denoising techniques including non-local means, non-uniform triangular partitioning, undecimated wavelet transforms, anisotropic diffusion, and support vector regression. Key types of image noise like Gaussian, salt and pepper, Poisson, and speckle noise are described. Limitations of blurring and noise in digital images are discussed. In conclusion, the document provides an overview of image denoising research using spatial and transform domain techniques.
2_2011 IGARSS SMAP Applications Program Presentation.pdfgrssieee
The document discusses the SMAP mission and its applications program. The SMAP mission will provide global soil moisture and freeze/thaw maps to further understanding of water, energy and carbon cycles. The applications program aims to increase interaction between scientists, users and applications involved in the mission. It identifies current and potential user communities and funds projects using NASA Earth science data for decision making. The SMAP Applications Working Group works with these communities and the SMAP science team to optimize use of SMAP data products.
A SELF-ADJUSTIVE GEOMETRIC CORRECTION METHOD FOR SERIOUSLY OBLIQUE AERO IMAGE...grssieee
This document presents a self-adjustive geometric correction method for seriously oblique aerial images. It analyzes projection errors caused by the curvature of the Earth and terrain relief. A ternary quadratic polynomial model is used, with adjustments to better correct for relief-induced projection errors. Experiments on images taken at large viewing angles demonstrate the new model outperforms conventional models in correction accuracy and recovering locations of high objects.
Radiometric Calibration of Digital ImagesSean Thibert
This document summarizes Sean Thibert's capstone project to develop a radiometric calibration method for an off-the-shelf digital camera integrated into a UAV system. It describes conducting two tests using different calibration models - a linear model and an exponential model. The exponential model provided a better fit. A Python script was created to apply the calibration equations to images. Applying the calibration improved vegetation index results by correcting for issues like shadows and negative reflectance values. Further refinement of the method is needed, but it shows promise as a time and cost effective calibration solution.
Lift using projected coded light for finger tracking and device augmentationShang Ma
We present Lift, a visible light-enabled finger
tracking and object localization technique that allows users to
perform freestyle multi-touch gestures on any object’s surface in
an everyday environment. By projecting encoded visible patterns
onto an object’s surface (e.g. paper, display, or table), and
localizing the user’s fingers with light sensors, Lift offers users a
richer interactive space than the device’s existing interfaces.
Additionally, everyday objects can be augmented by attaching
sensor units onto their surface to accept multi-touch gesture
input. We also present two applications as proof of concept.
Finally, results from our experiments indicate that Lift can
localize ten fingers simultaneously with an average accuracy of
1.7 millimeter and an average refresh rate of 84 Hz with 31
milliseconds delay on WiFi and 23 milliseconds delay on serial
communication, making gesture recognition on non-
instrumented objects possible.
2-Dimensional Wavelet pre-processing to extract IC-Pin information for disarr...IOSR Journals
Abstract: Due to higher processing power to cost ratio, it is now possible to replace the manual detection methods used in the IC (Integrated Circuit) industry by Image-processing based automated methods, to detect a broken pin of an IC connected on a PCB during manufacturing, which will make the process faster, easier and cheaper. In this paper an accurate and fast automatic detection method is used where the top view camera shots of PCBs are processed using advanced methods of 2-dimensional discrete wavelet pre-processing before applying edge-detection. Comparison with conventional edge detection methods such as Sobel, Prewitt and Canny edge detection without 2-D DWT is also performed. Keywords :2-dimensional wavelets, Edge detection, Machine vision, Image processing, Canny.
Developing a classification framework for landcover landuse change analysis i...Carolina
The document discusses developing a land cover/land use classification framework for Chile using remote sensing data. It describes using mathematical morphology techniques like morphological attribute profiles to integrate spatial context into spectral classifications, which significantly improved classification accuracy in three test subsets of a Landsat image - forests (61.3% to 80.8%), urban (75.5% to 92.2%) and agriculture (62.2% to 89.2%). However, the high-dimensional feature space requires specialized machine learning methods and high-performance computing.
Denoising and Edge Detection Using SobelmethodIJMER
The main aim of our study is to detect edges in the image without any noise , In many of the images edges carry important information of the image, this paper presents a method which consists of sobel operator and discrete wavelet de-noising to do edge detection on images which include white Gaussian noises. There were so many methods for the edge detection, sobel is the one of the method, by using this sobel operator or median filtering, salt and pepper noise cannot be removed properly, so firstly we use complex wavelet to remove noise and sobel operator is used to do edge detection on the image. Through the pictures obtained by the experiment, we can observe that compared to other methods, the method has more obvious effect on edge detection.
