Hyperspectral image anomaly detection based on auto encoder

Guanzhe Li; Lingda Wu; Hongxing Hao

doi:10.1117/12.2668415

23 February 2023 Hyperspectral image anomaly detection based on auto encoder

Guanzhe Li, Lingda Wu, Hongxing Hao

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 1255111 (2023) https://doi.org/10.1117/12.2668415
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

The reason for the existence of small targets and sub-pixel targets in hyperspectral images is that the low spatial resolution leads to the phenomenon of mixed pixels, which makes some targets exist in the form of small targets and sub-pixel targets in the image. Moreover, the traditional anomaly detection algorithm introduces the regularization parameter into hyperspectral anomaly detection, but the regularization parameter needs to be manually set and adjusted. However, since the anomaly detection is carried out without prior information, in real situations, it is difficult to obtain the prior information of the target, so the regularization parameter may be difficult to determine. In addition, considering that traditional algorithms are not sensitive to small target pixel targets, a hyperspectral anomaly detection network based on self encoder is designed for hyperspectral images with small targets and sub-pixel targets.

Citation Download Citation

Guanzhe Li, Lingda Wu, and Hongxing Hao "Hyperspectral image anomaly detection based on auto encoder", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 1255111 (23 February 2023); https://doi.org/10.1117/12.2668415

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KEYWORDS

Computer programming

Hyperspectral imaging

Detection and tracking algorithms

Image processing

Target detection

Convolution

Image restoration

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