This document provides information on several remote sensing projects from IEEE 2015. It lists the titles, languages, and abstracts for 8 projects related to classification and analysis of hyperspectral and multispectral images. The projects focus on techniques such as sparse representation in tangent space, Gabor feature-based collaborative representation, level set evolutions for object extraction, and dimension reduction using spatial and spectral regularization.
This document lists several image processing projects implemented using MATLAB. The first project uses a supervised classification model combining spectral data and a spatially adaptive Markov random field prior to classify hyperspectral images. The second project enhances image contrast through reversible data hiding by embedding messages in the highest histogram bins. The third project segments noisy images using an efficient level set method that embeds a Markov random field energy function for robustness. The final project evaluates heart rate from face video without contact by decomposing reflectance signals using the Hilbert-Huang transform.
This document proposes a novel long-term visual tracking algorithm called FAST that can be used for target following on UAVs. FAST transforms the correlation filter tracker from the frequency domain to the spatial domain to serve as a detector for redetection. It also introduces a coarse-to-fine redetection scheme using generic object proposals for coarse selection followed by a discriminative detector for fine selection, avoiding exhaustive search. Experiments show FAST achieves real-time, automatic, smooth long-term target following on UAVs for both indoor and outdoor scenarios.
"Efficient time-domain back-projection focusing core for the image formation of very high resolution and highly squinted SAR spotlight data on scenes with strong topography variation" - Author(s): Francesco Tataranni, Giuseppe Disimino, Antonella Gallipoli, INNOVA Consorzio per l’Informatica e la Telematica (Italy); Paolo Inversi, Telespazio S.p.A. (Italy)
This document provides information on several remote sensing projects from IEEE 2015. It lists the titles, languages, and abstracts for 8 projects related to classification and analysis of hyperspectral and multispectral images. The projects focus on techniques such as sparse representation in tangent space, Gabor feature-based collaborative representation, level set evolutions for object extraction, and dimension reduction using spatial and spectral regularization.
This document lists several image processing projects implemented using MATLAB. The first project uses a supervised classification model combining spectral data and a spatially adaptive Markov random field prior to classify hyperspectral images. The second project enhances image contrast through reversible data hiding by embedding messages in the highest histogram bins. The third project segments noisy images using an efficient level set method that embeds a Markov random field energy function for robustness. The final project evaluates heart rate from face video without contact by decomposing reflectance signals using the Hilbert-Huang transform.
This document proposes a novel long-term visual tracking algorithm called FAST that can be used for target following on UAVs. FAST transforms the correlation filter tracker from the frequency domain to the spatial domain to serve as a detector for redetection. It also introduces a coarse-to-fine redetection scheme using generic object proposals for coarse selection followed by a discriminative detector for fine selection, avoiding exhaustive search. Experiments show FAST achieves real-time, automatic, smooth long-term target following on UAVs for both indoor and outdoor scenarios.
"Efficient time-domain back-projection focusing core for the image formation of very high resolution and highly squinted SAR spotlight data on scenes with strong topography variation" - Author(s): Francesco Tataranni, Giuseppe Disimino, Antonella Gallipoli, INNOVA Consorzio per l’Informatica e la Telematica (Italy); Paolo Inversi, Telespazio S.p.A. (Italy)
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.
DUAL POLYNOMIAL THRESHOLDING FOR TRANSFORM DENOISING IN APPLICATION TO LOCAL ...ijma
Thresholding operators have been used successfully for denoising signals, mostly in the wavelet domain.
These operators transform a noisy coefficient into a denoised coefficient with a mapping that depends on
signal statistics and the value of the noisy coefficient itself. This paper demonstrates that a polynomial
threshold mapping can be used for enhanced denoising of Principal Component Analysis (PCA) transform
coefficients. In particular, two polynomial threshold operators are used here to map the coefficients
obtained with the popular local pixel grouping method (LPG-PCA), which eventually improves the
denoising power of LPG-PCA. The method reduces the computational burden of LPG-PCA, by eliminating
the need for a second iteration in most cases. Quality metrics and visual assessment show the improvement.
The document compares three image fusion techniques: wavelet transform, IHS (Intensity-Hue-Saturation), and PCA (Principal Component Analysis). For each technique, it describes the methodology, syntax used, and features. It then applies each technique to sample images to produce fused images. The RGB values of the fused images are recorded and compared in a table. The wavelet technique uses max area selection and consistency verification for feature selection. IHS transforms RGB to IHS values and replaces intensity with a panchromatic image. PCA replaces the first principal component with a high-resolution panchromatic image. The document concludes no single technique is best and the quality depends on the application.
tScene classification using pyramid histogram ofijcsa
This document proposes a new method called Pyramid Histogram of Multi-scale Block Local Binary Pattern (PH-MBLBP) for scene classification. PH-MBLBP encodes both micro- and macro-structures of image patterns to provide a more complete representation than basic LBP. It divides images into spatial regions at multiple resolutions to capture geometric information. Experiments on 15 scene categories show PH-MBLBP outperforms SIFT and provides a powerful yet fast texture descriptor for scene recognition.
A Hybrid Chebyshev-ICA Image Fusion Method Based on Regional SaliencyTELKOMNIKA JOURNAL
An image fusion method that performs robustly for image sets heavily corrupted by noise is
presented in this paper. The approach combines the advantages of two state-of-the-art fusion techniques,
namely Independent Component Analysis (ICA) and Chebyshev Poly-nomial Analysis (CPA) fusion.
Fusion using ICA performs well in transferring the salient features of the input images into the composite
output, but its performance deteriorates severely under mild to moderate noise conditions. CPA fusion is
robust under severe noise conditions, but eliminates the high frequency information of the images
involved. We pro-pose to use ICA fusion within high activity image areas, identified by edges and strong
textured surfaces and CPA fusion in low activity areas identified by uniform background regions and weak
texture. A binary image map is used for selecting the appropriate method, which is constructed by a
standard edge detector followed by morphological operators. The results of the proposed approach are
very encouraging as far as joint fusion and denoising is concerned. The works presented may prove
beneficial for future image fusion tasks in real world applications such as surveillance, where noise is
heavily present.
