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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
This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature.  ...  mixture models, ensemble learning, directed graphical models, undirected graphical models, clustering, Gaussian processes, Dirichlet processes, and deep learning.  ...  Dirichlet process has been applied for classifying and unmixing hyperspectral data. In [161] , a spatially adaptive, semi-supervised DP based classification algorithm was proposed.  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

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  ...  Hyperspectral remote sensing technology has advanced significantly in the past two decades.  ...  The process of detecting and identifying a target in hyperspectral imagery can be considered as consisting of two stages.  ... 
doi:10.1109/mgrs.2013.2244672 fatcat:4tk7q6izd5hevhnrck36i5wkiy

Self-Adjusting Models for Semi-supervised Learning in Partially Observed Settings

Ferit Akova, Murat Dundar, Yuan Qi, Bartek Rajwa
2012 2012 IEEE 12th International Conference on Data Mining  
We demonstrate the feasibility of the proposed approach for semi-supervised learning in two such applications.  ...  We model each class data by a mixture model and use a hierarchical Dirichlet process (HDP) to model observed as well as unobserved classes.  ...  The widespread use of machine-learning techniques in the analysis of hyperspectral imagery is usually hindered by the lack of well-defined ground truth.  ... 
doi:10.1109/icdm.2012.60 dblp:conf/icdm/AkovaDQR12 fatcat:snpeheacrfelpglo3dkc2tpibq

Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources

S. Salcedo-Sanz, P. Ghamisi, M. Piles, M. Werner, L. Cuadra, A. Moreno-Martínez, E. Izquierdo-Verdiguier, J. Muñoz-Marí, A. Mosavi, G. Camps-Valls
2020 Information Fusion  
This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation.  ...  This paper produces a thorough review of the latest work on information fusion for Earth observation, with a practical intention, not only focusing on describing the most relevant previous works in the  ...  The results have been tested in a specific database of multimodal ground-based clouds. In [36] a cloud detection method based on CNN was proposed.  ... 
doi:10.1016/j.inffus.2020.07.004 fatcat:m57jbkxnhjfqvgt5ol6iei35ta

Using Artificial Intelligence for Space Challenges: A Survey

Antonia Russo, Gianluca Lax
2022 Applied Sciences  
Moreover, we present and discuss current solutions proposed for each challenge to allow researchers to identify and compare the state of the art in this context.  ...  Artificial intelligence is applied to many fields and contributes to many important applications and research areas, such as intelligent data processing, natural language processing, autonomous vehicles  ...  In [99] , a framework based on semi-supervised spectral learning and GANs is proposed for hyperspectral image anomaly detection.  ... 
doi:10.3390/app12105106 fatcat:jlkdbe4panbqfhridpozon6634

Comparative Analysis of Covariance Matrix Estimation for Anomaly Detection in Hyperspectral Images

Santiago Velasco-Forero, Marcus Chen, Alvina Goh, Sze Kim Pang
2015 IEEE Journal on Selected Topics in Signal Processing  
Anomaly detection is challenging in hyperspectral images because the data has a high correlation among dimensions, heavy tailed distributions and multiple clusters.  ...  Covariance matrix estimation is fundamental for anomaly detection, especially for the Reed and Xiaoli Yu (RX) detector.  ...  INTRODUCTION Hyperspectral (HS) imagery provides rich information both spatially and spectrally.  ... 
doi:10.1109/jstsp.2015.2442213 fatcat:bkqf435aj5f4hmngticlzafa5u

Spatiotemporal event detection: a review

Manzhu Yu, Myra Bambacus, Guido Cervone, Keith Clarke, Daniel Duffy, Qunying Huang, Jing Li, Wenwen Li, Zhenlong Li, Qian Liu, Bernd Resch, Jingchao Yang (+1 others)
2020 International Journal of Digital Earth  
processes based on the extracted events) as an agenda for future event detection research.  ...  Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and  ...  Other authors contributedly equally to the paper for idea, writeup, and revision. They are listed in alphabetical order based on last name.  ... 
doi:10.1080/17538947.2020.1738569 fatcat:urbuc2zii5bajjmmkzu6idyrg4

Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest

Yonghao Xu, Bo Du, Liangpei Zhang, Daniele Cerra, Miguel Pato, Emiliano Carmona, Saurabh Prasad, Naoto Yokoya, Ronny Hansch, Bertrand Le Saux
2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Index Terms-Convolutional neural networks (CNN), deep learning, hyperspectral (HS) imaging (HSI), image analysis and data fusion, multimodal, multiresolution, multisource, multispectral light detection  ...  Participants proposed elaborate approaches rooted in remote-sensing, and also in machine learning and computer vision, to make the most of the available data.  ...  Mukherjee for their contributions in the preparation of the ground truth. D. Cerra, M. Pato, and E.  ... 
doi:10.1109/jstars.2019.2911113 fatcat:r5qtkkthfvf7dpde3adq6xsrh4

