Abstract
In this study, we present a hyperspectral image classification method by combining spectral, spatial, and semantic information. The main steps of the proposed method are summarized as follows: First, principal component analysis transform is conducted on an original image to produce its extended morphological profile, Gabor features, and superpixel-based segmentation map. To model spatial information, the extended morphological profile and Gabor features are used to represent structure and texture features, respectively. Moreover, the mean filtering is performed within each superpixel to maintain the homogeneity of the spatial features. Then, the k-means clustering and the entropy rate superpixel segmentation are combined to produce semantic feature vectors by using a bag of visual-words model for each superpixel. Next, three kernel functions are constructed to describe the spectral, spatial, and semantic information, respectively. Finally, the composite kernel technique is used to fuse all the features into a multiple kernel function that is fed into a support vector machine classifier to produce a final classification map. Experiments demonstrate that the proposed method is superior to the most popular kernel-based classification methods in terms of both visual inspection and quantitative analysis, even if only very limited training samples are available.
Highlights
Hyperspectral images (HSIs) have been widely used for various applications, such as precision agriculture [1], anomalous target detection [2], and environmental monitoring [3]
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Speciffiicalllyy, the spectral kernel is defifined using spectral features, the spatial kernel is constructed by stacking the structure and texture information of each pixel, and the semantic kernel is developed by performing the bag of visual words (BOVW) algorithm within each superpixel of the image
Summary
Hyperspectral images (HSIs) have been widely used for various applications, such as precision agriculture [1], anomalous target detection [2], and environmental monitoring [3]. HSIs are often contaminated by different types of noise and is dominated by mixed pixels To overcome these problems, many pixel-wise classification methods have been proposed, such as using a support vector machine (SVM) [5,6] and multinomial logistic regression [7,8,9]. Many pixel-wise classification methods have been proposed, such as using a support vector machine (SVM) [5,6] and multinomial logistic regression [7,8,9] Such methods only consider the spectral characteristics of each pixel and ignores the spatial relationship between pixels, which can cause classification maps to have a lot of “salt-and-pepper” noise
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