IRJET - Underwater Object Identification using Matlab and MachineIRJET Journal
This document discusses underwater object identification using MATLAB and machine learning. It begins with an abstract that outlines using image processing techniques like color correction and enhancement to improve underwater image quality and resolution for object detection. The methodology section then describes the process, which includes image acquisition, preprocessing like color conversion and noise removal, feature extraction to determine object type, and using a NodeMCU to send data to the cloud. It tests this approach by capturing images of fish underwater and classifying them by type. The results show enhanced, higher quality images compared to the originals. In conclusion, this method effectively removes color distortions and increases contrast to identify underwater objects using deep learning frameworks.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document describes an image denoising technique called the TWIST (Transform With Iterative Sampling and Thresholding) method. It begins with background on common types of image noise like Gaussian, salt-and-pepper, and quantization noise. It then discusses related work using eigendecomposition and the Nystrom extension for denoising. The proposed TWIST method uses the Nystrom extension to approximate the filter matrix with a low-rank matrix, allowing efficient processing of the entire image. It performs eigendecomposition on sample pixels to estimate eigenvalues and eigenvectors, then iterates this process with thresholding to denoise the image while preserving edges.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image fusion is the process of combining two or more images with specific objects with more precision. It is very common that when one object is focused remaining objects will be less highlighted. To get an image highlighted in all areas, a different means is necessary. This is done by the Image Fusion. In remote sensing, the increasing availability of Space borne images and synthetic aperture radar images gives a motivation to different kinds of image fusion algorithms. In the literature a number of time domain image fusion techniques are available. Few transform domain fusion techniques are proposed. In transform domain fusion techniques, the source images will be decomposed, then integrated into a single data and will be reconstructed back into time domain. In this paper, singular value decomposition as a tool to have transform domain data will be utilized for image fusion. In the literature, the quality assessment of fusion techniques is mainly by subjective tests. In this paper, objective quality assessment metrics are calculated for existing and proposed techniques. It has been found that the new image fusion technique outperformed the existing ones.
Speckle noise reduction from medical ultrasound images using wavelet threshIAEME Publication
This document proposes a method for reducing speckle noise from medical ultrasound images using wavelet thresholding and anisotropic diffusion. It first takes the logarithm of noisy images to convert speckle noise from multiplicative to additive. It then applies median filtering, followed by wavelet denoising using soft thresholding of detail coefficients. Finally, it uses SRAD filtering to enhance contrast while retaining edges. The proposed method is tested on three images and is found to improve SNR and PSNR compared to other filters based on statistical measurements.
Vicarious radiometric calibration refers to techniques used to calibrate remote sensing data without relying on onboard calibrators. Field spectroradiometers can be used to collect ground reflectance spectra and atmospheric parameters needed for vicarious calibration. Accurate vicarious calibration allows correction of instrument drift over time and comparison of datasets from different sensors, enabling monitoring of climate variables. Portable spectroradiometers like ASD's FieldSpec models are well-suited for rapid collection of calibration target and atmospheric data in the field.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Parameterized Image Filtering Using fuzzy LogicEditor IJCATR
The principal source of blur in digital images arise during image acquisition (digitization) or transmission. The
performance of imaging sensors is affected by a variety of factors, such as the environmental conditions during image acquisition.
Blurry images are the result of movement of the camera during shooting (not holding it still) or the camera not being capable of
choosing a fast enough shutter speed to freeze the action under the light conditions. For instance, in acquiring images with a camera,
light levels and sensor temperature are major factors affecting the amount of blur in the resulting image.
Blur was implemented by first creating a PSF filter in MatLab that would approximate linear motion blur. This PSF was then
convolved with the original image to produce the blurred image. Convolution is a mathematical process by which a signal, in this case
the image, is acted on by a system, the filter, in order to find the resulting signal. The amount of blur added to the original image
depended on two parameters of the PSF: length of blur (in pixels), and the angle of the blur. This thesis work is going to provide a
new, faster, and more efficient noise reduction method for images corrupted with motion blur. This new filter has two separated steps
or phases: the detection phase and the filtering phase. The detection phase uses fuzzy rules to determine whether a image is blurred or
not. When blurry image is detected, Then we use fuzzy filtering technique focuses only on the on the real blurred pixels.
4.[23 28]image denoising using digital image curveletAlexander Decker
This document summarizes research on using curvelet transforms for image denoising. It begins by discussing limitations of wavelet transforms for image processing, including lack of directionality and shift sensitivity. Curvelet transforms overcome these issues by providing high directional specificity and approximate shift invariance. The document proposes using digital implementations of curvelet, ridgelet, and contourlet transforms to denoise images corrupted by different types of noise. It describes the steps taken, which include applying the transforms after adding noise, then calculating peak signal-to-noise ratio and mean squared error to compare reconstruction quality. The transforms are found to provide better denoising performance than wavelet transforms as measured by these metrics.
This document provides an overview of digital image processing. It discusses what image processing entails, including enhancing images, extracting information, and pattern recognition. It also describes various image processing techniques such as radiometric and geometric correction, image enhancement, classification, and accuracy assessment. Radiometric correction aims to reduce noise from sources like the atmosphere, sensors, and terrain. Geometric correction geometrically registers images. Image enhancement improves interpretability. Classification categorizes pixels. The document outlines both supervised and unsupervised classification methods.
