Abstract
The Orbita hyperspectral satellite (OHS) is the first hyperspectral satellite with surface coating technology for sensors in the world. It includes 32 bands from visible to near-infrared wavelengths. However, technology such as the fabricating process of complementary metal–oxide–semiconductor (CMOS) sensors makes the image contain a lot of random and unsystematic stripe noise, which is so bad that it seriously affects visual interpretation, object recognition and the application of the OHS data. Although a large number of stripe removal algorithms have been proposed, very few of them take into account the characteristics of OHS sensors and analyze the causes of OHS data noise. In this paper, we propose a destriping algorithm for OHS data. Firstly, we use both the adaptive moment matching method and multi-level unidirectional total variation method to remove stripes. Then a model based on piecewise linear least squares fitting is proposed to restore the vertical details lost in the first step. Moreover, we further utilize the spectral information of the OHS image, and extend our 2-D destriping method to the 3-D case. Results demonstrate that the proposed method provides the optimal destriping result on both qualitative and quantitative assessments. Moreover, the experimental results show that our method is superior to the existing single-band and multispectral destriping methods. Also, we further use the algorithm to the stripe noise removal of other real remote sensing images, and excellent image quality is obtained, which proves the universality of the algorithm.
Highlights
The orbita hyperspectral satellite (OHS) is the world's first hyperspectral satellite that uses surface coating technology for sensors, and it obtained hyperspectral images of the target object by fabricating variable filters evenly on the detector glass and using the push-broom mode to acquire alone-track images
Compared with other hyperspectral satellites, OHS has the advantages of combining high spatial resolution, high spectral resolution, and the large swath width, so it breaks through the bottleneck of hyperspectral satellites, and it opens a new era of quantitative remote sensing
In order to realize the goal of removing stripe noises and maintaining details for the OHS image, in this paper, we propose a method based upon adaptive moment matching and multi-level unidirectional total variational to remove stripes, and a method based on piecewise linear least squares fit to restore details
Summary
The orbita hyperspectral satellite (OHS) is the world's first hyperspectral satellite that uses surface coating technology for sensors, and it obtained hyperspectral images of the target object by fabricating variable filters evenly on the detector glass and using the push-broom mode to acquire alone-track images. The filtering-based methods, such as the wavelet-based filter [7,8,9], the selective and adaptive filter [10] or the finite impulse response filter [11], etc., remove stripe noises by constructing a filter at a given frequency These approaches are easy to achieve and can produce good results on georectified images, but for the image with non-periodic stripes, it is impossible to accurately separate the stripes and the images, resulting in a serious loss of image details. In order to realize the goal of removing stripe noises and maintaining details for the OHS image, in this paper, we propose a method based upon adaptive moment matching and multi-level unidirectional total variational to remove stripes, and a method based on piecewise linear least squares fit to restore details.
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