Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks

TL;DR

This paper introduces a novel hyperspectral image segmentation method leveraging graph signal processing over multilayer networks to effectively extract spectral-spatial features, improving segmentation accuracy.

Contribution

It proposes a tensor-based multilayer network model and MLN spectral clustering for unsupervised segmentation, along with a semi-supervised classification approach using multi-resolution superpixels.

Findings

Demonstrates the effectiveness of M-GSP in hyperspectral image processing.

Shows improved spectral-spatial feature extraction.

Validates methods through experimental results.

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.