A Secure Color Image Steganography in Transform Domain ijcisjournal
Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
Homomorphic Filtering of Speckle Noise From Computerized Tomography (CT) Imag...CSCJournals
Adaptive filters are needed to accurately remove noise from noisy images when the variance of noise present varies. Linear filter such as Exponential filter becomes effective in removing speckle noise when homomorphic filtering technique is used. In this paper, an Adaptive Centre- Pixel-Weighed Exponential Filter for removing speckle noise from CT images was developed. The new filter is based on varying the centre-pixel of the filter kernel based on the estimated speckle noise variance present in a noisy CT image. Ten samples of 85x73 CT images corrupted by speckle noise level ranging from 10% to 30% were considered and the new technique gave a reasonably accurate speckle noise filtering performance with an average Peak Signal to Noise Ratio (PSNR) of 70.2839dB compared to 69.0658dB for Wiener filter and 64.3711dB for the Binomial filter. The simulation software used in the paper is Matrix Laboratory (Matlab).
Nonlinear Transformation Based Detection And Directional Mean Filter to Remo...IJMER
In this paper, a novel two stage algorithm for the removal of random valued impulse noise
from the images is presented. In the first stage the noise pixels are detected by using an exponential
nonlinear function. The transformation of the pixels increases the gap between noisy and noise free
candidates which leads to an efficient detection. In the second stage, the directional differences between
the pixels in the four main directions are calculated. The mean values of the pixels which lie in the
direction of minimum difference are calculated and the noisy pixel values are replaced with the mean
value of the pixels lying in the direction of minimum difference. Experimental results show that proposed
method is superior to the conventional methods in peak signal to noise ratio.
Survey Paper on Image Denoising Using Spatial Statistic son PixelIJERA Editor
This document summarizes research on image denoising using spatial statistics on pixel values. It begins with an abstract describing an approach that uses adaptive anisotropic weighted similarity functions between local neighborhoods derived from Mexican Hat wavelets to improve perceptual quality over existing methods. It then reviews literature on various denoising techniques including non-local means, non-uniform triangular partitioning, undecimated wavelet transforms, anisotropic diffusion, and support vector regression. Key types of image noise like Gaussian, salt and pepper, Poisson, and speckle noise are described. Limitations of blurring and noise in digital images are discussed. In conclusion, the document provides an overview of image denoising research using spatial and transform domain techniques.
2_2011 IGARSS SMAP Applications Program Presentation.pdfgrssieee
The document discusses the SMAP mission and its applications program. The SMAP mission will provide global soil moisture and freeze/thaw maps to further understanding of water, energy and carbon cycles. The applications program aims to increase interaction between scientists, users and applications involved in the mission. It identifies current and potential user communities and funds projects using NASA Earth science data for decision making. The SMAP Applications Working Group works with these communities and the SMAP science team to optimize use of SMAP data products.
A SELF-ADJUSTIVE GEOMETRIC CORRECTION METHOD FOR SERIOUSLY OBLIQUE AERO IMAGE...grssieee
This document presents a self-adjustive geometric correction method for seriously oblique aerial images. It analyzes projection errors caused by the curvature of the Earth and terrain relief. A ternary quadratic polynomial model is used, with adjustments to better correct for relief-induced projection errors. Experiments on images taken at large viewing angles demonstrate the new model outperforms conventional models in correction accuracy and recovering locations of high objects.
1) The document presents a method for detecting building damage from very high resolution satellite images using one-class SVM and shadow information.
2) Initial building damage is detected using one-class SVM classification on multitemporal images. Shadow detection and change identification is also performed.
3) The initial damage detection results are refined by considering areas of shadow change, improving overall accuracy compared to using spectral data alone.
This document describes a methodology for forest monitoring using hyperspectral images with sparse regularization. Field surveys were conducted to collect ground truth data on forest stands. Hyperspectral and SAR remote sensing data were then used as inputs for classification and regression models with sparse regularization techniques like sparse discriminant analysis and LASSO. The models accurately predicted tree species composition, canopy cover, timber volume, and tree height at the subcompartment level. Validation showed the predictions were consistent with actual forest conditions observed on the ground. Hyperspectral data provided better results than simulated multispectral data. This sparse regularization approach can provide effective forest monitoring from remote sensing with less data collection needed in the field.
This document summarizes an analysis of interference to spaceborne microwave radiometers operating in the 6-7 GHz band from downlinks of mobile satellite systems. It finds that interference exceeds acceptable levels in some cases, particularly when there is specular reflection from the ocean surface. The analysis is the first to quantify interference levels in ocean areas. Protection of the 6-7 GHz band for passive sensing is important but current radio regulations do not provide adequate protection.
The document summarizes lessons learned from joint field campaigns comparing techniques for vicarious calibration of optical sensors. Thirteen organizations from ten countries took part. Key findings include the need for standardized measurement formats and protocols to improve comparisons, the importance of bi-directional reflectance measurements, and developing rigorous error budgets and traceability to reference standards to achieve uncertainties below 2% and ensure climate-quality data. Future work should involve more instrumentation diversity and understanding sources of bias and uncertainty.
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
This document summarizes research on improving ambiguity resolution in GPS positioning using an ionospheric differential correction model. Data was collected from two stations in Malaysia's equatorial region over a short baseline of 33 km. Applying corrections from an ionospheric model led to ambiguities being resolved faster, in under an hour, compared to uncorrected data which took over 2.5 hours. The model also produced smaller standard errors in baseline positioning and increased the variance ratio and decreased reference variance indicators of successful ambiguity resolution. The findings show that an ionospheric differential correction model can improve ambiguity resolution for single frequency GPS over short baselines.
