Automatic cloud detection method for optical remote sensing images combining channel attention mechanism and multi-feature fusion

Abstract

Cloud detection is a crucial step in remote sensing image pre-processing, but there are still issues such as cloud miss detection and false detection. Therefore, this work suggests a multi-feature fusion and channel attention mechanism combined approach for automatically recognizing clouds. Based on DeepLabV3+, to make the feature extraction faster and more accurate, a lightweight network structure MobileNetV2 is used as the backbone network. To effectively distinguish snow and clouds, channel attention is added to the low-level semantic information, which makes the network more able to focus on essential features. Meantime, mid-level semantic features are added to the decoder module to make use of more abundant features, thus reducing the false detection of mist. Experimental findings on the GF1 WHU dataset demonstrate that the method’s detection accuracy is improved by 2.1% when compared to DeepLabV3+, and it performs well in the cloud detection job of optical remote sensing images.