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Article

Continuous Multi-Angle Remote Sensing and Its Application in Urban Land Cover Classification

1
National Engineering Research Center for Multimedia Software, Computer School & State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Institute of Future Cities, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
3
Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
4
School of Mathematics and Statistics & National Engineering Laboratory for Algorithm and Analysis Technologiy on Big Data, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jochen Hack
Remote Sens. 2021, 13(3), 413; https://doi.org/10.3390/rs13030413
Received: 22 December 2020 / Revised: 14 January 2021 / Accepted: 20 January 2021 / Published: 25 January 2021
(This article belongs to the Section Remote Sensing Image Processing)
Because of the limitations of hardware devices, such as the sensors, processing capacity, and high accuracy altitude control equipment, traditional optical remote sensing (RS) imageries capture information regarding the same scene from mostly one single angle or a very small number of angles. Nowadays, with video satellites coming into service, obtaining imageries of the same scene from a more-or-less continuous array of angles has become a reality. In this paper, we analyze the differences between the traditional RS data and continuous multi-angle remote sensing (CMARS) data, and unravel the characteristics of the CMARS data. We study the advantages of using CMARS data for classification and try to capitalize on the complementarity of multi-angle information and, at the same time, to reduce the embedded redundancy. Our arguments are substantiated by real-life experiments on the employment of CMARS data in order to classify urban land covers while using a support vector machine (SVM) classifier. They show the superiority of CMARS data over the traditional data for classification. The overall accuracy may increase up to about 9% with CMARS data. Furthermore, we investigate the advantages and disadvantages of directly using the CMARS data, and how such data can be better utilized through the extraction of key features that characterize the variations of spectral reflectance along the entire angular array. This research lay the foundation for the use of CMARS data in future research and applications. View Full-Text
Keywords: continuous multi-angle; remote sensing; earth observation; land cover classification; video satellite continuous multi-angle; remote sensing; earth observation; land cover classification; video satellite
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MDPI and ACS Style

Yao, Y.; Leung, Y.; Fung, T.; Shao, Z.; Lu, J.; Meng, D.; Ying, H.; Zhou, Y. Continuous Multi-Angle Remote Sensing and Its Application in Urban Land Cover Classification. Remote Sens. 2021, 13, 413. https://doi.org/10.3390/rs13030413

AMA Style

Yao Y, Leung Y, Fung T, Shao Z, Lu J, Meng D, Ying H, Zhou Y. Continuous Multi-Angle Remote Sensing and Its Application in Urban Land Cover Classification. Remote Sensing. 2021; 13(3):413. https://doi.org/10.3390/rs13030413

Chicago/Turabian Style

Yao, Yuan, Yee Leung, Tung Fung, Zhenfeng Shao, Jie Lu, Deyu Meng, Hanchi Ying, and Yu Zhou. 2021. "Continuous Multi-Angle Remote Sensing and Its Application in Urban Land Cover Classification" Remote Sensing 13, no. 3: 413. https://doi.org/10.3390/rs13030413

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