4.[23 28]image denoising using digital image curveletAlexander Decker
This document summarizes research on using curvelet transforms for image denoising. It begins with an introduction to wavelet denoising and its limitations in capturing edges. Curvelet transforms are proposed to overcome these limitations by providing directional selectivity and anisotropic elements that better represent curved edges. The document then describes steps to denoise an image using curvelet transforms, including adding noise, applying the curvelet transform, and calculating performance metrics like PSNR and MSE. It provides details on the curvelet transform and compares it to ridgelet transforms. The research aims to exhibit higher PSNR than wavelet methods across different noise levels on standard test images like Lenna.
This document describes a framework for unsupervised classification of SAR (synthetic aperture radar) images that can adapt to different numbers of classes. The framework involves extracting features from image patches, representing the image as a dissimilarity matrix, reordering the matrix to estimate the number of classes, obtaining initial class labels, performing a final SVM classification while incorporating spatial relationships between patches, and evaluating the method on test data. The method is shown to accurately estimate the number of classes and achieve high classification accuracy on SAR images containing various land cover types.
This document characterizes the neutron field in the instrument calibration facility rig room at ANSTO in Australia. Four standard methods were used to determine the fractional room return scatter and ambient dose equivalent response of the reference neutron monitor. The shadow shield method from ISO 10647 was adopted, using a truncated conical shield. It found a monitor reading of 200.40 uSv/h at 1m and fractional room return scatter of 1.210E-04. The objectives were to characterize the room's neutron scattering properties and calibrate the facility's neutron monitor standards.
Experimental analysis of non-Gaussian noise resistance on global method optic...journalBEEI
This paper presents the analytical of non-Gaussian noise resistance with the aid of the use of bilateral in reverse confidential with the optical flow. In particular, optical flow is the sample of the image’s motion from the consecutive images caused by the object’s movement. It is a 2-D vector where every vector is a displacement vector displaying the motion from the first image to the second. When the noise interferes with the image flow, the approximated performance on the vector in optical flow is poor. We ensure greater appropriate noise resistance by applying bilateral in reverse confidential in optical flow in the experiment by concerning the error vector magnitude (EVM). Many noise resistance models of the global method optical flow are using for comparison in our experiment. And many sequenced image data sets where they are interfered with by several types of non-Gaussian noise are used for experimental analysis.
Boosting CED Using Robust Orientation Estimationijma
n this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
International Journal of Computational Engineering Research(IJCER)ijceronline
This document summarizes and compares four point-based feature extraction methods for palmprint authentication: Forstner operator, SUSAN operator, wavelet-based salient point detection, and Trajkovic and Hedley corner detector. It provides details on how each method works, including equations to calculate features. The Forstner operator identifies points, edges and regions using autocorrelation and error ellipses. The SUSAN operator finds areas of similar brightness within a circular window. Wavelet-based detection extracts salient points from variations across image resolutions. Trajkovic and Hedley corner detection has improved localization compared to other operators like Plessey.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
The document summarizes the Level 1 processing pipeline (L1PP) used to process data from the Soil Moisture and Ocean Salinity (SMOS) mission. The L1PP takes raw instrument data and produces calibrated brightness temperature images at three levels - L1a, L1b, and L1c. Key algorithms described include calibration steps, removal of interference sources like sun and moon, image reconstruction, and geolocation. Validation results showed the L1PP algorithms could produce images with well-defined coastlines and improved radio frequency interference mitigation.
Noise Removal in SAR Images using Orthonormal Ridgelet TransformIJERA Editor
Development in the field of image processing for reducing speckle noise from digital images/satellite images is a challenging task for image processing applications. Previously many algorithms were proposed to de-speckle the noise in digital images. Here in this article we are presenting experimental results on de-speckling of Synthetic Aperture RADAR (SAR) images. SAR images have wide applications in remote sensing and mapping the surfaces of all planets. SAR can also be implemented as "inverse SAR" by observing a moving target over a substantial time with a stationary antenna. Hence denoising of SAR images is an essential task for viewing the information. Here we introduce a transformation technique called ―Ridgelet‖, which is an extension level of wavelet. Ridgelet analysis can be done in the similar way how wavelet analysis was done in the Radon domain as it translates singularities along lines into point singularities under different frequencies. Simulation results were show cased for proving that proposed work is more reliable than compared to other de-speckling processes, and the quality of de-speckled image is measured in terms of Peak Signal to Noise Ratio and Mean Square Error.
