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Editorial for Special Issue "Hyperspectral Imaging and Applications"

Chein-I Chang, Meiping Song, Junping Zhang, Chao-Cheng Wu
2019 Remote Sensing  
, Band Selection, Data Fusion, Applications.  ...  This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification  ...  detector for hyperspectral data which can improve the detection probability of anomaly presence in signals using the integration of information gathered during transition of sliding window for each pixel  ... 
doi:10.3390/rs11172012 fatcat:c23u3rahgjhctowk5xwllt2qea

Table of contents

2020 IEEE Transactions on Geoscience and Remote Sensing  
Bi 6197 Hyperspectral Data Subpixel-Pixel-Superpixel Guided Fusion for Hyperspectral Anomaly Detection ....... Z. Huang, L. Fang, and S.  ...  Wang 6550 Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery ....................... ..........................................................................  ... 
doi:10.1109/tgrs.2020.3006605 fatcat:g45mqghjmjenlfd2ydw7nzbcju

Table of contents

2019 IEEE Transactions on Geoscience and Remote Sensing  
Chanussot 4775 Structure Tensor and Guided Filtering-Based Algorithm for Hyperspectral Anomaly Detection .......................... .....................................................................  ...  Fernández-Prieto 4259 Hyperspectral Data PSASL: Pixel-Level and Superpixel-Level Aware Subspace Learning for Hyperspectral Image Classification ......... ...............................................  ... 
doi:10.1109/tgrs.2019.2923179 fatcat:nfaahnqzcvft5ezz36a6nyy2ri

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  
, and Anomaly Detection.  ...  The challenges and future research directions for the implementation of superpixel algorithms are also discussed. Index Terms-Hyperspectral Image, Superpixel Segmentation, Evaluation.  ...  17] , denoising [18] , and anomaly detection [19] in hyperspectral images.  ... 
doi:10.1109/jstars.2021.3076005 fatcat:smfb6jeox5eldbv6ys7ioeoko4

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary Region of Interest; TGRS Oct. 2019 7995-8010 Hu, T., see Kang, Z., TGRS Jan. 2019 181-193 Hu, T.,  ...  ., +, TGRS Oct. 2019 8235-8254 Structure Tensor and Guided Filtering-Based Algorithm for Hyperspectral Anomaly Detection.  ...  ., TGRS April 2019 2057-2074 Structure Tensor and Guided Filtering-Based Algorithm for Hyperspectral Anomaly Detection.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 4869-4880 Hyperspectral Anomaly Detection Via Dual Collaborative Representation.  ...  ., +, JSTARS 2020 2226-2239 Hyperspectral Anomaly Detection Via Dual Collaborative Representation.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

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

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment  ...  Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  A superpixel guided deep spatial-spectral sparse code learning method was published in [100] .  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

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

2015 IEEE Transactions on Geoscience and Remote Sensing  
., +, TGRS May 2015 2384-2396 Collaborative Representation for Hyperspectral Anomaly Detection.  ...  ., +, TGRS May 2015 2384-2396 Collaborative Representation for Hyperspectral Anomaly Detection.  ... 
doi:10.1109/tgrs.2015.2513444 fatcat:zuklkpk4gjdxjegoym5oagotzq

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Ayesha Shafique, Guo Cao, Zia Khan, Muhammad Asad, Muhammad Aslam
2022 Remote Sensing  
Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR  ...  Overall, this review will be beneficial for the future development of CD methods.  ...  The constructed anomaly detection model reconstructs the input from its representation in the latent space to identify pixels of new unseen image pairs.  ... 
doi:10.3390/rs14040871 fatcat:myyprcrcyzh6fhjz5ggqdc5e54

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

John E. Ball, Derek T. Anderson, Chee Seng Chan
2017 Journal of Applied Remote Sensing  
., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL.  ...  We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community.  ...  Acknowledgments The authors wish to thank graduate students Vivi Wei, Julie White, and Charlie Veal for their valuable inputs related to DL tools.  ... 
doi:10.1117/1.jrs.11.042609 fatcat:tdbssxma3fettcjy5iqgo6afwa

Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics

Dyah R. Panuju, David J. Paull, Amy L. Griffin
2020 Remote Sensing  
Advantages, limitations, challenges, and opportunities are identified for understanding the context of improvements, and this will guide the future development of bitemporal and multitemporal CD methods  ...  Within the fields of remote sensing and image processing, land surface change detection (CD) has been amongst the most discussed topics.  ...  Special thanks to Bambang Trisasongko for the discussion. We are grateful to three anonymous reviewers for their constructive comments leading to an improved manuscript.  ... 
doi:10.3390/rs12111781 fatcat:gyxuwlxzwbhzjez5lqg44f7h4i

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Debaleena Datta, Pradeep Kumar Mallick, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Jana Shafi, Jaeyoung Choi
2022
Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.  ...  Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved.  ...  Acknowledgments Jana Shafi would like to thank the Deanship of Scientific Research, Prince Sattam bin Abdul Aziz University, for supporting this work. is work was supported by the National Research Foundation  ... 
doi:10.1155/2022/3854635 pmid:35528334 pmcid:PMC9071975 fatcat:ptzpguwpczc7vjrcy4ywhx6t3u

Hyperspectral Image Unmixing Incorporating Adjacency Information

Sebastian Bauer
2018
This allows for faster and more accurate decomposition results.  ...  Many pixel spectra are mixtures of pure materials' spectra and therefore need to be decomposed into their constituents.  ...  Target detection can be subdivided into anomaly detection and signature-based target detection.  ... 
doi:10.5445/ksp/1000081665 fatcat:pxcroxkqwfbnlaeiv2g2pbufl4