Abstract
Hyperspectral image (HSI) classification is one of the most active topics in remote sensing. However, it is still a nontrivial task to classify the hyperspectral data accurately, since HSI always suffers from a large number of noise pixels, the complexity of the spatial structure of objects and the spectral similarity between different objects. In this study, an effective classification scheme for hyperspectral image based on superpixel and discontinuity preserving relaxation (DPR) is proposed to discriminate land covers of interest. A novel technique for measuring the similarity of a pair of pixels in HSI is suggested to improve the simple linear iterative clustering (SLIC) algorithm. Unlike the existing application of SLIC technique to HSI, the improved SLIC algorithm can be directly used to segment HSI into superpixels without using principal component analysis in advance, and is free of parameters. Furthermore, the proposed three-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Compared with the existing two-step classification framework, the use of DPR technology in preprocessing significantly improves the classification accuracy. The effectiveness of the proposed method is verified on three public real hyperspectral datasets. The comparison results of several competitive methods show the superiority of this scheme.
Highlights
A hyperspectral image (HSI) is acquired by hyperspectral remote sensors and composed of hundreds of bands over the same spatial area
Based on the improved simple linear iterative clustering (SLIC) algorithm and discontinuity preserving relaxation (DPR) strategy, this study develops an effective semi-supervised HSI classification scheme
A technique for measuring the similarity of two pixels in HSI is proposed to address the problem of dividing HSI into superpixels directly by using the SLIC algorithm without using a principal component analysis (PCA) method
Summary
A hyperspectral image (HSI) is acquired by hyperspectral remote sensors and composed of hundreds of bands over the same spatial area. Superpixel segmentation methods have been extended to HSI classification [42,43,44,45,46,47,48,49,50], aiming at making full use of spectral information and spatial structure in hyperspectral data. By the combination of different segmentation techniques with various classification methods, a number of approaches for HSI classification have been developed, such as ER with sparse representation [42,51,52,53], SVM [54] or extreme learning machines [55], SLIC with multi-morphological method [56], SVM [57] or convolutional neural network [58] and so on. Where (pi, qi) is the spatial coordinate of the pixel xi
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