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Hyperspectral intrinsic image decomposition based on local sparseness
Author(s): Zhiwei Ren; Lingda Wu
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Paper 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.

Paper Details

Date Published: 12 March 2019
PDF: 8 pages
Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 110232B (12 March 2019); doi: 10.1117/12.2516879
Show Author Affiliations
Zhiwei Ren, Space Engineering Univ. (China)
Lingda Wu, Space Engineering Univ. (China)


Published in SPIE Proceedings Vol. 11023:
Fifth Symposium on Novel Optoelectronic Detection Technology and Application
Qifeng Yu; Wei Huang; You He, Editor(s)

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