This document proposes a remote sensing image fusion approach that combines the Brovey transform and wavelet transforms. The Brovey transform is used first to reduce spectral distortion, followed by a wavelet transform to reduce spatial distortion. The approach was tested on MODIS and SPOT data as well as ETM+ and SPOT data. Statistical analysis showed the proposed technique performed better than traditional fusion techniques like IHS, PCA, and the Brovey transform alone in terms of metrics like correlation coefficient, entropy, and structural similarity. Future work will focus on improving the technique and applying fused images to classification tasks.
This paper proposes an improved Semi-Global Matching (SGM) algorithm for stereo vision that introduces a "branch cost propagation" concept. This allows each path to actively search for and collect feature information, boosting meaningful signal energy and helping overcome noise. The authors implemented this "branch SGM" on an FPGA, finding it used 10% more resources but reduced error rates by 10-30% compared to standard SGM. Standard SGM is a widely used real-time stereo matching method that aggregates costs along multiple scanline paths, but can be noisy. The proposed method aims to enhance SGM's noise resistance for applications like autonomous vehicles.
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Image hashing is an efficient way to handle digital data authentication problem. Image hashing represents quality summarization of image features in compact manner. In this paper, the modified center symmetric local binary pattern (CSLBP) image hashing algorithm is proposed. Unlike CSLBP 16 bin histogram, Modified CSLBP generates 8 bin histogram without compromise on quality to generate compact hash. It has been found that, uniform quantization on a histogram with more bin results in more precision loss. To overcome quantization loss, modified CSLBP generates the two histogram of a four bin. Uniform quantization on a 4 bin histogram results in less precision loss than a 16 bin histogram. The first generated histogram represents the nearest neighbours and second one is for the diagonal neighbours. To enhance quality in terms of discrimination power, different weight factor are used during histogram generation. For the nearest and the diagonal neighbours, two local weight factors are used. One is the Standard Deviation (SD) and other is the Laplacian of Gaussian (LoG). Standard deviation represents a spread of data which captures local variation from mean. LoG is a second order derivative edge detection operator which detects edges well in presence of noise. The proposed algorithm is resilient to the various kinds of attacks. The proposed method is tested on database having malicious and non-malicious images using benchmark like NHD and ROC which confirms theoretical analysis. The experimental results shows good performance of the proposed method for various attacks despite the short hash length.
Laser Beam Targeting System is a proven stretegy of using Facebook ads to build up your business.
Facebook Ads is a essential tool and skill when you come to target right audicence for your business, products and service. It will save a lot of money and time when you know how to take advantage of Facebook.
A Step-By-Step guide will be shown and lead you from scratch to advanced. We wish you could success and using the skill to develop your business.
The document discusses an approach to upscaling video using a back iteration algorithm. It begins with an abstract describing how the back iteration algorithm is similar to back-projection algorithms used in tomography. It then discusses how the back iteration algorithm is implemented iteratively on individual video frames to upscale the video. Key aspects of the algorithm include motion estimation, intensity calculation, and registering frames at sub-pixel accuracy. The document provides details on the mathematical model and implementation of the back iteration algorithm for video upscaling. It presents results of applying the algorithm and concludes with discussing opportunities for future improvements.
Reversible watermarking based on invariant image classification and dynamic h...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
The implementation of the improved omp for aic reconstruction based on parall...Nxfee Innovation
This document presents a hardware implementation of an improved orthogonal matching pursuit (OMP) algorithm for signal reconstruction in analog-to-information converters based on compressive sensing. The proposed architecture reduces computational complexity and the number of iterations compared to the original OMP algorithm. It achieves a higher recovery signal-to-noise ratio of 31.04 dB. The design includes parallel complex multiplication, matrix inversion using the Goldschmidt algorithm, and signal estimation units. Implementation on a Xilinx Virtex6 FPGA shows the architecture uses a few percentage of resources at 135.4 MHz with a reconstruction time of 170 μs, faster than existing designs.
M phil-computer-science-data-mining-projectsVijay Karan
This document provides summaries for several M.Phil Computer Science Data Mining Projects written in C#. The projects cover topics such as bridging virtual communities, mood recognition during online tests, surveying the size of the World Wide Web, knowledge sharing in virtual organizations, adaptive provisioning of human expertise in service-oriented systems, cost-aware rank joins with random and sorted access, improving data quality with dynamic forms, targeted data delivery algorithms, and sentiment classification using feature relation networks.
This document summarizes a study on the inhibition of copper corrosion in nitric acid solution by 3-arylazo 1,2,4-triazole compounds (AT). The main points are:
1) Potentiodynamic polarization and Tafel methods showed that AT compounds are good inhibitors of copper corrosion in nitric acid, achieving over 95% inhibition efficiency at 10-4M concentration.
2) The high inhibition is believed to be due to adsorption of the AT compounds or formed Cu(II)-AT complexes at the electrode interface.
3) Cathodic polarization measurements indicated AT dyes are predominantly cationic inhibitors that act in the cathodic region.
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.
DUAL POLYNOMIAL THRESHOLDING FOR TRANSFORM DENOISING IN APPLICATION TO LOCAL ...ijma
Thresholding operators have been used successfully for denoising signals, mostly in the wavelet domain.
These operators transform a noisy coefficient into a denoised coefficient with a mapping that depends on
signal statistics and the value of the noisy coefficient itself. This paper demonstrates that a polynomial
threshold mapping can be used for enhanced denoising of Principal Component Analysis (PCA) transform
coefficients. In particular, two polynomial threshold operators are used here to map the coefficients
obtained with the popular local pixel grouping method (LPG-PCA), which eventually improves the
denoising power of LPG-PCA. The method reduces the computational burden of LPG-PCA, by eliminating
the need for a second iteration in most cases. Quality metrics and visual assessment show the improvement.
