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Remote Sens. 2017, 9(11), 1145; doi:10.3390/rs9111145

A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
College of Computer Science and Software Engineering, Computer Vision Research Institute, Shenzhen University, Shenzhen 518060, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4
Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1900-118 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Received: 8 September 2017 / Revised: 22 October 2017 / Accepted: 31 October 2017 / Published: 8 November 2017
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Abstract

Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs). The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far beyond that offered by multispectral images. However, HSIs are inevitably corrupted by noise during acquisition and transmission processes. The presence of noise may significantly degrade the quality of the extracted mineral information. In order to improve the accuracy of mineral mapping, denoising is a crucial pre-processing task. By leveraging on low-rank and self-similarity properties of HSIs, this paper proposes a state-of-the-art HSI denoising algorithm that implements two main steps: (1) signal subspace learning via fine-tuned Robust Principle Component Analysis (RPCA); and (2) denoising the images associated with the representation coefficients, with respect to an orthogonal subspace basis, using BM3D, a self-similarity based state-of-the-art denoising algorithm. Accordingly, the proposed algorithm is named Hyperspectral Denoising via Robust principle component analysis and Self-similarity (HyDRoS), which can be considered as a supervised version of FastHyDe. The effectiveness of HyDRoS is evaluated in a series of mineral mapping experiments using noise-reduced AVIRIS and Hyperion HSIs. In these experiments, the proposed denoiser yielded systematically state-of-the-art performance. View Full-Text
Keywords: hyperspectral image; denoising; low-rank representation; self-similarity; mineral mapping hyperspectral image; denoising; low-rank representation; self-similarity; mineral mapping
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gao, L.; Yao, D.; Li, Q.; Zhuang, L.; Zhang, B.; Bioucas-Dias, J.M. A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping. Remote Sens. 2017, 9, 1145.

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