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

Abstract

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.

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

“…Therefore, considering that such detectors adopting a sliding window strategy are usually sensitive to the window sizes, different pairs of window sizes are set in order to convincingly reflect their detection performance. Taking account of both characteristics of different hyperspectral images and requirements of different detectors' parameter settings, we define four pairs of window sizes for LRX- (17,7), (17,9), (19,7), and (19,9)-and six pairs of window sizes for SRD- (13,7), (15,9), (17,7), (17,9), (19,7), and (19,9). Besides, we set the optimal value for the regularized parameter of SRD for each hyperspectral image in the experiment.…”

“…These spectra are represented by hundreds of continuous bands that can meticulously describe the characteristics of different materials to recognize their subtle differences [3]. Therefore, owing to this good discriminative property of hyperspectral image, it has been widely used in many remote sensing research fields [4,5], such as image denoising [6,7], hyperspectral unmixing [8,9], band selection [10,11], target detection [12,13], and image classification [14,15]. They all have important practical applications in geological exploration, urban remote sensing and planning management, environment and disaster monitoring, precision agriculture, archaeology, etc.…”

A Detail-Preserving Cross-Scale Learning Strategy for CNN-Based Pansharpening

“…In addition, it is also worth mentioning the matrix factorization approaches, examples are References [38][39][40], which are more suited to the fusion of low resolution hyperspectral images with high resolution multispectral ones. In this case, in fact, the spectral variability becomes a serious concern to be handled carefully by means of unmixing oriented methodologies [41][42][43].…”

Noise Removal Based on Tensor Modelling for Hyperspectral Image Classification

“…In [4,5], it was shown that the SI noise in some HSIs is colored, i.e., spectrally non-white. With the improvement of the sensitivity in the electronic components [6], the resolution of the charged coupled device (CCD) camera has improved significantly, so that the photon noise has become as dominant as the signal-independent electronic noise in HSI data collected by new-generation hyperspectral sensors [7][8][9][10]. In this case, the assumption of additive and stationary noise model is not appropriate although this hypothesis is plausible for HSIs where the SI noise is dominant while SD noise, which depends on the useful signal level, is negligible.…”

“…In this case, the assumption of additive and stationary noise model is not appropriate although this hypothesis is plausible for HSIs where the SI noise is dominant while SD noise, which depends on the useful signal level, is negligible. Therefore, in this paper, we use the widely accepted noise model in [7][8][9][10] including both signal-dependent and signal-independent noise. There are few denoising algorithms based on the photon noise model.…”