Self-Supervised Hyperspectral Anomaly Detection Based on Finite Spatialwise Attention

Zhipeng WANG, Dan MA, Guanghui YUE, Beichen LI*, Runmin CONG, Zhiqiang WU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Hyperspectral anomaly detection (HAD) is of great value in both practical and theoretical terms. However, due to the lack of available semantic labels, previous works mainly relied on unsupervised or semisupervised methods to construct learning models, which inevitably lacked semantic guidance and led to limited AD effectiveness. Besides, few previous methods jointly mine spectral and spatial global dependencies, which limits their effectiveness in practical scenarios. To address the above-mentioned problems, we design a novel self-supervised HAD method, named the Self-Supervised HAD method based on the finite spatialwise attention (FSA). The core of the proposed method is the designed self-supervised HAD transFormer (SSHADFormer). It explores the specific spectral attributes of hyperspectral images (HSIs) to reconstruct background HSI from a given RGB image, which solves the difficulty of acquiring semantic information and enhances the agility of AD models. In addition, we propose an FSA mechanism. The mechanism mines the cluster structure of the background spectrum in a data-driven manner, enhancing the discriminative ability between background and anomalous targets while avoiding anomalous target interference during training. Extensive experiments on six public datasets demonstrate the effectiveness and agility of the proposed method.

Original languageEnglish
Article number5502918
Pages (from-to)1-18
Number of pages18
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume62
Early online date25 Dec 2023
DOIs
Publication statusPublished - 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1980-2012 IEEE.

Keywords

  • Hyperspectral anomaly detection (HAD)
  • self-attention mechanism
  • self-supervised learning
  • spatial cluster
  • transformer

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