Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, have a tendency to lose fine features and spatial data, leading to coarse segmentation of cloud and cloud shadow borders, false detections, and omissions of targets. To address the aforementioned issues, a multi-scale strip feature attention network (MSFANet) is proposed. This approach uses Resnet18 as the backbone for obtaining semantic data at multiple levels. It incorporates a particular attention module that we name the deep-layer multi-scale pooling attention module (DMPA), aimed at extracting multi-scale contextual semantic data, deep channel feature information, and deep spatial feature information. Furthermore, a skip connection module named the boundary detail feature perception module (BDFP) is introduced to promote information interaction and fusion between adjacent layers of the backbone network. This module performs feature exploration on both the height and width dimensions of the characteristic pattern to enhance the recovery of boundary detail intelligence of the detection targets. Finally, during the decoding phase, a self-attention module named the cross-layer self-attention feature fusion module (CSFF) is employed to direct the aggregation of deeplayer semantic feature and shallow detail feature. This approach facilitates the extraction of feature information to the maximum extent while conducting image restoration. The experimental outcomes unequivocally prove the efficacy of our network in effectively addressing complex cloud-covered scenes, showcasing good performance across the cloud and cloud shadow datasets, the HRC_WHU dataset, and the SPARCS dataset. Our model outperforms existing methods in terms of segmentation accuracy, underscoring its paramount importance in the field of cloud recognition research.

Among the most difficult difficulties in contemporary satellite image-processing subjects is cloud and cloud shade segmentation. Due to substantial background noise interference, existing cloud and cloud shadow segmentation techniques would result in false detection and missing detection. We propose a Location Pooling Multi-Scale Network (LPMSNet) in this study. The residual network is utilised as the backbone in this method to acquire semantic info on various levels. Simultaneously, the Location Attention Multi-Scale Aggregation Module (LAMA) is introduced to obtain the image’s multi-scale info. The Channel Spatial Attention Module (CSA) is introduced to boost the network’s focus on segmentation goals. Finally, in view of the problem that the edge details of cloud as well as cloud shade are easily lost, this work designs the Scale Fusion Restoration Module (SFR). SFR can perform picture upsampling as well as the acquisition of edge detail information from cloud as well as cloud shade. The mean intersection over union (MIoU) accuracy of this network reached 94.36% and 81.60% on the Cloud and Cloud Shadow Dataset and the five-category dataset L8SPARCS, respectively. On the two-category HRC-WHU Dataset, the accuracy of the network on the intersection over union (IoU) reached 90.51%. In addition, in the Cloud and Cloud Shadow Dataset, our network achieves 97.17%, 96.83%, and 97.00% in precision (P), recall (R), and F1 score ($F_1$) in cloud segmentation tasks, respectively. In the cloud shadow segmentation task, precision (P), recall (R), and F1 score ($F_1$) reached 95.70%, 96.38%, and 96.04%, respectively. Therefore, this method has a significant advantage over the current cloud and cloud shade segmentation methods.

Cloud and snow often share comparable visual and structural patterns in satellite observations, making their accurate discrimination and segmentation particularly challenging. To overcome this, we design an innovative Transformer-guided architecture with complementary feature-extraction capabilities. The encoder adopts a dual-path structure, integrating a Transformer Encoder Module (TEM) for capturing long-range semantic dependencies and a ResNet18-based convolutional branch for detailed spatial representation. A Feature-Enhancement Module (FEM) is introduced to promote bidirectional interaction and adaptive feature integration between the two pathways. To improve delineation of object boundaries, especially in visually complex areas, we embed a Deep Feature-Extraction Module (DFEM) at the deepest layer of the convolutional stream. This component refines channel-level information to highlight critical features and enhance edge clarity. Additionally, to address noise from intricate backgrounds and ambiguous cloud-snow transitions, we incorporate both a Transformer Fusion Module (TFM) and a Strip Pooling Auxiliary Module (SPAM) in the decoding phase. These modules collaboratively enhance structural recovery and improve robustness in segmentation. Extensive experiments on the CSWV and SPARCS datasets show that our method consistently outperforms state-of-the-art baselines, demonstrating its strong effectiveness and applicability in real-world cloud and snow-detection scenarios.