Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares
نویسندگان
چکیده
منابع مشابه
Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares
As a widely used classifier, sparse representation classification (SRC) has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other no...
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Fully Constrained Least Squares (FCLS) has been widely used and proven to be a powerful tool for hyperspectral image classification. But for multispectral remote sensing images with only a few bands, the Least-Squares based approaches will all encounter the band number constraint (BNC), which requires the number of bands should be no less than the number of classes. In this paper, we proposed a...
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ژورنال
عنوان ژورنال: ISPRS International Journal of Geo-Information
سال: 2017
ISSN: 2220-9964
DOI: 10.3390/ijgi6110344