Coexisting Cloud and Snow Detection Based on a Hybrid Features Network Applied to Remote Sensing Images

Owing to the characteristics of cloud and snow, it is difficult to detect them when they coexist. Thus, we propose an end-to-end semantic segmentation network for cloud and snow detection based on hybrid feature (CSD-HFnet) to be applied to remote sensing images (RSIs), in which cloud and snow coexist. First, the local binary pattern (LBP), gray-level co-occurrence matrix (GLCM), and superpixel segmentation are combined as basic features to preserve the textural, spatial, and shape information of the cloud and snow objects. We also propose deep learning feature extraction network to obtain the multiscale deep learning features, which is utilized to better distinguish cloud from snow. The original spectral bands, the basic features, and the multiscale deep learning features are input into the feature integration module simultaneously to form the primary multiscale hybrid features (PMHF). Then, the PMHF are filtered, sorted, and weighted by the feature filtering and sorting block and attention mechanism block to obtain the advanced multiscale hybrid features (AMHFs). Finally, the AMHFs are input into the cloud and snow segmentation network, which consists of several memory-capable gate circuits for training and validation of cloud and snow detection. The results indicate that CSD-HFnet with advanced multiscale hybrid feature (AMHF) can provide reliable detection under the condition of cloud and snow coexistence. CSD-HFnet can detect cloud and snow in multispectral RSIs of various spatial resolutions with an overall accuracy (OA) of 95.37%. Moreover, CSD-HFnet exhibits excellent cloud detection ability for red–green–blue (RGB) images with an OA of 95.88%, higher than other state-of-the-art cloud detection methods.