"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)
"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.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
www.irjes.com
tScene classification using pyramid histogram ofijcsa
Pyramid Histogram of Multi-scale Block Local Binary Pattern (PH-MBLBP) descriptor for recognizing
scene categories, is presented in this paper. We show that scene categorization, especially for indoor and
outdoor environments, requires its visual descriptor to process properties that are different from other
vision domains (e.g., SIFT descriptor used for object categorization). Our proposed PH-MBLBP satisfies
these properties and suits the scene categorization task. Since the proposed PH-MBLBP mainly encodes
micro- and macro-structures of image patterns, thus, it provides relatively more complete image descriptor
than the basic LBP operator. Moreover, our PH-MBLBP descriptor is more powerful texture descriptor
than the conventional operator and it can also be calculated extremely fast. Our experiments demonstrate
that PH-MBLBP outperforms the other descriptor such as SIFT.
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.
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.
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
Sparse signal recovery becomes extremely challenging for a variety of real-time applications. In this paper, we improve the orthogonal matching pursuit (OMP) algorithm based on parallel correlation indices selection mechanism in each iteration and Goldschmidt algorithm. Simulation results show that the improved OMP algorithm with a reduced number of iterations and low hardware complexity of matrix operations has higher success rate and recovery signal-to-noise-ratio (RSNR) for sparse signal recovery. This paper presents an efficient complex valued system hardware architecture of the recovery algorithm for analog-to-information structure based on compressive sensing. The proposed architecture is implemented and validated on the Xilinx Virtex6 field-programmable gate array (FPGA) for signal reconstruction with N = 1024, K = 36, and M = 256. The implementation results showed that the improved OMP algorithm achieved a higher RSNR of 31.04 dB compared with the original OMP algorithm. This synthesized design consumes a few percentages of the hardware resources of the FPGA chip with the clock frequency of 135.4 MHZ and reconstruction time of 170 µs, which is faster than the existing design.
Aminic nitrogen- bearing polydentate Schiff base compounds as corrosion inhibitors for iron in acidic and alkaline media: A combined experimental and DFT studies
Loutfy H. Madkour 1,*, S. K. Elroby2
electric material CNC cutting table cut the electric material to make sample and do small production.
Skype: trinityhu
MSN: trinityhu@hotmail.com
http://www.packagingmachiney.com/
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.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
www.irjes.com
tScene classification using pyramid histogram ofijcsa
Pyramid Histogram of Multi-scale Block Local Binary Pattern (PH-MBLBP) descriptor for recognizing
scene categories, is presented in this paper. We show that scene categorization, especially for indoor and
outdoor environments, requires its visual descriptor to process properties that are different from other
vision domains (e.g., SIFT descriptor used for object categorization). Our proposed PH-MBLBP satisfies
these properties and suits the scene categorization task. Since the proposed PH-MBLBP mainly encodes
micro- and macro-structures of image patterns, thus, it provides relatively more complete image descriptor
than the basic LBP operator. Moreover, our PH-MBLBP descriptor is more powerful texture descriptor
than the conventional operator and it can also be calculated extremely fast. Our experiments demonstrate
that PH-MBLBP outperforms the other descriptor such as SIFT.
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.
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.
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
Sparse signal recovery becomes extremely challenging for a variety of real-time applications. In this paper, we improve the orthogonal matching pursuit (OMP) algorithm based on parallel correlation indices selection mechanism in each iteration and Goldschmidt algorithm. Simulation results show that the improved OMP algorithm with a reduced number of iterations and low hardware complexity of matrix operations has higher success rate and recovery signal-to-noise-ratio (RSNR) for sparse signal recovery. This paper presents an efficient complex valued system hardware architecture of the recovery algorithm for analog-to-information structure based on compressive sensing. The proposed architecture is implemented and validated on the Xilinx Virtex6 field-programmable gate array (FPGA) for signal reconstruction with N = 1024, K = 36, and M = 256. The implementation results showed that the improved OMP algorithm achieved a higher RSNR of 31.04 dB compared with the original OMP algorithm. This synthesized design consumes a few percentages of the hardware resources of the FPGA chip with the clock frequency of 135.4 MHZ and reconstruction time of 170 µs, which is faster than the existing design.
Aminic nitrogen- bearing polydentate Schiff base compounds as corrosion inhibitors for iron in acidic and alkaline media: A combined experimental and DFT studies
Loutfy H. Madkour 1,*, S. K. Elroby2
electric material CNC cutting table cut the electric material to make sample and do small production.
Skype: trinityhu
MSN: trinityhu@hotmail.com
http://www.packagingmachiney.com/
Parallel and Distributed System IEEE 2015 ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for the year 2015
Define and understand communication and the communication process
List and overcome the filters/barriers in a communication process
Practice active listening
Tips to improve verbal and non verbal communication
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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.
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.
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,
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 restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
M.Phil Computer Science Wireless Communication ProjectsVijay Karan
List of Wireless Communication IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Wireless Communication for M.Phil Computer Science students.
M.E Computer Science Wireless Communication ProjectsVijay Karan
List of Wireless Communication IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Wireless Communication for M.E Computer Science students.
M.Phil Computer Science Parallel and Distributed System ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.Phil Computer Science students.
M.E Computer Science Parallel and Distributed System ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.E Computer Science students.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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-ariyalur-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-ariyalur-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.