Hyperspectral image change detection based on intrinsic image decomposition feature extraction

Kecheng Du; Sicong Liu

doi:10.1117/12.2603753

12 September 2021 Hyperspectral image change detection based on intrinsic image decomposition feature extraction

Kecheng Du, Sicong Liu

Proceedings Volume 11862, Image and Signal Processing for Remote Sensing XXVII; 1186215 (2021) https://doi.org/10.1117/12.2603753
Event: SPIE Remote Sensing, 2021, Online Only

Conference Poster

Abstract

Hyperspectral images (HSIs) provides abundant spectral information through hundreds of bands with continuous spectral information that can be used in land cover fine change detection (CD). HSIs make it possible for hyperspectral CD performance with higher discrimination on changes but provides a challenge to the conventional CD techniques due to its high dimensionality and dense spectral representation. In this paper, we implemented intrinsic image decomposition (IID) model to decompose the hyperspectral temporal difference image into two parts: real change and pseudo change information. In particular, the spectral reflecting component is selected as a kind of pure spectral feature used to enhance the CD performance in multitemporal HSIs. Experimental results illustrate the effectiveness of IID features extraction in addressing a supervised CD task.

Conference Presentation

Citation Download Citation

Kecheng Du and Sicong Liu "Hyperspectral image change detection based on intrinsic image decomposition feature extraction", Proc. SPIE 11862, Image and Signal Processing for Remote Sensing XXVII, 1186215 (12 September 2021); https://doi.org/10.1117/12.2603753

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