Abstract
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.
Highlights
Land cover and its dynamics play a major role in the analysis and evaluation of the land surface processes that impact environmental, social, and economic components of sustainability [1,2]
The spectral curve shape of one surface cover type is usually different from other covers
A remotely sensed classification method that fully utilizes the spectral shape was developed by parameterizing the spectral shape
Summary
Land cover and its dynamics play a major role in the analysis and evaluation of the land surface processes that impact environmental, social, and economic components of sustainability [1,2]. Accurate and up-to-date land cover information is necessary. Such information can be acquired by use of remotely sensed image classification techniques [1]. Remotely sensed image classification is of increasing interest in the present development of digital image analysis [3]. Over the last several decades, a considerable number of classification approaches have been developed for classification of remotely sensed data. Many advanced classification methods have been presented in the past two decades, such as artificial neural networks (ANN) [6,7], support vector machine (SVM) [8,9], and decision tree classifiers [10,11]. Object-based image analysis (OBIA) [12,13,14], which is different from the pixel-based classifiers, has been reported to be effective for the problem of environmental heterogeneity
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