Abstract
Hyperspectral images have been widely applied to various fields due to the high spectral and spatial resolution. However, the vast amounts of spectral and spatial information also bring difficulties in hyperspectral image processing, where the efficiency is one of the biggest challenges. To address this challenge, we propose a method to extract the spectral-spatial 3-dimensional homogeneous regions (SS3DHRs) from hyperspectral images. First, highly correlated neighbor spectral bands are selected based on the correlation coefficients between adjacent bands; Based on the sub-band selection, a superpixel segmentation method is improved for hyperspectral images to gather the spatial information; Combining the spectral sub-bands and spatial superpixels, the SS3DHRs are collected from the 3-deminsion hyperspectral data cube. The SS3DHR can be processed as a unit for the subsequent applications, which may significantly reduce the redundant data and thus raise the efficiency. In experiment part, the extracted SS3DHRs are applied for hyperspectral image classification, where the experimental results demonstrate the effectiveness and efficiency of the proposed method.
This work was in part supported by the National Nature Science Foundation of China under Grant no. 61801222, and in part supported by the Fundamental Research Funds for the Central Universities under Grant no. 30919011230, and in part supported by the JiangSu Undergraduate Training Program for Innovation and Entrepreneurship under Item no. 20190288126Y.
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Cai, Y., Geng, Z., Liang, Y., Fu, P. (2020). Extraction of Spectral-Spatial 3-Dimensional Homogeneous Regions from Hyperspectral Images and Its Application to Fast Classification. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_17
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DOI: https://doi.org/10.1007/978-3-030-60639-8_17
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