Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks

• URL: http://arxiv.org/abs/2111.15018v1
• Date: Mon, 29 Nov 2021 23:28:18 GMT
• Title: Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks
• Authors: Songyang Zhang, Qinwen Deng, and Zhi Ding
• Abstract summary: One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features. We propose several approaches to HSI segmentation based on M-GSP feature extraction. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral-spatial information extraction.
• Score: 51.15952040322895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral imaging is an important sensing technology with broad applications and impact in areas including environmental science, weather, and geo/space exploration. One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features. Leveraging on the recent-developed graph signal processing over multilayer networks (M-GSP), this work proposes several approaches to HSI segmentation based on M-GSP feature extraction. To capture joint spectral-spatial information, we first customize a tensor-based multilayer network (MLN) model for HSI, and define a MLN singular space for feature extraction. We then develop an unsupervised HSI segmentation method by utilizing MLN spectral clustering. Regrouping HSI pixels via MLN-based clustering, we further propose a semi-supervised HSI classification based on multi-resolution fusions of superpixels. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral-spatial information extraction.

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