Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringHyperspectral Image SegmentationImage SegmentationSegmentationSemantic SegmentationSuperpixelsSimilar Papers 제목 키워드 기반
Image Processing via Multilayer Graph Spectra
Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures. Many real-life multimedia datasets, however, exhibit heterogeneous structures ac…
Edge DetectionHyperspectral Image SegmentationImage CompressionImage Segmentation+1Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis
Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspect…
Hyperspectral image analysisSuperpixelsConditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation
Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, convolutional neural network (CNN) has established itself as a powerful model in segmentation a…
Hyperspectral Image SegmentationImage SegmentationSegmentationSemantic SegmentationHyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE)
Hyperspectral imaging, a rapidly evolving field, has witnessed the ascendancy of deep learning techniques, supplanting classical feature extraction and classification methods in various applications. However, many resear…
Hyperspectral Image SegmentationImage SegmentationSegmentationSemantic SegmentationEvaluation of Dirichlet Process Gaussian Mixtures for Segmentation on Noisy Hyperspectral Images
Image segmentation is a fundamental step for the interpretation of Remote Sensing Images. Clustering or segmentation methods usually precede the classification task and are used as support tools for manual labeling. The …
Image SegmentationSegmentationSemantic Segmentation