Boosting ced using robust orientation estimationijma
In this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
The document describes a novel algorithm for despeckling synthetic aperture radar (SAR) images using particle swarm optimization (PSO) in the curvelet domain. The algorithm first identifies homogeneous regions in the speckled image using variance calculations. It then uses PSO to optimize the thresholding of curvelet coefficients, with the objective of minimizing the average power spectral value. This provides an optimized threshold to apply curvelet-based despeckling. The proposed method is tested on standard images and shown to outperform conventional filters like median and Lee filters in reducing speckle noise.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
This document summarizes an article from the International Journal of Electronics and Communication Engineering & Technology. The article proposes techniques for image steganography based on chaos theory and the contourlet transform. It describes using a modified Arnold cat map to scramble secret data for increased security before embedding it in the contourlet domain of an image. Experimental results showed the proposed method provides high embedding capacity while maintaining good stego image quality compared to wavelet-based approaches. It embeds data in subbands with lower energy to minimize distortion, and evaluates quality using PSNR, SNR, and correlation metrics.
This document summarizes an article from the International Journal of Electronics and Communication Engineering & Technology. The article proposes techniques for image steganography based on chaos theory and the contourlet transform. It describes using a modified Arnold cat map to scramble secret data for increased security before embedding it in the contourlet domain of an image. Experimental results showed this approach provides high embedding capacity while maintaining good stego image quality as measured by PSNR, SNR, and correlation metrics. It was found to perform better than similar wavelet-based steganography methods.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This document discusses and compares different thresholding techniques for image denoising using wavelet transforms. It introduces the concept of image denoising using wavelet transforms, which involves applying a forward wavelet transform, estimating clean coefficients using thresholding, and applying the inverse transform. It then describes several common thresholding methods - hard, soft, universal, improved, Bayes shrink, and neigh shrink. Simulation results on test images corrupted with additive white Gaussian noise show that the proposed improved thresholding technique achieves lower MSE and higher PSNR than the universal hard thresholding method, demonstrating better noise removal performance while preserving image details.
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODELgrssieee
1) The document describes a segmentation algorithm for polarimetric SAR (PolSAR) data that can model both scalar-texture and multi-texture scattering.
2) The algorithm uses log-cumulants and hypothesis testing to determine whether a scalar-texture or dual-texture model best fits the data within each segment.
3) The algorithm is tested on simulated multi-texture PolSAR data and is shown to accurately segment the classes and estimate their texture parameters. However, when applied to real data sets, the algorithm only finds the simpler scalar-texture case.
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...grssieee
This document discusses using wavelet transforms to analyze two-point statistics of polarimetric synthetic aperture radar (PolSAR) data. It introduces wavelet variance and kurtosis as metrics that can be applied to PolSAR data transformed using a wavelet frame. It then provides an example of applying this analysis to ALOS PALSAR data over Hawaii's Papau Seamount to characterize sea surface features.
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIESgrssieee
The Sentinel-1 mission is part of the GMES program and consists of two satellites to provide C-band SAR data for emergency response, marine and land monitoring, and other applications. The satellites operate in a near-polar orbit with a 12 day repeat cycle. The main acquisition mode is an interferometric wide swath mode with 5m range and 20m azimuth resolution over a 250km swath. Sentinel-1 will support operational services and create a long-term SAR data archive.
The document summarizes the status of the GMES Space Component program. It describes the Sentinel satellite missions for monitoring land, ocean, atmosphere and emergency situations. The Sentinels will provide long-term data continuity as well as improved coverage compared to existing missions. Sentinel data will be freely and openly available to both operational users and the science community. The program is on track, with the first Sentinel launches beginning in 2013.
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETERgrssieee
The document describes the progress of the development of CFOSAT SCAT, a Ku-band scatterometer onboard the Chinese-French Oceanography Satellite (CFOSAT). CFOSAT will measure global ocean surface winds and waves to improve weather forecasting, ocean dynamics modeling, climate research, and understanding of surface processes. The SCAT instrument is a rotating fan-beam radar scatterometer that will retrieve wind vectors using measurements of backscatter at incidence angles from 26 to 46 degrees. It has a wide swath of over 1000km and specifications are designed to achieve high-precision wind measurements globally. System details including parameters and the operation mode are provided.
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...grssieee
The document describes the SAP4PRISMA project which aims to develop algorithms and products to support the Italian hyperspectral PRISMA Earth observation mission. The project will focus on data processing, quality assessment, classification methods, and generating level 3 and 4 products for applications like land monitoring, agriculture, and hazard monitoring. It will include the generation of "PRISMA-like" synthetic test data to support algorithm development and validation. The research will be carried out across multiple work packages focusing on topics like data quality, classification methods, calibration/validation, and developing applicative products.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
1) The EO-1 Hyperion instrument has collected over 65,000 scenes over its 12-year mission to study land and coastal ecosystems using imaging spectroscopy.
2) Studies using Hyperion data have identified spectral indices related to chlorophyll that correlate with carbon flux measurements at different sites, including a Zambian woodland and North Carolina forest sites.
3) Time series of Hyperion data at flux tower sites show seasonal changes in these spectral indices that match patterns in ecosystem carbon uptake and release.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
1) The EO-1 Hyperion instrument has collected over 65,000 scenes over its 12-year mission to study land and coastal ecosystems using imaging spectroscopy.
2) Studies using Hyperion data have identified spectral indices related to chlorophyll that correlate with carbon flux measurements at different forest, grassland, and woodland sites globally.
