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Indexed by:期刊论文

Date of Publication:2019-04-03

Journal:REMOTE SENSING LETTERS

Included Journals:SCIE、Scopus

Volume:10

Issue:4

Page Number:343-352

ISSN No.:2150-704X

Key Words:Fuzzy sets; Pixels; Remote sensing; Support vector machines, Accuracy assessment; Normalized difference vegetation index; Remote sensing images; SVM classifiers; Training sample; Variable fuzzy set; Winter wheat; Winter wheat areas, Crops, Triticum aestivum

Abstract:This paper proposed a method of extracting the winter wheat area by combining support vector machine (SVM) with variable fuzzy sets. This method mainly aims to deal with mixed pixels in remote sensing images. The SVM classifier can accurately identify pure winter wheat pixels with training samples. However, winter wheat area information in mixed pixels cannot be directly obtained because they contain multiple types of features. In order to estimate the winter wheat area information of mixed pixels in Landsat8 data, this paper introduced the normalized difference vegetation index (NDVI) and the variable fuzzy set method. Finally, this paper reasonably estimated the area of winter wheat from the mixed pixels in Landsat8 images. The accuracy assessment showed that the proposed method could extract winter wheat area information more accurately.