The document compares three image fusion techniques: wavelet transform, IHS (Intensity-Hue-Saturation), and PCA (Principal Component Analysis). For each technique, it describes the methodology, syntax used, and features. It then applies each technique to sample images to produce fused images. The RGB values of the fused images are recorded and compared in a table. The wavelet technique uses max area selection and consistency verification for feature selection. IHS transforms RGB to IHS values and replaces intensity with a panchromatic image. PCA replaces the first principal component with a high-resolution panchromatic image. The document concludes no single technique is best and the quality depends on the application.
tScene classification using pyramid histogram ofijcsa
This document proposes a new method called Pyramid Histogram of Multi-scale Block Local Binary Pattern (PH-MBLBP) for scene classification. PH-MBLBP encodes both micro- and macro-structures of image patterns to provide a more complete representation than basic LBP. It divides images into spatial regions at multiple resolutions to capture geometric information. Experiments on 15 scene categories show PH-MBLBP outperforms SIFT and provides a powerful yet fast texture descriptor for scene recognition.
A Hybrid Chebyshev-ICA Image Fusion Method Based on Regional SaliencyTELKOMNIKA JOURNAL
An image fusion method that performs robustly for image sets heavily corrupted by noise is
presented in this paper. The approach combines the advantages of two state-of-the-art fusion techniques,
namely Independent Component Analysis (ICA) and Chebyshev Poly-nomial Analysis (CPA) fusion.
Fusion using ICA performs well in transferring the salient features of the input images into the composite
output, but its performance deteriorates severely under mild to moderate noise conditions. CPA fusion is
robust under severe noise conditions, but eliminates the high frequency information of the images
involved. We pro-pose to use ICA fusion within high activity image areas, identified by edges and strong
textured surfaces and CPA fusion in low activity areas identified by uniform background regions and weak
texture. A binary image map is used for selecting the appropriate method, which is constructed by a
standard edge detector followed by morphological operators. The results of the proposed approach are
very encouraging as far as joint fusion and denoising is concerned. The works presented may prove
beneficial for future image fusion tasks in real world applications such as surveillance, where noise is
heavily present.
This document proposes a remote sensing image fusion approach that combines the Brovey transform and wavelet transforms. The Brovey transform is used first to reduce spectral distortion, followed by a wavelet transform to reduce spatial distortion. The approach was tested on MODIS and SPOT data as well as ETM+ and SPOT data. Statistical analysis showed the proposed technique performed better than traditional fusion techniques like IHS, PCA, and the Brovey transform alone in terms of metrics like correlation coefficient, entropy, and structural similarity. Future work will focus on improving the technique and applying fused images to classification tasks.
This paper proposes an improved Semi-Global Matching (SGM) algorithm for stereo vision that introduces a "branch cost propagation" concept. This allows each path to actively search for and collect feature information, boosting meaningful signal energy and helping overcome noise. The authors implemented this "branch SGM" on an FPGA, finding it used 10% more resources but reduced error rates by 10-30% compared to standard SGM. Standard SGM is a widely used real-time stereo matching method that aggregates costs along multiple scanline paths, but can be noisy. The proposed method aims to enhance SGM's noise resistance for applications like autonomous vehicles.
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Image hashing is an efficient way to handle digital data authentication problem. Image hashing represents quality summarization of image features in compact manner. In this paper, the modified center symmetric local binary pattern (CSLBP) image hashing algorithm is proposed. Unlike CSLBP 16 bin histogram, Modified CSLBP generates 8 bin histogram without compromise on quality to generate compact hash. It has been found that, uniform quantization on a histogram with more bin results in more precision loss. To overcome quantization loss, modified CSLBP generates the two histogram of a four bin. Uniform quantization on a 4 bin histogram results in less precision loss than a 16 bin histogram. The first generated histogram represents the nearest neighbours and second one is for the diagonal neighbours. To enhance quality in terms of discrimination power, different weight factor are used during histogram generation. For the nearest and the diagonal neighbours, two local weight factors are used. One is the Standard Deviation (SD) and other is the Laplacian of Gaussian (LoG). Standard deviation represents a spread of data which captures local variation from mean. LoG is a second order derivative edge detection operator which detects edges well in presence of noise. The proposed algorithm is resilient to the various kinds of attacks. The proposed method is tested on database having malicious and non-malicious images using benchmark like NHD and ROC which confirms theoretical analysis. The experimental results shows good performance of the proposed method for various attacks despite the short hash length.
Laser Beam Targeting System is a proven stretegy of using Facebook ads to build up your business.
Facebook Ads is a essential tool and skill when you come to target right audicence for your business, products and service. It will save a lot of money and time when you know how to take advantage of Facebook.
A Step-By-Step guide will be shown and lead you from scratch to advanced. We wish you could success and using the skill to develop your business.
The document discusses an approach to upscaling video using a back iteration algorithm. It begins with an abstract describing how the back iteration algorithm is similar to back-projection algorithms used in tomography. It then discusses how the back iteration algorithm is implemented iteratively on individual video frames to upscale the video. Key aspects of the algorithm include motion estimation, intensity calculation, and registering frames at sub-pixel accuracy. The document provides details on the mathematical model and implementation of the back iteration algorithm for video upscaling. It presents results of applying the algorithm and concludes with discussing opportunities for future improvements.
Reversible watermarking based on invariant image classification and dynamic h...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
The implementation of the improved omp for aic reconstruction based on parall...Nxfee Innovation
This document presents a hardware implementation of an improved orthogonal matching pursuit (OMP) algorithm for signal reconstruction in analog-to-information converters based on compressive sensing. The proposed architecture reduces computational complexity and the number of iterations compared to the original OMP algorithm. It achieves a higher recovery signal-to-noise ratio of 31.04 dB. The design includes parallel complex multiplication, matrix inversion using the Goldschmidt algorithm, and signal estimation units. Implementation on a Xilinx Virtex6 FPGA shows the architecture uses a few percentage of resources at 135.4 MHz with a reconstruction time of 170 μs, faster than existing designs.
