{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T06:23:43Z","timestamp":1763274223491,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072360, 61902292, 62001357, 62072359, 62072355"],"award-info":[{"award-number":["62072360, 61902292, 62001357, 62072359, 62072355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image classification is essential for satellite Internet of Things (IoT) to build a large scale land-cover surveillance system. After acquiring real-time land-cover information, the edge of the network transmits all the hyperspectral images by satellites with low-latency and high-efficiency to the cloud computing center, which are provided by satellite IoT. A gigantic amount of remote sensing data bring challenges to the storage and processing capacity of traditional satellite systems. When hyperspectral images are used in annotation of land-cover application, data dimension reduction for classifier efficiency often leads to the decrease of classifier accuracy, especially the region to be annotated consists of natural landform and artificial structure. This paper proposes encoding spectral-spatial features for hyperspectral image classification in the satellite Internet of Things system to extract features effectively, namely attribute profile stacked autoencoder (AP-SAE). Firstly, extended morphology attribute profiles EMAP is used to obtain spatial features of different attribute scales. Secondly, AP-SAE is used to extract spectral features with similar spatial attributes. In this stage the program can learn feature mappings, on which the pixels from the same land-cover class are mapped as closely as possible and the pixels from different land-cover categories are separated by a large margin. Finally, the program trains an effective classifier by using the network of the AP-SAE. Experimental results on three widely-used hyperspectral image (HSI) datasets and comprehensive comparisons with existing methods demonstrate that our proposed method can be used effectively in hyperspectral image classification.<\/jats:p>","DOI":"10.3390\/rs13183561","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T02:41:07Z","timestamp":1631068867000},"page":"3561","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Encoding Spectral-Spatial Features for Hyperspectral Image Classification in the Satellite Internet of Things System"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4091-714X","authenticated-orcid":false,"given":"Ning","family":"Lv","sequence":"first","affiliation":[{"name":"The School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7710-6079","authenticated-orcid":false,"given":"Zhen","family":"Han","sequence":"additional","affiliation":[{"name":"The School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4971-5029","authenticated-orcid":false,"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7535-6464","authenticated-orcid":false,"given":"Yijia","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1529-4198","authenticated-orcid":false,"given":"Tao","family":"Su","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing of School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5981-5683","authenticated-orcid":false,"given":"Sotirios","family":"Goudos","sequence":"additional","affiliation":[{"name":"Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1109\/TITS.2020.3025687","article-title":"An Edge Traffic Flow Detection Scheme Based on Deep Learning in An Intelligent Transportation System","volume":"22","author":"Chen","year":"2021","journal-title":"IEEE Trans. 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