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- W2383857569 abstract "A hyperspectral image classification algorithm based on support vector machine(SVM) is presented.The hybrid features are made up of the spectral domain and spatial domain information of the image.In order to deal with high dimensional data of the hyperspectral image,a maximum noise fraction(MNF) extraction method is adopted.A virtual dimensionality(VD) estimation algorithm is used to determine the number of the principal components(PC) remained.The morphology profiles(MP) of the object are extracted from the remained PC images by using mathematical morphology method.The hybrid features for image classification are constructed by combining the spatial characteristics with spectral domain characteristics of the original image.Because of its high efficiency and small training samples,the SVM is adopted as the classifier.The superiors of the proposed method compared with the classifiers only using spectral or spatial information are shown by experiments." @default.
- W2383857569 created "2016-06-24" @default.
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- W2383857569 date "2011-01-01" @default.
- W2383857569 modified "2023-09-23" @default.
- W2383857569 title "Hyperspectral Image Classification Algorithm Based on Spectral-Spatial Hybrid Features and SVM" @default.
- W2383857569 hasPublicationYear "2011" @default.
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