M phil-computer-science-data-mining-projectsVijay Karan
This document provides summaries for several M.Phil Computer Science Data Mining Projects written in C#. The projects cover topics such as bridging virtual communities, mood recognition during online tests, surveying the size of the World Wide Web, knowledge sharing in virtual organizations, adaptive provisioning of human expertise in service-oriented systems, cost-aware rank joins with random and sorted access, improving data quality with dynamic forms, targeted data delivery algorithms, and sentiment classification using feature relation networks.
This document summarizes a study on the inhibition of copper corrosion in nitric acid solution by 3-arylazo 1,2,4-triazole compounds (AT). The main points are:
1) Potentiodynamic polarization and Tafel methods showed that AT compounds are good inhibitors of copper corrosion in nitric acid, achieving over 95% inhibition efficiency at 10-4M concentration.
2) The high inhibition is believed to be due to adsorption of the AT compounds or formed Cu(II)-AT complexes at the electrode interface.
3) Cathodic polarization measurements indicated AT dyes are predominantly cationic inhibitors that act in the cathodic region.
The document lists the names and affiliations of 44 scholars who serve on the Editorial Panel for EC Chemistry. The panel members are from various universities and research institutions located around the world, including the Americas, Europe, Asia, the Middle East, and Africa.
This document contains information about several M.Phil Computer Science Cloud Computing projects written in C# and NS2. It provides the titles, languages, links, and short abstracts for each project. The projects focus on topics related to cloud computing including secure cloud storage, data integrity verification, privacy-preserving auditing, and keyword search over encrypted cloud data.
This document discusses the five senses - sight, smell, hearing, taste, and touch - and how each sense is associated with a specific body part. It notes that eyes help with sight, the nose with smell, ears with hearing, the tongue with taste, and hands/fingers and skin with touch. These five sense organs work together to help the person experience and understand the world around them. The document was created by Amita Arora to teach about the sense organs.
De juegos, sueños y esperanza. cuento de felipeada48salamanca
El documento habla sobre juegos, sueños y esperanzas a lo largo de 14 páginas, terminando con la frase "Y colorín colorado este cuento de Eduardito se ha terminado".
Ноябрь 2014. Обзор бытовых браслетов для непрерывного снятия биосигналовAlexandre Prozoroff
#mHealthLab. Младшие научные сотрудники тренируются в эпистолярном жанре. Сделали отчет об исследовании бытовых фитнес-трекеров на предмет использования для мониторинга состояния здоровья и прогноза его изменения на основе HRV. Общий итог: использовать пока нельзя.
This document discusses several options for using iOS with Drupal, including using the Services module to create RESTful APIs, using the DiOS SDK to build native iOS apps that interact with Drupal, and several additional modules and apps like Drupal Gardens, Scoreshare, Walkthough.it, and Drupal Kiosk.
The document discusses the Patient Protection and Affordable Care Act (ACA) and how it will impact free clinics and their patients. Key points include: (1) the ACA will expand Medicaid eligibility and provide subsidies to make insurance more affordable, (2) an estimated 30 million uninsured individuals, including many free clinic patients, will become eligible for coverage in 2014, and (3) while the ACA aims to reduce the uninsured population, certain groups like undocumented immigrants and those who opt out will still need care from places like free clinics.
The document provides details about a quiz competition involving 12 multiple choice questions with answers related to various topics. The first 6 questions are clockwise and the remaining 6 are anti-clockwise. Points are awarded based on correct (+10) or incorrect (-0) answers without using a double, and with a double the points are +20 or -10. The questions cover topics like Olympic medal records, the Ford Model T car, a privately purchased Scottish island, connecting images, treaty signings, cameo movie appearances of a US Senator, a statue of Usain Bolt, criticism of Internet Explorer 7, recalling the value of pi, India's first AC double-decker train connection, the origin of a famous book title, and an obscure
Risk management and patient safety are separate but related concepts. Risk management focuses on insurance, claims, and adverse events while patient safety focuses on systematic mindfulness, transparency, and viewing adverse events as learning experiences. Since the IOM report, the field has shifted from reactive to proactive approaches and viewing risks and errors as opportunities for education rather than punishment. The goal of risk management is to understand risks and advise on approaches while patient safety aims to provide a safe environment and reduce failures through understanding systems.
This document provides information on several 2015 IEEE Matlab projects related to signal processing and image analysis. It lists the project titles, languages, links, and abstracts for 10 different Matlab projects. The projects cover topics such as target source separation using deep neural networks, hyperspectral image classification using sparse representation, image denoising techniques, and cardiovascular biometrics.
This document contains summaries of several academic papers. The papers discuss topics related to computer vision, image processing, and machine learning including monocular depth estimation, subspace clustering, person re-identification, image set coding, 3D shape matching, single-image super-resolution, video synopsis, frame rate upconversion, in-loop filtering in video coding, microscopy image denoising, visual tracking, stereo matching, and brain image segmentation. Contact information is provided for TSYS Academic Projects in Adyar, India.
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Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
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Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...IOSR Journals
This document describes an implementation of fuzzy logic for high-resolution remote sensing image classification with improved accuracy. It discusses using an object-based approach with fuzzy rules to classify urban land covers in a satellite image. The approach involves image segmentation using k-means clustering or ISODATA clustering. Features are then extracted from the image objects and fuzzy logic is applied to classify the objects based on membership functions. The method was tested on different sensor and resolution images in MATLAB and showed improved classification accuracy over other techniques, achieving lower entropy in results. Future work planned includes designing an unsupervised classification model combining k-means clustering and fuzzy-based object orientation.
The document contains abstracts from 10 different research papers related to image processing and computer vision. The papers cover topics such as face recognition, reversible watermarking, building footprint detection from SAR images, change detection in remote sensing images, estimating information from image colors, vehicle detection, histology image retrieval, automatic license plate recognition, SAR image segmentation, and context-dependent logo matching and recognition.
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
SINGLE IMAGE SUPER RESOLUTION: A COMPARATIVE STUDYcsandit
The majority of applications requiring high resolution images to derive and analyze data
accurately and easily. Image super resolution is playing an effective role in those applications.
