Cloud detection is one of the important tasks for remote sensing image (RSI) pre processing. In this paper,we utilize the thumbnail (i.e., preview image) of RSI, which contains the information of original multi spectral or panchromaticimagery, to extract cloud mask efficiently. Compared with detection cloud mask from original RSI, it is more challenging to detect cloud mask using thumbnails due to the loss of resolution and spectrum information. To tackle this problem,we propose a cloud detection neural network (CDnet) with an encoder–decoder structure, a feature pyramid module (FPM),and a boundary refinement (BR) block. The FPM extracts the multi scale contextual information without the loss of re solution and coverage; the BR block refines object boundaries; and the encoder–decoder structure gradually recovers segmentation results with the same size as input image. Experimental results on the ZY-3 satellite thumbnails cloud cover validation data set and two other validation data sets (GF-1 WFV Cloud and Cloud Shadow Cover Validation Data and Landsat-8 Cloud Cover Assessment Validation Data) demonstrate that the proposed method achieves accurate detection accuracy and out performs several state-of-the-art methods.
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