3) Time series of Hyperion data at sites in Zambia, North Carolina, and Kansas show seasonal changes in these spectral indices that match patterns in ecosystem carbon uptake and release measured by flux towers.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
EO-1/Hyperion has been collecting hyperspectral imagery for over 12 years, acquiring over 65,000 scenes. Researchers have been using these data to develop and validate algorithms for estimating vegetation properties like fraction of absorbed photosynthetically active radiation (fAPAR) and photochemical reflectance index (PRI). Comparisons of Hyperion data to field measurements at flux tower sites show these algorithms can accurately track vegetation changes over time and relate spectral properties to productivity metrics like light use efficiency and gross ecosystem productivity. This work is helping prototype data products for the upcoming HyspIRI mission.
This document is a return and exchange form for a wetsuit company. It provides instructions for customers to fill out when returning an undamaged item for a refund, exchange, or size change. The form requests information like the customer's order details, contact information, the suit being returned and its size, the reason for return, and if applicable, the new desired size. It also provides the return shipping address and notifies customers that the company is not responsible for lost or damaged return packages.
This document provides instructions for clients of Fox Tax Planning and Preparation for preparing to have their taxes filed. It lists important income and deduction documentation to bring to an appointment, such as W-2s, 1099s, receipts for donations. It also includes an engagement letter detailing the services to be provided, responsibilities of both parties, fees, and electronic filing and signature procedures. Clients are asked to sign the letter agreeing to the terms and return it along with their tax information.
The document discusses mapping wetlands in North America using MODIS 500m imagery. It describes wetlands and existing global wetland databases. The methodology uses MODIS data from 2008, digital elevation models, and reference data to classify wetlands into three types - forest/shrub dominant wetlands, herbaceous dominant wetlands, and sea grass dominant wetlands. Training data is collected from existing land cover maps and Landsat imagery. A decision tree model and maximum likelihood classification are applied to extract wetlands from other land covers.
The document summarizes research using SBAS-DInSAR (Small BAseline Subset differential interferometric synthetic aperture radar) techniques to analyze ground deformation at Mt. Etna volcano in Italy over the last 18 years using ERS and ENVISAT satellite data. The analysis revealed three main deformation processes: inflation of the volcanic edifice, subsidence of sectors on the eastern flank due to gravitational spreading, and deflation-inflation cycles associated with eruptive and post-eruptive activity. More recent analysis using higher resolution COSMO-SkyMed data from 2009-2010 detected deformation related to faults and a 2010 earthquake more precisely than lower resolution ENVISAT data.
1. 2011 IEEE International Geoscience and Remote Sensing Symposium
A NEW ALGORITHM FOR NOISE REDUCTION AND
QUALITY IMPROVEMENT IN SAR INTERFEROGRAMS
USING INPAINTING AND DIFFUSION
Silvia Liberata Ullo, Maurizio di
Bisceglie, Carmela Galdi1
Universit` degli Studi del Sannio
a
Benevento, ITALY
July 28, 2011
1 This work is supported by Centro Euro-Mediterraneo per i Cambiamenti Climatici
within a framework project by Italian Ministry of Environment.
2. Introduction
Main objective of SAR interferometry is the generation of high–quality and
high–resolution digital elevation maps (DEM’s).
Accuracy of DEM’s is strongly related to the quality of the generated
interferograms.
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3. In this context
• We first present an inpainting–diffusion algorithm recently introduced 2
to improve interferograms quality and therefore final quality of DEM’s
• Secondly we go through the Complex Ginzburg–Landau (CGL) equation
successfully applied to image restoration 3 , and in our work applied to the
inpainting scheme for restoration of SAR interferograms
• At the end we propose a modified version of starting algorithm and
evaluate the efficiency of two algorithms also in the presence of noise
2 A. Borz` M. di Bisceglie, C. Galdi, L. Pallotta, and S. L. Ullo, Phase retrieval in
ı,
SAR interferograms using diffusion and inpainting, proceedings of IEEE Transactions
on Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, July 2010
3 A. Borz` H. Grossauer, and O. Scherzer, Analysis of iterative methods for solving
ı,
a Ginzburg–Landau equation, International Journal of Computer Vision, vol.64,
pp.203–219, September 2005
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4. Preliminary Considerations
• We start from an interferogram, unwrapped via the Minimum Cost Flow
(MCF) approach, centered on Ariano Irpino (AV), Italy, produced
through a couple of SAR images acquired with ERS–1 and ERS–2 on the
13th and the 14th of July 1995.
• A selected area of the interferogram and its corresponding coherence map
are shown as follows
4 of 23 2011 IEEE International Geoscience and Remote Sensing Symposium
5. • Based on the coherence map and fixing an appropriate threshold, a new
interferogram is produced by discarding phase values whose correlation
coefficients are lower than the threshold.
• Dark pixels in the figure represent the discarded phase values.
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6. Threshold Setting
The choice for the threshold is subject to some considerations:
• a correlation coefficient of 0.25 is sufficient to guarantee height errors of
7.5 meters or less (thus approaching a suitable error for topographic
mapping) 4
• for coherence values under 0.05 the corresponding unwrapped phases are
very noisy
• coherence values are regarded as low if they vary between 0.05 and 0.20
Therefore we make the threshold vary from 0.05 to 0.25 in our experiments.