Image super resolution is the process of producing high resolution image from low resolution
image. In this paper, we study various image super resolution techniques with respect to the
quality of results and processing time. This comparative study introduces a comparison between
four algorithms of single image super-resolution. For fair comparison, the compared algorithms
are tested on the same dataset and same platform to show the major advantages of one over the
others.
Data mining projects topics for java and dot netredpel dot com
This document discusses several papers related to data mining and machine learning techniques. It begins with a brief summary of each paper, discussing the key contributions and findings. The summaries cover topics such as differential privacy-preserving data anonymization, fault detection in power systems using decision trees, temporal pattern searching in event data, high dimensional indexing for similarity search, landmark-based approximate shortest path computation, feature selection for high dimensional data, temporal pattern mining in data streams, data leakage detection, keyword search in spatial databases, analyzing relationships on Wikipedia, improving recommender systems using user-item subgroups, decision trees for uncertain data, and building confidential query services in the cloud using data perturbation.
The document discusses integrating support vector machines (SVMs) and Markov random fields (MRFs) for remote sensing image classification. SVMs are good at identifying optimal discriminant hypersurfaces but do not consider context between samples. The paper aims to integrate SVMs and MRFs to allow for contextual classification. A novel classifier is proposed that reformulates the MRF minimum-energy decision rule as an SVM discriminant function with a "contextual kernel." Experimental results on real remote sensing datasets show the proposed method provides significantly more accurate classifications compared to a standard noncontextual SVM.
This document summarizes a technique for generating highly accurate 3D surface models from sparse sensor data using sparse surface adjustment. It proposes modeling the surface as small planar patches called surfels, optimizing the poses of the sensor and surfels jointly to minimize errors, and iteratively refining correspondences between surfels. Experiments on environmental and object datasets demonstrate improved consistency over standard SLAM techniques.
Vector quantization (VQ) is a powerful technique in the field of digital image compression. The generalized
residual codebook is used to remove the distortion in the reconstructed image for further enhancing the quality of the
image. Already, Generalized Residual Vector Quantization (GRVQ) was optimized by Particle Swarm Optimization (PSO)
and Honey Bee Mating Optimization (HBMO). The performance of GRVQ was degraded due to instability in convergence
of the PSO algorithm when particle velocity is high and the performance of HBMO algorithm is depended on many
parameters which are required to tune for reducing size of codebook. So, in this paper the Artificial Plant Optimization
Algorithm (APOA) is used to optimize the parameters used in GRVQ. The Extensive experiment demonstrates that
proposed APOA-GRVQ algorithm outperforms than existing algorithm in terms of quantization accuracy and computation
accuracy.
IEEE PROJECT TOPICS &ABSTRACTS on image processingaswin tbbc
The document describes a proposed approach called Multiview Alignment Hashing (MAH) for learning image hashing functions from multiple feature representations. Existing hashing methods rely on a single feature descriptor and spectral or graph-based techniques. MAH uses Nonnegative Matrix Factorization to combine multiple views, finding a low-dimensional representation that respects the joint probability distribution of data views while discarding redundancy. It formulates the problem as non-convex optimization and solves it through alternate optimization. Evaluation on image datasets shows MAH outperforms state-of-the-art multiview hashing techniques.
This paper proposes a new algorithm for single-image super-resolution that exploits image compressibility in the wavelet domain using compressed sensing theory. The algorithm incorporates the downsampling low-pass filter into the measurement matrix to decrease coherence between the wavelet basis and sampling basis, allowing use of wavelets. It then uses a greedy algorithm to solve for sparse wavelet coefficients representing the high-resolution image. Results show improved performance over existing super-resolution approaches without requiring training data.
Visualization of hyperspectral images on parallel and distributed platform: A...IJECEIAES
The field of hyperspectral image storage and processing has undergone a remarkable evolution in recent years. The visualization of these images represents a challenge as the number of bands exceeds three bands, since direct visualization using the trivial system red, green and blue (RGB) or hue, saturation and lightness (HSL) is not feasible. One potential solution to resolve this problem is the reduction of the dimensionality of the image to three dimensions and thereafter assigning each dimension to a color. Conventional tools and algorithms have become incapable of producing results within a reasonable time. In this paper, we present a new distributed method of visualization of hyperspectral image based on the principal component analysis (PCA) and implemented in a distributed parallel environment (Apache Spark). The visualization of the big hyperspectral images with the proposed method is made in a smaller time and with the same performance as the classical method of visualization.
Fractal analysis for reduced referencejpstudcorner
To get this project in ONLINE or through TRAINING Sessions,
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1. M.Phil Computer Science Remote Sensing Projects
Web : www.kasanpro.com Email : sales@kasanpro.com
List Link : http://kasanpro.com/projects-list/m-phil-computer-science-remote-sensing-projects
Title :Unsupervised Classification of PolInSAR Data Based on Shannon Entropy Characterization With Iterative
Optimization
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/unsupervised-classification-polinsar-data-based-shannon-entropy-characterization
Abstract : In this paper, we propose a modified unsupervised classification method for the analysis of polarimetric
and inter- ferometric synthetic aperture radar (PolInSAR) images using the intensity, polarimetric and interferometric
contributions to the Shannon entropy characterization. In order to improve the classification accuracy where the
polarimetric information is similar, the method gives intensity, polarimetric and interferometric information equal
weighting to more effectively use the full range of information contained in PolInSAR data. In addition, this method
uses an iterative clustering scheme which combines the expectation maximization (EM) and fast primal-dual (FastPD)
optimization techniques to improve the classification quality. The first step of the method is to extract the Shannon
entropy char- acterization from the PolInSAR data. Then, the image is initially classified respectively by the spans of
the intensity, polarimetric and interferometric contributions to Shannon entropy. Finally, classification results of
different contributions are merged and reduced to a specified number of clusters. An iterative clustering scheme is
applied to further improve the classification results. The effectiveness of this method is demonstrated with DLR
(German Aerospace Center) E-SAR PolInSAR data and CETC (China Electronics Technology Group Corporation) 38
Institute PolInSAR data.
