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Don't stop them now: Rocket Lab launches 5 satellites to orbit | Space
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Besides, the dilated residual blocks of the generator can also sufficiently improve the dehazing effects. Many approaches of removing haze focus on processing multi-spectral or RGB images, whereas few of them utilize multi-sensor data. Copyright and all rights therein are retained by authors or by other copyright holders. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. Our experiments demonstrate that the proposed method, which fuses the information of different sensors applied to the cloudy conditions, can achieve more precise results than other baseline models.{/INSERTKEYS}{/PARAGRAPH} This material is presented to ensure timely dissemination of scholarly and technical work. {PARAGRAPH}{INSERTKEYS}WACV open access. Satellite image dehazing aims at precisely retrieving the real situations of the obscured parts from the hazy remote sensing RS images, which is a challenging task since the hazy regions contain both ground features and haze components. Moreover, we propose a novel fusion dehazing method to directly restore the haze-free RS images by using an end-to-end conditional generative adversarial network cGAN. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In this paper, a dataset called SateHaze1k is established and composed of pairs clear Synthetic Aperture Radar SAR , hazy RGB, and corresponding ground truth images, which are divided into three degrees of the haze, i. The multi-sensor data fusion is significant to provide auxiliary information since RGB images are sensitive to atmospheric conditions.