Abstract
In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method based on the fusion of two spatial information, which will be classified by a large margin distribution machine (LDM). First, the spatial texture information is extracted from the top of the principal component analysis for hyperspectral images by a curvature filter (CF). Second, the spatial correlation information of a hyperspectral image is completed by using domain transform recursive filter (DTRF). Last, the spatial texture information and correlation information are fused to be classified with LDM. The experimental results of hyperspectral images classification demonstrate that the proposed curvature filter and domain transform recursive filter with LDM(CFDTRF-LDM) method is superior to other classification methods.
Highlights
Hyperspectral images (HSI), which provide valuable spectral information, have been widely used in remote-sensing applications [1,2,3,4,5,6]
A hyperspectral classification method was proposed based on sparse representation classification spatial features, which were extracted by joint bilateral filter (BF) with the first principal component as the guidance image in the literature [37]
Tu et al proposed an HSI classification method o based on non-local means filtering with maximum probability and Support Vector Machine (SVM), which uses the spatial context information and non-local means filtering in the first principal component to obtain the optimization probability image of spatial structure [45]
Summary
Hyperspectral images (HSI), which provide valuable spectral information, have been widely used in remote-sensing applications [1,2,3,4,5,6]. Liao et al applied the morphological profile filter and domain transform normalized convolution Filter (DTNCF) to extract the spatial information [25], which was combined and fed into support vector machine (SVM), and implemented two-step optimization in the classification process [26]. A hyperspectral classification method was proposed based on sparse representation classification spatial features, which were extracted by joint BF with the first principal component as the guidance image in the literature [37]. Tu et al proposed an HSI classification method o based on non-local means filtering with maximum probability and SVM, which uses the spatial context information and non-local means filtering in the first principal component to obtain the optimization probability image of spatial structure [45].
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