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Satellite Imagery Processing and Applications remote sensing Very High Resolution (VHR) Satellite Imagery Processing and Applications Edited by Francisco Eugenio and Javier Marcello Printed Edition of the Special Issue Published in Remote Sensing www.mdpi.com/journal/remotesensing Very High Resolution (VHR) Satellite Imagery Very High Resolution (VHR) Satellite Imagery Processing and Applications Special Issue Editors Francisco Eugenio Javier Marcello MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade Special Issue Editors Francisco Eugenio Javier Marcello University of Las Palmas of Gran Canaria University of Las Palmas of Gran Canaria (ULPGC) (ULPGC) Spain Spain Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Remote Sensing (ISSN 2072-4292) from 2018 to 2019 (available at: https://www.mdpi.com/journal/ remotesensing/special issues/VHR Satellite Imagery). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Article Number, Page Range. ISBN 978-3-03921-756-4 (Pbk) ISBN 978-3-03921-757-1 (PDF) Cover image courtesy of Francisco Eugenio and Javier Marcello. c 2019 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. Contents About the Special Issue Editors ..................................... vii Preface to ”Very High Resolution (VHR) Satellite Imagery” ..................... ix Kui Jiang, Zhongyuan Wang, Peng Yi, Junjun Jiang, Jing Xiao and Yuan Yao Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution Reprinted from: Remote Sensing 2018, 10, 1700, doi:10.3390/rs10111700 ............... 1 Yun Ren, Changren Zhu and Shunping Xiao Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images Reprinted from: Remote Sensing 2018, 10, 1470, doi:10.3390/rs10091470 ............... 24 Yao Yao and Shixin Wang Evaluating the Effects of Image Texture Analysis on Plastic Greenhouse Segments via Recognition of the OSI-USI-ETA-CEI Pattern Reprinted from: Remote Sensing 2019, 11, 231, doi:10.3390/rs11030231 ................ 37 Wei Zhang, Ping Tang and Lijun Zhao Remote Sensing Image Scene Classification Using CNN-CapsNet Reprinted from: Remote Sensing 2019, 11, 494, doi:10.3390/rs11050494 ................ 58 Melanie K. Vanderhoof and Clifton Burt Applying High-Resolution Imagery to Evaluate Restoration-Induced Changes in Stream Condition, Missouri River Headwaters Basin, Montana Reprinted from: Remote Sensing 2018, 10, 913, doi:10.3390/rs10060913 ................ 80 Javier Marcello, Francisco Eugenio, Javier Mart´ın and Ferran Marqu´es Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery Reprinted from: Remote Sensing 2018, 10, 1208, doi:10.3390/rs10081208 ...............108 Wei Wu, Qiangzi Li, Yuan Zhang, Xin Du and Hongyan Wang Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water Reprinted from: Remote Sensing 2018, 10, 1704, doi:10.3390/rs10111704 ...............129 George Marmorino and Wei Chen Use of WorldView-2 Along-Track Stereo Imagery to Probe a Baltic Sea Algal Spiral Reprinted from: Remote Sensing 2019, 11, 865, doi:10.3390/rs11070865 ................150 Livia Piermattei, Mauro Marty, Wilfried Karel, Camillo Ressl, Markus Hollaus, Christian Ginzler and Norbert Pfeifer Impact of the Acquisition Geometry of Very High-Resolution Pleiades´ Imagery on the Accuracy of Canopy Height Models over Forested Alpine Regions Reprinted from: Remote Sensing 2018, 10, 1542, doi:10.3390/rs10101542 ...............159 Donato Amitrano, Raffaella Guida, Domenico Dell’Aglio, Gerardo Di Martino, Diego Di Martire, Antonio Iodice, Mario Costantini, Fabio Malvarosa and Federico Minati Long-Term Satellite Monitoring of the Slumgullion Landslide Using Space-Borne Synthetic Aperture Radar Sub-Pixel Offset Tracking Reprinted from: Remote Sensing 2019, 11, 369, doi:10.3390/rs11030369 ................181 v Angel Garcia-Pedrero, Consuelo Gonzalo-Mart´ın, Mario Lillo-Saavedra and Dionisio Rodriguez-Esparragon The Outlining of Agricultural Plots Based on Spatiotemporal Consensus Segmentation Reprinted from: Remote Sensing 2018, 10, 1991, doi:10.3390/rs10121991 ...............194 Yongfa You, Siyuan Wang, Yuanxu Ma, Guangsheng Chen, Bin Wang, Ming Shen and Weihua Liu Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index Reprinted from: Remote Sensing 2018, 10, 1287, doi:10.3390/rs10081287 ...............207 Lipeng Gao, Wenzhong Shi, Zelang Miao and Zhiyong Lv Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images Reprinted from: Remote Sensing 2018, 10, 900, doi:10.3390/rs10060900 ................229 vi About the Special Issue Editors Francisco Eugenio received his B.S., M.S., and Ph.D. degrees in electrical engineering from the Universidad de Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain, in 1986, 1993, and 2000, respectively. In June 1996, he joined the Department of Signal and Communications, ULPGC. From 1998 to December 2000, he was with the Technical University of Catalonia (UPC), Barcelona, Spain, working in image processing. Since 2017, he has been a Full Professor with ULPGC, where he served as the Dean of the Telecommunication School in 2004–2010 and is currently lecturing on the area of remote sensing and radar. His current research interests at the Institute of Oceanography and Global Change (IOCAG, ULPGC), focuses on new methodologies and algorithms for multispectral and hyperspectral high-resolution remote sensing processing for the monitoring of shallow-water environments and fusion of multisensor/multiresolution satellite image data. In these areas, he is the author or coauthor of many publications that have been published in journals, and he has also been a reviewer for more than 15 publications. He is a Guest Editor for the Special Issue in Remote Sensing: Very High Resolution (VHR) Satellite Imagery: Processing and Applications. Javier Marcello received his M.S. degree in electrical engineering from the Technical University of Catalonia (UPC), Barcelona, Spain, in 1993 and the Ph.D. degree from the Universidad de Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain, in 2006. From 1992 to 2000, he was the Head Engineer at the Spanish Aerospace Defense Administration (Instituto Nacional de Tecnica´ Aeroespacial), where he served different programs at the Canary Space Center (Cospas-Sarsat, MINISAT, Helios, and CREPAD). In January 1994, he joined the Department of Signals and Communications, ULPGC, where he has been an Associate Professor in the Telecommunication School, lecturing on the areas of satellite and radio communications since 2000. His research is carried out at the Institute of Oceanography and Global Change (IOCAG, ULPGC) and includes multisensor remote sensing image processing (image fusion, classification, segmentation, etc.) and the generation of coastal and land products. He has authored 30 papers in remote sensing journals with medium-high impact factors. Additionally, he has been a reviewer in more than 20 remote sensing publications. He is a member of the Editorial Board of Remote Sensing and has also served as Guest Editor for the Special Issue in Remote Sensing: Very High Resolution (VHR) Satellite Imagery: Processing and Applications. Since 2016, he is the vice-president of the IEEE Geoscience and Remote Sensing Spanish Chapter. vii Preface to ”Very High Resolution (VHR) Satellite Imagery” Nowadays, optical sensors provide multispectral and panchromatic imagery at much finer spatial resolutions than in previous decades. Ikonos was the first commercial high-resolution satellite sensor. Launched on September 24, 1999, it broke the one meter mark. Since then, Quickbird, Geoeye, Pleiades, Kompsat, and many other very high resolution (VHR) satellites have been launched. Another important milestone was the 2009 launch of WorldView-2, the first VHR satellite to provide eight spectral channels in the visible to near-infrared range. On the other hand, very high-resolution SAR finally became available in 2007 with the launch of the Italian Cosmo-Skymed and German TerraSAR-X, both providing X band imagery at a 1-m resolution. Following these innovations, the recent advances in sensor technology and algorithm development have enabled the use of VHR remote sensing to quantitatively study the biophysical and biogeochemical processes in coastal and inland waters. Apart from bodies of water, VHR can be fundamental for the monitoring of complex land ecosystems for biodiversity conservation or precision agriculture for the management of soils, crops and pests. In this context, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost,
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