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Fog Model-Based Hyperspectral Image Defogging
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AI
DOI：10.1109/TGRS.2021.3101491.png)
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Abstract
En 中文
Fog in hyperspectral images severely limits the visibility of imaging scene and reduces the image contrast, which has a negative effect on the following image interpretation. Defogging methods aim at restoring a high-quality image from the degraded image. Currently, most dehazing methods mainly depend on the atmospheric scattering model in computer vision and multispectral image communities. However, when these approaches are directly used to remove the fog from HSIs, they cannot produce satisfactory defogging performance. To alleviate this issue, we develop a novel fog model to achieve fog removal from hyperspectral images. First, a fog density map is calculated by differentiating the averaged bands falling into visible and infrared spectral ranges. Then, haze abundance in different spectral bands is estimated based on the pixel reflectance between two selected pixels with different haze levels. Finally, the high-quality hyperspectral image is restored by solving the defogging model. Experiments performed on a new benchmark created by ourselves demonstrate that the proposed method obtains favorable dehazing performance in contrast to other approaches in computer vision and remote sensing fields.
Keywords:
Atmospheric modeling
Hyperspectral imaging
Scattering
Image restoration
Sensors
Histograms
Image color analysis
Fog intensity map
haze abundance
hyperspectral image
image defogging
Journal
IF:
8.6
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2.1W
Citations:
10.7W
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