4 H. A. Zebker, C. L. Werner, P.A. Rosen, and S. Hensley, Accuracy of topographic
maps derived from ERS–1 interferomtric radar, IEEE Transactions on Geoscience and
Remote Sensing, vol.32, no. 4, pp.823–836, July 1994
6 of 23 2011 IEEE International Geoscience and Remote Sensing Symposium
7. Inpainting is a well known method for restoration of missing or damaged
portions of images or paintings.
Complex Ginzburg–Landau inpainting is a technique that can be
conveniently considered to fill fragmentary areas.
7 of 23 2011 IEEE International Geoscience and Remote Sensing Symposium
8. The Complex Ginzburg–Landau inpainting scheme
The basic element of the algorithm is the CGL differential diffusion equation
stated in the space–time coordinates in the form
∂u 1
= ∆u + 2 1 − |u|2 u = 0 (1)
∂t ε
where
• an inpainting domain Ω is defined where there are the phase values to be
restored
• the function u : D → C is the complex valued solution of the equation
such that Ω ⊂ D
• ε is a suitably chosen parameter
8 of 23 2011 IEEE International Geoscience and Remote Sensing Symposium
9. • It has been proved [3] that the solution u of the equation lies on the unit
radius sphere
• The real part of u, u, includes the interferogram values scaled and
normalized, such that u may assume any value in the interval [−1, +1]
• The imaginary part (u) is computed as
2
(u) = 1 − (u) (2)
such that |u| = 1
• a sequence of images are generated where the information along the
borders is smoothly propagated inside the region of missing data
• this is achieved by moving mostly along the level line directions to
preserve edges
• the steady state is achieved when the smoothness of the image is nearly
constant along the level lines ( ∂u = 0)
∂t
9 of 23 2011 IEEE International Geoscience and Remote Sensing Symposium
10. Application of the algorithm
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11. Application of the algorithm
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12. Application of the algorithm
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13. Application of the algorithm
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14. Application of the algorithm
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15. Application of the algorithm
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16. Application of the algorithm
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17. Application of the algorithm
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18. Application of the algorithm
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19. Application of the algorithm
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20. Application of the algorithm
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21. Application of the algorithm
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22. Application of the algorithm
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23. Application of the algorithm
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24. Application of the algorithm
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25. Application of the algorithm
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26. Application of the algorithm
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27. Application of the algorithm
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28. Application of the algorithm
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29. Application of the algorithm
10 of 23 2011 IEEE International Geoscience and Remote Sensing Symposium
30. Evaluation of the algorithm
To get a measure of performance for the proposed algorithm we used the
Signal–to–Noise Ratio (SNR) as expressed by the equation:
Nt
SN R = 20 log (3)
Nr
where Nt is the number of total pixels in the interferogram and Nr is the
number of residuals, where the residuals are those points around which a
close integral of the phase differences gives a non–zero result 5 .
5 U. Wegmuller, C. Werner, T. Strozzi, and A. Wiesmann, Phase unwrapping with
GAMMA ISP, Technical Report, Gamma Report Sensing, May 2002
11 of 23 2011 IEEE International Geoscience and Remote Sensing Symposium
31. • SNR=27.3483 dB with the MCF algorithm
Table: SNR for the CGL inpainting algorithm.
Threshold SNR Number of Number of
[dB] iterations residuals [Nr ]
0.05 27.3549 21 10636
0.10 27.3860 47 10598
0.15 27.4287 185 10546
0.20 27.6009 639 10339
0.25 27.9436 3509 9939
Results show that the proposed algorithm works better than the MCF based
algorithm.
12 of 23 2011 IEEE International Geoscience and Remote Sensing Symposium
32. Modified CGL based algorithm
• The CGL inpainting equation works by filling the regions where there are
missing values with zeros at the first iteration and later on through the
reaction diffusion and inpainting procedure explained before.
• In a new version of the algorithm we modify the inpainting equation (1)
and the low–coherence phase values are not discarded but used as initial
conditions. The CGL equation is forced to use these values at the first
iteration to drive the inpainting scheme.
The idea is that even if these phase values have low–coherence they may
contain a part of useful information in any case.
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33. Results for the modified and the previous algorithm are shown in the table
Table: SNR for the new and the old CGL inpainting algorithm.
Threshold SN R1 SN R0 [Nr1 ] [Nr0 ] ∆ [Nr ]
[dB] [dB]
0.05 27.3565 27.3549 10634 10636 -2
0.10 27.3863 27.3860 10596 10598 -2
0.15 27.4584 27.4287 10510 10546 -36
0.20 27.6480 27.6009 10283 10339 -56
0.25 28.0199 27.9436 9852 9939 -13
0.30 28.6393 28.6336 9174 9180 -6
Modified algorithm performs even better and reaches its best performance
for a threshold of 0.20
This appears to be reasonable. With the modified version of the algorithm
we re–used as initial conditions the discarded pixels.