Title :Classification of Hyperspectral Image Based on Sparse Representation in Tangent Space
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/hyperspectral-image-classification-based-sparse-representation
Abstract : In many real-world problems, data always lie in a low-dimensional manifold. Exploiting the manifold can
greatly enhance the discrimination between different categories. In this letter, we propose a classification framework
based on sparse representation to directly exploit the underlying manifold. Specifically, using the tangent plane to
approximate the local manifold of each test sample, the proposed method classifies the sample by sparse
representation in tangent space. Unlike several existing sparse-representation-based classification methods, which
sparsely represent the test sample itself, the proposed method sparsely represents the local manifold of the test
sample by tangent plane approximation. Therefore, it goes beyond the sample itself and is more robust to kinds of
variations confronted in hyperspectral image (HSI) such as illustration differences and spectrum mixing. Experimental
results show that the proposed algorithm outperforms several state-of-the-art methods for the classification of HSI
with limited training samples.
Title :Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/hyperspectral-imagery-classification-gabor-feature
Abstract : Sparse-representation-based classification (SRC) assigns a test sample to the class with minimum
representation error via a sparse linear combination of all the training samples, which has successfully been applied
to several pattern recognition problems. According to compressive sensing theory, the l1-norm minimization could
yield the same sparse solution as the l0 norm under certain conditions. However, the computational complexity of the
l1-norm optimization process is often too high for large-scale high-dimensional data, such as hyperspectral imagery
(HSI). To make matter worse, a large number of training data are required to cover the whole sample space, which is
difficult to obtain for hyperspectral data in practice. Recent advances have revealed that it is the collaborative
representation but not the l1-norm sparsity that makes the SRC scheme powerful. Therefore, in this paper, a 3-D
Gabor feature-based collaborative representation (3GCR) approach is proposed for HSI classification. When 3-D
Gabor transformation could significantly increase the discrimination power of material features, a nonparametric and
2. effective l2-norm collaborative representation method is developed to calculate the coefficients. Due to the simplicity
of the method, the computational cost has been substantially reduced; thus, all the extracted Gabor features can be
directly utilized to code the test sample, which conversely makes the l2-norm collaborative representation robust to
noise and greatly improves the classification accuracy. The extensive experiments on two real hyperspectral data sets
have shown higher performance of the proposed 3GCR over the state-of-the-art methods in the literature, in terms of
both the classifier complexity and generalization ability from very small training sets.
Title :Extracting Man-Made Objects From High Spatial Resolution Remote Sensing Images via Fast Level Set
Evolutions
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/extracting-man-made-objects-from-high-spatial-resolution-remote-sensing-images
Abstract : Object extraction from remote sensing images has long been an intensive research topic in the field of
surveying and mapping. Most past methods are devoted to handling just one type of object, and little attention has
been paid to improving the computational efficiency. In recent years, level set evolution (LSE) has been shown to be
very promising for object extraction in the field of image processing because it can handle topological changes
automatically while achieving high accuracy. However, the application of state-of-the-art LSEs is compromised by
laborious parameter tuning and expensive computation. In this paper, we proposed two fast LSEs for manmade
object extraction from high spatial resolution remote sensing images. We replaced the traditional mean
curvature-based regularization term by a Gaussian kernel, and it is mathematically sound to do that. Thus, we can
use a larger time step in the numerical scheme to expedite the proposed LSEs. Compared with existing methods, the
proposed LSEs are significantly faster. Most importantly, they involve much fewer parameters while achieving better
performance. Their advantages over other state-of-the-art approaches have been verified by a range of experiments.
Title :Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral
Image Classification
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/hyperspectral-dimension-reduction-using-spatial-spectral-regularized-local-discriminant
Abstract : Dimension reduction (DR) is a necessary and helpful preprocessing for hyperspectral image (HSI)
classification. In this paper, we propose a spatial and spectral regularized local discriminant embedding (SSRLDE)
method for DR of hyperspectral data. In SSRLDE, hyperspectral pixels are first smoothed by the multiscale spatial
weighted mean filtering. Then, the local similarity information is described by integrating a spectral-domain regularized
local preserving scatter matrix and a spatial-domain local pixel neighborhood preserving scatter matrix. Finally, the
optimal discriminative projection is learned by minimizing a local spatial-spectral scatter and maximizing a modified
total data scatter. Experimental results on benchmark hyperspectral data sets show that the proposed SSRLDE
significantly outperforms the state-of-the-art DR methods for HSI classification.
M.Phil Computer Science Remote Sensing Projects
Title :Aerial Image Registration for Tracking
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/aerial-image-registration-tracking
Abstract : To facilitate the tracking of moving targets in a video, the relation between the camera and the scene is
kept fixed by registering the video frames at the ground level. When the camera capturing the video is fixed with
respect to the scene, detected motion will represent the target motion. However, when a camera in motion is used to
capture the video, image registration at ground level is required to separate camera motion from target motion. An
image registration method is introduced that is capable of registering images from different views of a 3-D scene in
the presence of occlusion. The proposed method is capable of withstanding considerable occlusion and
homogeneous areas in images. The only requirement of the method is for the ground to be locally flat and sufficient
ground cover be visible in the frames being registered. Experimental results of 17 videos fromthe Brown University
data set demonstrate robustness of the method in registering consecutive frames in videos covering various urban
and suburban scenes. Additional experimental results are presented demonstrating the suitability of the method in
registering images captured from different views of hilly and coastal scenes.
