remote sensing Article A Cloud Detection Method Using Convolutional Neural Network Based on Gabor Transform and Attention Mechanism with Dark Channel Subnet for Remote Sensing Image Jing Zhang 1,∗ , Qin Zhou 1, Jun Wu 1, Yuchen Wang 1, Hui Wang 1, Yunsong Li 1, Yuzhou Chai 2 and Yang Liu 2 1 State Key Laboratory of Integrated Service Network, Xidian University, Xi’an 710071, China;
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[email protected] (Y.L.) 2 Data Transmission Institute, China Academy of Space Technology, Xi’an 710000, China;
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[email protected] (Y.L.) * Correspondence:
[email protected]; Tel.: +86-298-820-3116 Received: 17 August 2020; Accepted: 2 October 2020; Published: 7 October 2020 Abstract: Cloud detection, as a crucial step, has always been a hot topic in the field of optical remote sensing image processing. In this paper, we propose a deep learning cloud detection Network that is based on the Gabor transform and Attention modules with Dark channel subnet (NGAD). This network is based on the encoder-decoder framework. The information on texture is an important feature that is often used in traditional cloud detection methods. The NGAD enhances the attention of the network towards important texture features in the remote sensing images through the proposed Gabor feature extraction module. The channel attention module that is based on the larger scale features and spatial attention module that is based on the dark channel subnet have been introduced in NGAD.