Spatial Techniques for Image Classification [chapter]

Selim Aksoy
2007 Image Processing for Remote Sensing  
The constant increase in the amount and resolution of remotely sensed imagery necessitates development of intelligent systems for automatic processing and classification.  ...  We describe a Bayesian framework that uses spatial information for classification of high-resolution images. First, spectral and textural features are extracted for each pixel.  ...  ., for the DC Mall data set, and Dr. Paolo Gamba from the University of Pavia, Italy, for the Centre and University data sets.  ... 
doi:10.1201/9781420066654.ch10 fatcat:nsxe4dbqgjcs5lqo6xj5mktx7i

Spatial Techniques for Image Classification [chapter]

Selim Aksoy
2006 Signal and Image Processing for Remote Sensing  
The constant increase in the amount and resolution of remotely sensed imagery necessitates development of intelligent systems for automatic processing and classification.  ...  We describe a Bayesian framework that uses spatial information for classification of high-resolution images. First, spectral and textural features are extracted for each pixel.  ...  ., for the DC Mall data set, and Dr. Paolo Gamba from the University of Pavia, Italy, for the Centre and University data sets.  ... 
doi:10.1201/9781420003130.ch22 fatcat:yw2nvgkjgjfetdhpkec2nd7uti

2020 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 42

2021 IEEE Transactions on Pattern Analysis and Machine Intelligence  
., +, TPAMI April 2020 780-792 Entropy Context-Aware Query Selection for Active Learning in Event Recognition.  ...  Matsukawa, T., +, TPAMI Sept. 2020 2179-2194 Hyperspectral Recovery from RGB Images using Gaussian Processes.  ...  ., +, 2581 -2593 Open-Ended Learning of Latent Topics for 3D Object Recognition. Kasaei, S.H., +, 2567 -2580 Object Detection in Videos by High Quality Object Linking.  ... 
doi:10.1109/tpami.2020.3036557 fatcat:3j6s2l53x5eqxnlsptsgbjeebe

How can big data and machine learning benefit environment and water management: A survey of methods, applications, and future directions

Alexander Y. Sun, Bridget R Scanlon
2019 Environmental Research Letters  
The authors are grateful to Dr Michael Fienen and an anonymous reviewer for their constructive comments on the original manuscript.  ...  In addition, the process-based coupled models are often computationally costly to run and not suitable for web-based decision support.  ...  machines perform the so-called association type learning by looking for regularities in observations); (b) whether the mainstream scientific community, deeply rooted with process-based causal reasoning  ... 
doi:10.1088/1748-9326/ab1b7d fatcat:vx4thuy45vhlnmhu7bk2hwh2g4

Semantic Boosting: Enhancing Deep Learning Based LULC Classification

Marvin Mc Mc Cutchan, Alexis J. Comber, Ioannis Giannopoulos, Manuela Canestrini
2021 Remote Sensing  
In each experiment CORINE LULC data was used as a ground truth and predicted using imagery from Sentinel-2 and semantics from LinkedGeoData using deep learning.  ...  In this paper, we demonstrate how semantics can be fused with imagery to classify LULC. Three experiments were performed to explore and highlight the impact and potential of semantics for this task.  ...  3, which was based on imagery only, can be seen in Figure 5 .  ... 
doi:10.3390/rs13163197 fatcat:5fzf623lq5arnkqzplap6xrzhi

Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics

Angela Lausch, Michael E. Schaepman, Andrew K. Skidmore, Eusebiu Catana, Lutz Bannehr, Olaf Bastian, Erik Borg, Jan Bumberger, Peter Dietrich, Cornelia Glässer, Jorg M. Hacker, Rene Höfer (+25 others)
2022 Remote Sensing  
Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.  ...  RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented.  ...  T.W. wishes to thank the German Federal Environmental Foundation (Deutsche Bundesstiftung Umwelt (DBU) and for the CLEARING HOUSE (Collaborative Learning in Research, Information-sharing, and Governance  ... 
doi:10.3390/rs14092279 fatcat:5khv6g2q4rgtlp5pgym4hr3oum

Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review [article]

Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jocelyn Chanussot, Lucas Drumetz, Jean-Yves Tourneret, Alina Zare, Christian Jutten
2021 arXiv   pre-print
We also review methods used to construct spectral libraries (which are required by many SU techniques) based on the observed hyperspectral image, as well as algorithms for library augmentation and reduction  ...  This resulted in the development of algorithms that incorporate different strategies to allow the EMs to vary within a hyperspectral image, using, for instance, sets of spectral signatures known a priori  ...  In this context, a recent review article by James Theiler and his coworkers provides an excellent overview of spectral variability in hyperspectral target detection [14] .  ... 
arXiv:2001.07307v3 fatcat:6ambb6x2pzgoxott3jqt3hts2i
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