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Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
The paper is comprehensive in coverage of both hyperspectral image analysis tasks and machine learning algorithms.  ...  We also discuss the open challenges in the field of hyperspectral image analysis and explore possible future directions.  ...  In this method, higher order potentials were defined over pixels inside each segment obtained by unsupervised segmentation of the hyperspectral image by mean-shift algorithm.  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., and Foerster, S  ...  ., +, JSTARS June 2014 2571-2582 Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection.  ...  ., +, JSTARS June 2014 2246-2255 Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

Unfolding the Restrained Encountered in Hyperspectral Images

2019 International journal of recent technology and engineering  
Moreover with this advancement in the field of image processing, precise and huge information capturing images are desired. The hyperspectral images find its place in such fields of applications.  ...  A clear depiction of the current issues and approaches in the field of compression as well as some general issues are also discussed towards the end section.  ...  Fig. 1 . 1 General framework of Classification in HSI with an elaborated focus on supervised and unsupervised classification Fig . 2 . 2 An AVIRIS hyperspectral image of true color over the Kennedy Space  ... 
doi:10.35940/ijrte.b1763.078219 fatcat:cgdtvtrzbzaylm4eowtog52x3m

Hyperspectral Image Classification [chapter]

Rajesh Gogineni, Ashvini Chaturvedi
2019 Processing and Analysis of Hyperspectral Data [Working Title]  
Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel.  ...  Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images.  ...  The familiar unsupervised methods are principal component analysis (PCA) [16] and independent component analysis (ICA) [17].  ... 
doi:10.5772/intechopen.88925 fatcat:7ixv44bobbd3vkp7hn5c6tlb2y

Assessing the performance of multiple spectral–spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

Reddy Pullanagari, Gábor Kereszturi, Ian J. Yule, Pedram Ghamisi
2017 Journal of Applied Remote Sensing  
The segmentation procedure was performed on the hyperspectral image using the Hidden Markov Random Field (HMRF).  ...  We tested the proposed method on a real application of hyperspectral image acquired from AisaFENIX and also on widely used hyperspectral images (ROSIS and AVIRIS).  ...  Principal component analysis (PCA) [21] , minimum noise fraction (MNF) [22] , projection pursuit (PP), independent component analysis (ICA) [23] , are widely used linear unsupervised approaches for  ... 
doi:10.1117/1.jrs.11.026009 fatcat:apc5l5hrnfeqdhea4g7cdgjb7y

2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53

2015 IEEE Transactions on Geoscience and Remote Sensing  
., +, TGRS Nov. 2015 6148-6162 Representation and Spatially Adaptive Segmentation for PolSAR Images Based on Wedgelet Analysis.  ...  ., +, TGRS July 2015 3600-3614 Precise Focusing of Airborne SAR Data With Wide Apertures Large Trajectory Deviations: A Chirp Modulated Back-Projection Approach.  ... 
doi:10.1109/tgrs.2015.2513444 fatcat:zuklkpk4gjdxjegoym5oagotzq

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Jose M. Bioucas-Dias, Antonio Plaza, Gustavo Camps-Valls, Paul Scheunders, Nasser Nasrabadi, Jocelyn Chanussot
2013 IEEE Geoscience and Remote Sensing Magazine  
This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection  ...  Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions.  ...  For instance, a relevant unsupervised method successfully applied to hyperspectral image data is Tilton's recursive hierarchical segmentation (RHSEG) algorithm 11 .  ... 
doi:10.1109/mgrs.2013.2244672 fatcat:4tk7q6izd5hevhnrck36i5wkiy

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Chiman Kwan
2019 Information  
Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection.  ...  For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection?  ...  Acknowledgments: C.K. would like to thank all the anonymous reviewers for their comments and suggestions, which greatly improved the quality of this paper.  ... 
doi:10.3390/info10110353 fatcat:kp67hz7znngbddskpjkz5lvy3e

Survey of contemporary trends in color image segmentation

Sreenath Rao Vantaram, Eli Saber
2012 Journal of Electronic Imaging (JEI)  
In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing  ...  Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial  ...  Carlson Center for Imaging Science and the Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY.  ... 
doi:10.1117/1.jei.21.4.040901 fatcat:dco5abqsvzcuxi5ydktbssz6xi

