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.
Highlights
In recent years, hyperspectral imaging technology has achieved great success in many applications such as agriculture, surveillance, and mining
Among the 18 minerals presented in this area, three of them (Alunite, Chalcedony, and Kaolinite) are selected because their outcrops can be spatially and spectrally clearly identified with both high SNR Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and low SNR Hyperion hyperspectral images (HSIs) data simultaneously
Since the additive noise model is the situation usually found in HSIs and many HSI denoising algorithms have been derived based on this model [46], Gaussian i.i.d. noise is added to the reference AVIRIS data in case 1; the variance values are 0.02, 0.04, 0.06, 0.08 and 0.1
Summary
Hyperspectral imaging technology has achieved great success in many applications such as agriculture, surveillance, and mining. The difference between hyperspectral and multispectral images (MSIs) is application dependent, but, in general, HSIs have comparatively higher spectral and lower spatial resolution, whereas MSIs have lower spectral and higher spatial resolution [2]. Multispectral images generally offer high spatial resolution for geological mapping. The spectral resolution of these images is, very low, which severely constrains the application and development of mineral mapping. In order to address this problem, hyperspectral imaging provides high spectral resolution in visible, near-infrared, and shortwave infrared spectral bands. The work [4] uses a knowledge-based expert system to produce mineral maps from AVIRIS HSI data. The relevance of having HSIs with a low level of noise or, equivalently, with a high signal-to-noise-ratio (SNR), to improve the accuracy of mineral prediction, food spectroscopy, and of various tasks such as classification and retrieving supported by spectral libraries has been pointed out, for example, in [6,7,8]
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