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A Spectral Signature Shape-Based Algorithm for Landsat Image Classification

Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Icube (UMR7357), UdS, CNRS, 300 Bld Sébastien Brant, CS10413, Illkirch 67412, France
School of Resources and Environmental Science, Hubei University, Wuhan 430062, China
School of Electrical and Information, Zhejiang University of Media and Communications, Hangzhou 310018, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(9), 154;
Received: 18 May 2016 / Revised: 15 August 2016 / Accepted: 23 August 2016 / Published: 26 August 2016
(This article belongs to the Special Issue Recent Advances in Geodesy & Its Applications)
PDF [5973 KB, uploaded 26 August 2016]


Land-cover datasets are crucial for earth system modeling and human-nature interaction research at local, regional and global scales. They can be obtained from remotely sensed data using image classification methods. However, in processes of image classification, spectral values have received considerable attention for most classification methods, while the spectral curve shape has seldom been used because it is difficult to be quantified. This study presents a classification method based on the observation that the spectral curve is composed of segments and certain extreme values. The presented classification method quantifies the spectral curve shape and takes full use of the spectral shape differences among land covers to classify remotely sensed images. Using this method, classification maps from TM (Thematic mapper) data were obtained with an overall accuracy of 0.834 and 0.854 for two respective test areas. The approach presented in this paper, which differs from previous image classification methods that were mostly concerned with spectral “value” similarity characteristics, emphasizes the "shape" similarity characteristics of the spectral curve. Moreover, this study will be helpful for classification research on hyperspectral and multi-temporal images. View Full-Text
Keywords: image classification; spectral curve; shape; quantification image classification; spectral curve; shape; quantification

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Chen, Y.; Wang, Q.; Wang, Y.; Duan, S.-B.; Xu, M.; Li, Z.-L. A Spectral Signature Shape-Based Algorithm for Landsat Image Classification. ISPRS Int. J. Geo-Inf. 2016, 5, 154.

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