Abstract
Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial correlation information among pixels and subpixels is considered in the prediction of the spatial locations of land-cover classes within the mixed pixels. In this paper, a novel subpixel mapping method for hyperspectral remote sensing imagery based on a nonlocal method, namely nonlocal total variation subpixel mapping (NLTVSM), is proposed to use the nonlocal self-similarity prior to improve the performance of the subpixel mapping task. Differing from the existing spatial regularization subpixel mapping technique, in NLTVSM, the nonlocal total variation is used as a spatial regularizer to exploit the similar patterns and structures in the image. In this way, the proposed method can obtain an optimal subpixel mapping result and accuracy by considering the nonlocal spatial information. Compared with the classical and state-of-the-art subpixel mapping approaches, the experimental results using a simulated hyperspectral image, two synthetic hyperspectral remote sensing images, and a real hyperspectral image confirm that the proposed algorithm can obtain better results in both visual and quantitative evaluations.
Highlights
Due to the impact of the sensors’ instantaneous field of view (IFOV) and the diversity of land-cover objects, mixed pixels are common in hyperspectral remote sensing images [1,2,3]
We described the nonlocal total variation spatial operator in detail
TVSM algorithms, they all consider the spatial information as prior knowledge in the process of subpixel mapping, nonlocal total variation subpixel mapping (NLTVSM) obtains a better result by utilizing the nonlocal total variation regularization operator, which can take advantage of the high degree of redundancy in the nonlocal spatial information of the image to predict more fine structure and details, and can suppress most of NLTVSM method is better thanthe the withered traditional algorithms
Summary
Due to the impact of the sensors’ instantaneous field of view (IFOV) and the diversity of land-cover objects, mixed pixels are common in hyperspectral remote sensing images [1,2,3]. The spatial distribution of each endmember or land-cover class in the pixel remains unknown [8,9], which is quite important in real applications such as subpixel classification and subpixel target detection To solve this problem, the subpixel mapping technique was proposed to determine the subpixel location of each class or endmember based on the fractional abundances [10,11,12,13,14,15]. A new spatial regularization subpixel mapping algorithm based on a nonlocal prior model, namely nonlocal total variation subpixel mapping (NLTVSM), is proposed.
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