Abstract
Abstract. Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on. In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting the leading principal components. The main contribution of paper is to introduce the spectral spatial structure and nonlocal similarity of the HSIs into the PCA denoising model. PCA with spectral spatial structure can exploit spectral correlation and spatial correlation of HSI by using 3D blocks instead of 2D patches. Nonlocal similarity means the similarity between the referenced pixel and other pixels in nonlocal area, where Mahalanobis distance algorithm is used to estimate the spatial spectral similarity by calculating the distance in 3D blocks. The proposed method is tested on both simulated and real hyperspectral images, the results demonstrate that the proposed method is superior to several other popular methods in HSI denoising.
Highlights
Hyperspectral image (HSI) is produced with high spectral resolution, providing contiguous or noncontiguous bands throughout the 400-2500 nm region
The existence of noise changes the spectral curve of HSI, which has a negative impact on various HSI processing tasks, classification, unmixing, subpixel mapping, target detection, and so on
Image denoising based on principal component analysis (PCA) model has been attracting more attention, and it has been proved that PCA algorithm is very effective and efficient denoising approach because PCA can separate the signal and noise well by converting the data into the PCA domain
Summary
Hyperspectral image (HSI) is produced with high spectral resolution, providing contiguous or noncontiguous bands throughout the 400-2500 nm region. Most noise reduction methods rely on the local information of signals, the main drawback of which is that the information provided by the neighborhood is too limited to preserve the true structure, details and texture of an image To deal with this problem, nonlocal algorithm was proposed for image denoising (Buades et al, 2005). As a result, constructing a nonlocal spatial spectral correlation model base on PCA is crucial for hyperspectral denoising. The novelty of nonlocal SSPCA lies in the following aspects: 1) separating the signal and noise in HSI only by PCA; 2) exploiting spectral-spatial joint structure of the HSI in PCA; 3) incorporating nonlocality of the HSI into PCA denoising model by calculating the Mahalanobis distance among pixels.
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