Title :An Adaptive Pixon Extraction Technique for Multispectral/Hyperspectral Image Classification
Language : Matlab
3. Project Link :
http://kasanpro.com/p/matlab/pixon-extraction-technique-multispectral-hyperspectral-image-classification
Abstract : Hyperspectral imaging has gained significant interest in the past few decades, particularly in remote
sensing applications. The considerably high spatial and spectral resolution of modern remotely sensed data often
provides more accurate information about the scene. However, the complexity and dimensionality of such data, as
well as potentially unwanted details embedded in the images, may act as a degrading factor in some applications
such as classification. One solution to this issue is to utilize the spatial-spectral features to extract segments before
the classification step. This preprocessing often leads to better classification results and a considerable decrease in
computational time. In this letter, we propose a Pixon-based image segmentation method, which benefits from a
preprocessing step based on partial differential equation to extractmore homogenous segments.Moreover, a fast
algorithm has been presented to adaptively tune the required parameters used in our Pixon-based schema. The
acquired segments are then fed into the support vector machine classifier, and the final thematic class maps are
produced. Experimental results on multi/hyperspectral data are encouraging to apply the proposed Pixons for
classification.
Title :Saliency-Guided Unsupervised Feature Learning for Scene Classification
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/scene-classification-saliency-guided-unsupervised-feature-learning
Abstract : Due to the rapid technological development of various different satellite sensors, a huge volume of
high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from
such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature
learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set
of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an
unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not
need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that
the statistics generated from the learned feature extractors can characterize a complex scene very well and can
produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ
a recently developed regularization method called "dropout," which has proved to be very effective in image
classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the
UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data
set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that
were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the
highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based
scene classification method provides more accurate classification results than the other
latent-Dirichlet-allocation-based methods and the sparse coding method.
http://kasanpro.com/ieee/final-year-project-center-dharmapuri-reviews
Title :A New Framework for SAR Multitemporal Data RGB Representation: Rationale and Products
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/sar-multitemporal-data-rgb-representation
Abstract : This paper presents the multitemporal adaptive processing (MAP3) framework for the treatment of
multitemporal synthetic aperture radar (SAR) images. The framework is organized in three major activities dealing
with calibration, adaptability, and representation. The processing chain has been designed looking at the simplicity,
i.e., the minimization of the operations needed to obtain the products, and at the algorithms' availability in the
literature. Innovation has been provided in the crosscalibration step, which is solved introducing the variable
amplitude levels equalization (VALE) method, through which it is possible to establish a common metrics for the
measurement of the amplitude levels exhibited by the images of the series. Representation issues are discussed with
an application-based approach, supported by examples with regard to semiarid and temperate regions in which
amplitude maps and interferometric coherence are combined in an original way.
Title :Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing
Images
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/segmentation-arbitrarily-large-remote-sensing-images-stable-mean-shift-algorithm
4. Abstract : Segmentation of real-world remote sensing images is challenging because of the large size of those data,
particularly for very high resolution imagery. However, a lot of high-level remote sensing methods rely on
segmentation at some point and are therefore difficult to assess at full image scale, for real remote sensing
applications. In this paper, we define a new property called stability of segmentation algorithms and demonstrate that
pieceor tile-wise computation of a stable segmentation algorithm can be achieved with identical results with respect to
processing the whole image at once. We also derive a technique to empirically estimate the stability of a given
segmentation algorithm and apply it to four different algorithms. Among those algorithms, the mean-shift algorithm is
found to be quite unstable. We propose a modified version of this algorithm enforcing its stability and thus allowing for
tile-wise computation with identical results. Finally, we present results of this method and discuss the various trends
and applications.
M.Phil Computer Science Remote Sensing Projects
Title :Rotation-Invariant Object Detection in Remote Sensing Images Based on Radial-Gradient Angle
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/rotation-invariant-object-detection-remote-sensing-images-based-radial-gradient-angle
Abstract : To improve the detection precision in complicated backgrounds, a novel rotation-invariant object detection
method to detect objects in remote sensing images is proposed in this letter. First, a rotation-invariant feature called
radial-gradient angle (RGA) is defined and used to find potential object pixels from the detected image blocks by
combining with radial distance. Then, a principal direction voting process is proposed to gather the evidence of
objects from potential object pixels. Since the RGA combined with the radial distance is discriminative and the voting
process gathers the evidence of objects independently, the interference of the backgrounds is effectively reduced.
Experimental results demonstrate that the proposed method outperforms other existing well-known methods (such as
the shape context-based method and rotation-invariant part-based model) and achieves higher detection precision for
objects with different directions and shapes in complicated background. Moreover, the antinoise performance and
parameter influence are also discussed.
Title :A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble
Strategy
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/self-training-based-unsupervised-satellite-image-classification-techni
Abstract : This letter addresses the problem of unsupervised land-cover classification of remotely sensed
multispectral satellite images fromthe perspective of cluster ensembles and self-learning. The cluster ensembles
combine multiple data partitions generated by different clustering algorithms into a single robust solution. A
cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative
expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters
considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover
classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is
introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set
obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier,
although trained without any external supervision, reduces the effect of data overlapping from different clusters which
otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a
medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual
clustering of the ensemble.
Title :An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/sift-based-mode-seeking-algorithm-sub-pixel-registration-remotely-sensed-images
Abstract : Several image registration methods, based on the scaled-invariant feature transform (SIFT) technique,
have appeared recently in the remote sensing literature. All of these methods attempt to overcome problems
encountered by SIFT in multimodal remotely sensed imagery, in terms of the quality of its feature correspondences.
The deterministic method presented in this letter exploits the fact that each SIFT feature is associated with a scale,
orientation, and position to perform mode seeking (in transformation space) to eliminate outlying corresponding key
points (i.e., features) and improve the overall match obtained. We also present an exhaustive empirical study on a
variety of test cases, which demonstrates that our method is highly accurate and rather fast. The algorithm is capable
of automatically detecting whether it succeeded or failed.
5. Title :Remote Sensing Image Segmentation Based on an Improved 2-D Gradient Histogram and MMAD Model
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/remote-sensing-image-segmentation-based-improved-2-d-gradient-histogram-mm
Abstract : A novel remote sensing image segmentation algorithm based on an improved 2-D gradient histogram and
minimum mean absolute deviation (MMAD) model is proposed in this letter. We extract the global features as a 1-D
histogram from an improved 2-D gradient histogram by diagonal projection and subsequently use the MMAD model
on the 1-D histogram to implement the optimal threshold. Experiments on remote sensing images indicate that the
new algorithm provides accurate segmentation results, particularly for images characterized by Laplace distribution
histograms. Furthermore, the new algorithm has low time consumption.
