Cloud detection is critical in remote sensing image processing, and convolutional neural networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily focus on extracting local features, which can be challenging for cloud detection due to the variability in the size, shape, and boundaries of clouds. To address this limitation, we propose a hybrid Swin transformer–CNN cloud detection (STCCD) network that combines the strengths of both architectures. The STCCD network employs a novel dual-stream encoder that integrates Swin transformer and CNN blocks. Swin transformers can capture global context features more effectively than traditional CNNs, while CNNs excel at extracting local features. The two streams are fused via a fusion coupling module (FCM) to produce a richer representation of the input image. To further enhance the network’s ability in extracting cloud features, we incorporate a feature fusion module based on the attention mechanism (FFMAM) and an aggregation multiscale feature module (AMSFM). The FFMAM selectively merges global and local features based on their importance, while the AMSFM aggregates feature maps from different spatial scales to obtain a more comprehensive representation of the cloud mask. We evaluated the STCCD network on three challenging cloud detection datasets (GF1-WHU, SPARCS, and AIR-CD), as well as the L8-Biome dataset to assess its generalization capability. The results show that the STCCD network outperformed other state-of-the-art methods on all datasets. Notably, the STCCD model, trained on only four bands (visible and near-infrared) of the GF1-WHU dataset, outperformed the official Landsat-8 Fmask algorithm in the L8-Biome dataset, which uses additional bands (shortwave infrared, cirrus, and thermal).

The impervious surface (IS) cover and its dynamics are key parameters in research about urban and ecology. This letter proposed an automatic framework to map the IS growth end-to-end based on the temporal deep learning (DL) model and long time-series Landsat imagery. First, the training and validating datasets were auto-generated by a joint strategy. Then, a DL network was designed, and the IS growth was predicted in temporal windows. Finally, the results from multi-temporal windows are combined to generate the IS growth map. The data around the core of Beijing, China, is tested, and the result shows that the proposed method could: 1) efficiently model the IS growth; 2) map IS growth with less salt-and-pepper noise and false alarm compared to existing products; and 3) be extended to future data easily.

Clouds often cause challenges during the application of optical satellite images. Masking clouds and cloud shadows is a crucial step in the image preprocessing workflow. The absence of a thermal band in products of the Sentinel-2 series complicates cloud detection. Additionally, most existing cloud detection methods provide binary results (cloud or non-cloud), which lack information on thin clouds and cloud shadows. This study attempted to use end-to-end supervised spatial–temporal deep learning (STDL) models to enhance cloud detection in Sentinel-2 imagery for China. To support this workflow, a new dataset for time-series cloud detection featuring high-quality labels for thin clouds and haze was constructed through time-series interpretation. A classification system consisting of six categories was employed to obtain more detailed results and reduce intra-class variance. Considering the balance of accuracy and computational efficiency, we constructed four STDL models based on shared-weight convolution modules and different classification modules (dense, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and transformer). The results indicated that spatial and temporal features were crucial for high-quality cloud detection. The STDL models with simple architectures that were trained on our dataset achieved excellent accuracy performance and detailed detection of clouds and cloud shadows, although only four bands with a resolution of 10 m were used. The STDL models that used the Bi-LSTM and that used the transformer as the classifier showed high and close overall accuracies. While the transformer classifier exhibited slightly lower accuracy than that of Bi-LSTM, it offered greater computational efficiency. Comparative experiments also demonstrated that the usable data labels and cloud detection results obtained with our workflow outperformed the results of the existing s2cloudless, MAJA, and CS+ methods.