CLOUD DETECTION USING GABOR FILTERS AND ATTENTION-BASED CONVOLUTIONAL NEURAL NETWORK FOR REMOTE SENSING IMAGES

Cloud detection is a critical part of remote sensing images preprocessing, which can be regarded as an image pixel-segmentation problem. In recent years, because of effective performance, convolutional neural network is widely used in image segmentation. This paper proposed a cloud detection method based on convolutional neural network, not only adding Gabor feature extraction module to further extract the detail information in the low-level features but also mining the correlation between high-level features through the channel attention module. In order to evaluate our method, experiments were carried on the Gaofen-1 WFV dataset containing different types of clouds over various underlying. The results show that our method has higher accuracy rate and lower false alarm rate comparing to several state-of-the-art image segmentation network.