A novel graph convolution and frequency domain filtering approach for hyperspectral anomaly detection

A novel graph convolution and frequency domain filtering approach for hyperspectral anomaly detection

Abstract This paper introduces a novel algorithm for hyperspectral anomaly detection (HAD) that combines graph-based representations with frequency domain filtering techniques. In this approach, hyperspectral images (HSIs) are modeled as graphs, where each pixel is treated as a node with spectral fe...

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Bibliographic Details
Main Authors: Yang Ding, Hao Yan, Jingyuan He, Juanjuan Yin, A. Ruhan
Format: Article
Language:English
Published: Springer 2025-01-01
Series:Complex & Intelligent Systems
Subjects:
Hyperspectral anomaly detection (HAD)
Graph neural networks (GNN)
Graph convolutional networks(GCN)
Right-shift phenomenon
Beta wavelet
Online Access:https://doi.org/10.1007/s40747-024-01738-z
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https://doi.org/10.1007/s40747-024-01738-z

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