A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis

Subhashree Subudhi, Ram Narayan Patro, Pradyut Kumar Biswal, Fabio Dell'Acqua
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Recent developments in hyperspectral sensors have made it possible to acquire HyperSpectral Images (HSI) with higher spectral and spatial resolution.  ...  Superpixel segmentation is a process of segmenting the spatial image into several semantic sub-regions with similar characteristic features.  ...  Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Minimum Noise Fraction (MNF) are popular linear unsupervised methods.  ... 
doi:10.1109/jstars.2021.3076005 fatcat:smfb6jeox5eldbv6ys7ioeoko4

Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Luis Gomez-Chova, Devis Tuia, Gabriele Moser, Gustau Camps-Valls
2015 Proceedings of the IEEE  
Multiple and heterogeneous image sources can be available for the same geographical region: multispectral, hyperspectral, radar, multitemporal and multiangular images can nowadays be acquired over a given  ...  We also highlight the most recent advances, which exploit synergies with machine learning and signal processing: sparse methods, kernel-based fusion, 2 Markov modeling, and manifold alignment.  ...  ACKNOWLEDGEMENTS The authors would like to thank DigitalGlobe Inc. for the optical data on Rio and Haiti, and the Italian Space Agency for the SAR data on Haiti.  ... 
doi:10.1109/jproc.2015.2449668 fatcat:gaficd2bcrbshcrds3a2wfa25a

Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing [article]

Vishal Monga, Yuelong Li, Yonina C. Eldar
2020 arXiv   pre-print
We extensively cover popular techniques for algorithm unrolling in various domains of signal and image processing including imaging, vision and recognition, and speech processing.  ...  In this article, we review algorithm unrolling for signal and image processing.  ...  Compared with traditional low-level image segmentation, it provides additional information about object categories, and thus creates semantically meaningful segmented objects.  ... 
arXiv:1912.10557v3 fatcat:klkwcacburca3hr63m7v77pvnq

Hyperspectral Imagery for Environmental Urban Planning

C. Weber, R. Aguejdad, X Briottet, J. Avala, S. Fabre, J. Demuynck, E. Zenou, Y. Deville, M.S. Karoui, F.Z. Benhalouche, S. Gadal, W. Ourghemmi (+3 others)
2018 IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium  
., and Bruzzone, L. (2010a). Extended profiles with morphological attribute filters for the analysis of hyperspectral data.  ...  ., and Snyder, W. (2003). Band selection using independent component analysis for hyperspectral image processing.  ...  Duarte, "An Overview of Blind Source Separation Methods for Linear-Quadratic and Post-nonlinear Mixtures," Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, vol. 9237,  ... 
doi:10.1109/igarss.2018.8519085 dblp:conf/igarss/WeberABAFDZDKBG18 fatcat:eknmohwbirb67pnmn7adsjjija

Efficient Employment of Non-Reactive Sensors

Moshe Kress, Roberto Szechtman, Jason S. Jones
2008 Military Operations Research  
, Unsupervised Target Detection in Hyperspectral Images Using Projection Pursuit, IEEE Transactions on Geoscience and Remote Sensing, 39:1380 -1391 Clare Phil, Bernhardt Mark, Oxford William, Murphy Sean  ...  Achard V., Landrevie A. and Fort J.C., 2004, Anomalies Detection in Hyperspectral Imagery Using Projection Pursuit Algorithm, SPIE Conference on Image and Signal Processing for Remote Sensing X, 5573:193  ...  APPROACH PHASE by L.  ... 
doi:10.5711/morj.13.4.45 fatcat:5txjd4lsmbhszk3pabwqtrlkii

Sparse Modeling for Image and Vision Processing [article]

Julien Mairal, Jean Ponce (Ecole Normale Supérieure)
2014 arXiv   pre-print
The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing.  ...  In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements.  ...  joint centre, and a French grant from the Agence Nationale de la Recherche (MACARON project, ANR-14-CE23-0003-01).  ... 
arXiv:1411.3230v2 fatcat:qeqhd2rbvncqtl6ktuj6mda6ia
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