Hyperspectral intrinsic image decomposition based on local sparseness

Zhiwei Ren; Lingda Wu

doi:10.1117/12.2516879

12 March 2019 Hyperspectral intrinsic image decomposition based on local sparseness

Zhiwei Ren, Lingda Wu

Proceedings Volume 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application; 110232B (2019) https://doi.org/10.1117/12.2516879
Event: Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 2018, Xi'an, China

Abstract

Due to the influence of conditions such as sensor status, imaging mechanism, climate and light, hyperspectral remote sensing images have serious distortion, which seriously affects the classification accuracy of hyperspectral remote sensing images. In this paper, the intrinsic image decomposition technology, which is widely used in computer vision and graphics, is introduced into hyperspectral image processing to perform intrinsic image decomposition on the original image. A hyperspectral intrinsic image decomposition method based on local sparseness is proposed. The automatic subspace partition and sparse representation theory are used to decompose the original hyperspectral image. The reflectance intrinsic image obtained by the decomposition is subjected to hyperspectral image classification processing. The experimental results show that the method proposed in this paper can obtain the intrinsic images better, and improve the classification accuracy of hyperspectral images to a large extent.

Citation Download Citation

Zhiwei Ren and Lingda Wu "Hyperspectral intrinsic image decomposition based on local sparseness", Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 110232B (12 March 2019); https://doi.org/10.1117/12.2516879

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