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Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is designed...
Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step...
Cloud detection plays a major role for remote sensing image processing. Most of the existed cloud detection methods use the low-level feature of the cloud, which often cause error result especially for thin cloud and complex scene. In this paper, a novel cloud detection method based on deep learning framework is proposed. The designed deep Convolutional Neural Networks (CNNs) consists of four convolutional...
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