# A spectral-spatial fusion anomaly detection method for hyperspectral imagery

arXiv: https://arxiv.org/abs/2202.11889 · HTML: https://mlsanity.com/paper/2202.11889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery](https://mlsanity.com/paper/1711.08970/) (2017)
- [A Joint Morphological Profiles and Patch Tensor Change Detection for Hyperspectral Imagery](https://mlsanity.com/paper/2201.08027/) (2022)

## Built on by (most cited first, 1 of 1)

- [Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study](https://mlsanity.com/paper/2507.05730/) (2025)

## Abstract (arXiv)

In hyperspectral, high-quality spectral signals convey subtle spectral differences to distinguish similar materials, thereby providing unique advantage for anomaly detection. Hence fine spectra of anomalous pixels can be effectively screened out from heterogeneous background pixels. Since the same materials have similar characteristics in spatial and spectral dimension, detection performance can be significantly enhanced by jointing spatial and spectral information. In this paper, a spectralspatial fusion anomaly detection (SSFAD) method is proposed for hyperspectral imagery. First, original spectral signals are mapped to a local linear background space composed of median and mean with high confidence, where saliency weight and feature enhancement strategies are implemented to obtain an initial detection map in spectral domain. Futhermore, to make full use of similarity information of local background around testing pixel, a new detector is designed to extract the local similarity spatial features of patch images in spatial domain. Finally, anomalies are detected by adaptively combining the spectral and spatial detection maps. The experimental results demonstrate that our proposed method has superior detection performance than traditional methods.