Title :Building Change Detection Based on Satellite Stereo Imagery and Digital Surface Models
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/building-change-detection-based-satellite-stereo-imagery-digital-surface-models
Abstract : Building change detection is a major issue for urban area monitoring. Due to different imaging conditions
and sensor parameters, 2-D information delivered by satellite images from different dates is often not sufficient when
dealing with building changes. Moreover, due to the similar spectral characteristics, it is often difficult to distinguish
buildings from other man-made constructions, like roads and bridges, during the change detection procedure.
Therefore, stereo imagery is of importance to provide the height component which is very helpful in analyzing 3-D
building changes. In this paper, we propose a change detection method based on stereo imagery and digital surface
models (DSMs) generated with stereo matching methodology and provide a solution by the joint use of height
changes and Kullback-Leibler divergence similarity measure between the original images. The Dempster-Shafer
fusion theory is adopted to combine these two change indicators to improve the accuracy. In addition, vegetation and
shadow classifications are used as no-building change indicators for refining the change detection results. In the end,
an object-based building extraction method based on shape features is performed. For evaluation purpose, the
proposed method is applied in two test areas, one is in an industrial area in Korea with stereo imagery from the same
sensor and the other represents a dense urban area in Germany using stereo imagery from different sensors with
different resolutions. Our experimental results con- firm the efficiency and high accuracy of the proposed methodology
even for different kinds and combinations of stereo images and consequently different DSM qualities.
M.Phil Computer Science Remote Sensing Projects
Title :Hyperspectral Image Denoising With a Spatial-Spectral View Fusion Strategy
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/hyperspectral-image-denoising-spectral-fusion
Abstract : In this paper, we propose a hyperspectral image denoising algorithm with a spatial-spectral view fusion
strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the hyperspectral total variation algorithm, but
applied to both the spatial and spectral views. A metric Q-weighted fusion algorithm is then adopted to merge the
denoising results of the two views together, so that the denoising result is improved. A number of experiments
illustrate that the proposed approach can produce a better denoising result than both the individual spatial and
spectral view denoising results.
http://kasanpro.com/ieee/final-year-project-center-dharmapuri-reviews
Title :Land cover change detection by wavelet feature extraction and post classification
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/land-cover-change-detection-wavelet-feature-extraction-post-classification
Abstract :
Title :Land cover change detection by wavelet features and change vector analysis
Language : Matlab
6. Project Link : http://kasanpro.com/p/matlab/land-cover-change-detection-wavelet-features-change-vector-analysis
Abstract : Traditional Change Vector Analysis in Multi-temporal space (TCVAM) can effectively extract land cover
change information based on VI time series, and it has been one of the main methods to detect land cover change at
large scale. However, the TCVAM may exaggerate the change information and mix the land cover conversion and
land cover modification because of the oversensitivity to the changes of VI values. The paper proposes an Improved
Change Vector Analysis in Multi-temporal space (ICVAM) based on cross-correlogram spectral matching algorithm
and applies it in the Beijing-Tianjin-Tangshan urban agglomeration district, China, using MODIS_EVI time series data
to test the performance of the ICVAM. The results demonstrated the improvement of the ICVAM compared to the
TCVAM: overall accuracy increased by 10.80% and the kappa coefficient increased by 0.13. The ICVAM has great
potential to be widely used for land cover change detection based on VI time series at large scale.
Title :Change Detection of Hyper Spectral Remote Sensing Image by Multilevel Image Segmentation
Language : Java
Project Link :
http://kasanpro.com/p/java/change-detection-hyper-spectral-remote-sensing-image-multilevel-image-segmentation
Abstract : Land cover composition and change are important factors that affect ecosystem condition and function.
Remote sensing is the most important and effective way to acquire data of land cover. The paper proposes an
Improved Change detection with Hyperspectral remote sensing images. Hyperspectral remote sensing images
contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design
accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding
method is introduced for the seg- mentation of hyperspectral and multispectral images. The new method is based on
fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions
that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of
particles. And finally Post-classification Comparison Change Detection applied which is the most commonly used
quantitative method of change detection.
Title :A Sparse Image Fusion Algorithm With Application to Pan-Sharpening
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/sparse-image-fusion-algorithm-with-application-pan-sharpening
Abstract : Data provided by most optical Earth observation satellites such as IKONOS, QuickBird, and GeoEye are
composed of a panchromatic channel of high spatial resolution (HR) and several multispectral channels at a lower
spatial resolution (LR). The fusion of an HR panchromatic and the corresponding LR spectral channels is called
"pan-sharpening." It aims at obtaining an HR multispectral image. In this paper, we propose a new pan-sharpening
method named Sparse Fusion of Images (SparseFI, pronounced as "sparsify"). SparseFI is based on the
compressive sensing theory and explores the sparse representation of HR/LR multispectral image patches in the
dictionary pairs cotrained from the panchromatic image and its downsampled LR version. Compared with
conventional methods, it "learns" from, i.e., adapts itself to, the data and has generally better performance than
existing methods. Due to the fact that the SparseFI method does not assume any spectral composition model of the
panchromatic image and due to the super-resolution capability and robustness of sparse signal reconstruction
algorithms, it gives higher spatial resolution and, in most cases, less spectral distortion compared with the
conventional methods.
M.Phil Computer Science Remote Sensing Projects
Title :Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization
Language : Java
Project Link : http://kasanpro.com/p/java/multilevel-image-segmentation-based-particle-swarm-optimization
Abstract : Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality
of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such
imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and
multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO)
which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional
derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for
each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to
an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental
7. results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of
multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both
CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class
variance in less computational time than the other approaches. In addition, a new classification approach based on
support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation
method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.