{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:20:14Z","timestamp":1760235614103,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund","award":["Grant RTI2018-095287-B-I00 and by the Catalan Government under Grant 2017SGR-463"],"award-info":[{"award-number":["Grant RTI2018-095287-B-I00 and by the Catalan Government under Grant 2017SGR-463"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and\/or to customers. Due to different limitations, compression has to be applied on-board and\/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.<\/jats:p>","DOI":"10.3390\/rs13183727","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T03:47:35Z","timestamp":1632282455000},"page":"3727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Editorial to Special Issue \u201cRemote Sensing Data Compression\u201d"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1920-2847","authenticated-orcid":false,"given":"Benoit","family":"Vozel","sequence":"first","affiliation":[{"name":"Engineering School of Applied Sciences and Technology, University of Rennes 1, 22305 Lannion, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir","family":"Lukin","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4729-9292","authenticated-orcid":false,"given":"Joan","family":"Serra-Sagrist\u00e0","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, Universitat Aut\u00f2noma de Barcelona, Cerdanyola del Valles, 08290 Barcelona, Catalonia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chow, K., Tzamarias, D.E.O., Hern\u00e1ndez-Cabronero, M., Blanes, I., and Serra-Sagrist\u00e0, J. (2020). Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure. Remote Sens., 12.","DOI":"10.3390\/rs12121983"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chow, K., Tzamarias, D.E.O., Blanes, I., and Serra-Sagrist\u00e0, J. (2019). Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression. Remote Sens., 11.","DOI":"10.3390\/rs11212461"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tzamarias, D.E.O., Chow, K., Blanes, I., and Serra-Sagrist\u00e0, J. (2019). Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms. Remote Sens., 11.","DOI":"10.3390\/rs11192290"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Blanes, I., Kiely, A., Hern\u00e1ndez-Cabronero, M., and Serra-Sagrist\u00e0, J. (2019). Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard. Remote Sens., 11.","DOI":"10.3390\/rs11111390"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Cabronero, M., Portell, J., Blanes, I., and Serra-Sagrist\u00e0, J. (2020). High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation. Remote Sens., 12.","DOI":"10.3390\/rs12182955"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lukin, V., Vasilyeva, I., Krivenko, S., Li, F., Abramov, S., Rubel, O., Vozel, B., Chehdi, K., and Egiazarian, K. (2020). Lossy Compression of Multichannel Remote Sensing Images with Quality Control. Remote Sens., 12.","DOI":"10.3390\/rs12223840"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Radosavljevi\u0107, M., Brklja\u010d, B., Lugonja, P., Crnojevi\u0107, V., Trpovski, \u017d., Xiong, Z., and Vukobratovi\u0107, D. (2020). Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study. Remote Sens., 12.","DOI":"10.3390\/rs12101590"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, J., Torres, D., Santos, S., and Atzberger, C. (2020). Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks. Remote Sens., 12.","DOI":"10.3390\/rs12030517"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Meli\u00e1n, J.M., Jim\u00e9nez, A., D\u00edaz, M., Morales, A., Horstrand, P., Guerra, R., L\u00f3pez, S., and L\u00f3pez, J.F. (2021). Real-Time Hyperspectral Data Transmission for UAV-Based Acquisition Platforms. Remote Sens., 13.","DOI":"10.3390\/rs13050850"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Caba, J., D\u00edaz, M., Barba, J., Guerra, R., and L\u00f3pez, J.A. (2020). FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations. Remote Sens., 12.","DOI":"10.3390\/rs12223741"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alves de Oliveira, V., Chabert, M., Oberlin, T., Poulliat, C., Bruno, M., Latry, C., Carlavan, M., Henrot, S., Falzon, F., and Camarero, R. (2021). Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression. Remote Sens., 13.","DOI":"10.3390\/rs13030447"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kong, F., Hu, K., Li, Y., Li, D., and Zhao, S. (2021). Spectral\u2013Spatial Feature Partitioned Extraction Based on CNN for Multispectral Image Compression. Remote Sens., 13.","DOI":"10.3390\/rs13010009"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"B\u00e1scones, D., Gonz\u00e1lez, C., and Mozos, D. (2020). An FPGA Accelerator for Real-Time Lossy Compression of Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12162563"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Choi, H.-M., Yang, H.-S., and Seong, W.-J. (2021). Compressive Underwater Sonar Imaging with Synthetic Aperture Processing. Remote Sens., 13.","DOI":"10.3390\/rs13101924"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dai, S., Liu, W., Wang, Z., and Li, K. (2021). A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing. 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