Obviously as the threshold increases besides a certain value all this work
sounds to be worthless.
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34. Inpainting in the presence of noise
In another set of experiments the algorithm is tested in the presence of noise.
The idea is to introduce noise only in some parts of the interferogram, in a
controlled manner.
We could add noise to the interferogram in a random way, but we have
preferred to use the following method.
• first a region of the interferogram, with high–coherence phase values
only, is selected
• then a mask is created from a different part of the interferogram, where
also low–coherence phase values are present: the mask is composed by all
these low-coherence pixels and keeps their shape
• the high–coherence region is marked using this mask to make a footprint
• at the end noise is added only over the footprint: practically through the
mask, some high–coherence phase values are identified first and
corrupted with noise later on.
Since we know exactly the true values before adding the noise, after
implementing our algorithm, we can use them for comparison.
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35. In figure the red rectangle includes a part of interferogram where
high–coherence phase values only are present; the black circles on the right
and on the left of red rectangle are regions where also low–coherence phase
values are present. These last two regions will be used as masks to establish
where, in the good interferogram, noise will be added.
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36. The phase noise distribution
For the generation of the random additive term necessary to degrade the
interferometric phase, an interesting theoretical model has been used 6-7 .
√
The phase noise ∆φ is the differential phase of the product S(g1 , g2 ),
often referred in the literature as the compound–Gaussian model, where g1
and g2 are complex Gaussian variables, independent of S, with zero mean
and assigned covariance matrix K.
Interestingly, since S is a real–values random variable, the pdf of ∆φ
depends on the covariance matrix of the Gaussian components only.
6 M. di Bisceglie, C. Galdi, and R. Lanari, Statistical characterization of the phase
process in interferometric SAR images, proceedings of IEEE Transactions on
Geoscience and Remote Sensing Symposium, Lincoln, NE, USA, May 1996
7 D. Just, R. Bambler, Phase Statistics of Interferograms with Applications to
Synthetic Aperture Radar, Applied Optics, Vol.33, No.20; July 1994, pp.4361-4368
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37. Generation of the additive phase noise
Two complex vectors c1 = (X1 + jY1 ) and c2 =(X2 + jY2 ) of independent
Gaussian variables with zero mean and unitary variance are generated whose
length must cover the total number of pixels we want to corrupt with noise.
Since each scatterer is observed at two slightly different viewpoints, some
degree of correlation is expected between the two vectors. To take into
account such a correlation, the vectors c1 and c2 ,are returned into vectors g1
and g2 through the covariance matrix K given by
1 0 ρc ρs
K = 0 1 −ρs ρc
ρc −ρs
(4)
1 0
ρs ρc 0 1
where ρc is the correlation coefficient of the components (X1n , X2n ) and
(Y1n , Y2n ); ρs is the cross–correlation coefficient of Xin , Yjn , for i, j = 1, 2
∗
and i = j. We get the noise to be added as the phase of the product g1 g2 .
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38. Validation in the presence of noise
In sequence (left-right, top-bottom) : 1) high–coherence interferogram
represented before in the red rectangle; 2) the same region marked with the
mask in the circle on the right; 3) the same region after noise is added; 4)
the interferogram restored with the CGL algorithm.
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39. Results and some considerations
In this case with a threshold of 0.20 the SNR is equal to 59.92 dB, very
high, both in the case of MCF and in the case of CGL algorithm.
This can be justified because first we start from a high–coherence part of
the interferogram, and moreover the mask used to mark the ” good ” region
is small. Consequently the quantity of phase values corrupted with noise has
been little.
The final amount of residuals is actually only 13.
If we use also the Mean Square Error (MSE) to appreciate the difference
between the two interferograms we get a MSE = 5 × 10−7 if the CGL
algorithm is applied without noise and a MSE = 1.8 × 10−6 if noise is added.
Results appear to be pretty good and show the algorithm works well in
reconstructing the missing values even in the presence of noise.
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40. If instead we use as mask the one on the left of the red rectangle
SNR = 27.09 dB when noise is added
This can be explained because if the noise is added over several parts of the
interferogram, we should consider a covariance matrix K not equal for the
whole region, but varying.
Moreover, the more pixels are marked to add the noise, the more corrupted
regions are considered and the greater the number of these regions that take
a part into the algorithm, resulting in a lower performance.
We remark also that with respect to the modified algorithm that took in
consideration the true values, even if with low–coherence coefficients, in this
case the algorithm starts from noisy values that are generated in accordance
with a theoretical model but are not real values.
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41. Conclusions and future work
• An inpainting scheme based on Complex Ginzburg–Landau equation has
been successfully applied to image restoration of SAR interferograms
• A new algorithm is used to restore interferograms by using the low
coherence–phase values as initial conditions
• Results appear to be very good especially in the medium–coherence area
• The algorithm shows to work well in reconstructing the interferogram
even in presence of noise if the region is restrained
• It is under analysis the possibility to adapt the algorithm even when the
noise is added over a region spatially distributed
• Future verifications can be done also through comparison of final DEM’s
to test algorithm efficiency
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42. THANK YOU FOR YOUR ATTENTION!
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