Traditional building and water segmentation methods are vulnerable to noise interference, and hence they could not avoid missed and false detections in the detection process. Excessive deep learning downsampling would lead to significant loss of feature map information, and image location information offset, and the overall effect of falling apart. To address these issues, a Multi-Scale Location Attention Network (MSLA) is proposed. Location-spatial information and channel information are particularly important for edge detail segmentation in building and water cover. The network includes a Location Channel Attention Unit (LCA) to focus on tributary details of rivers and segmentation of building edge eaves. Moreover, this paper builds a Dual-Branch Multi-Scale Aggregation Unit (DBMSA) to obtain deeper multi-scale semantic information. Finally, the Multi-Scale Fusion Unit (MSF) is used to guide the information merging of multiple stages, and the boundary information is improved by splicing the acquired deep multi-scale information with the information of the relevant feature extraction layer in the downsampling. The experimental results on several datasets show that the proposed approach outperforms other methodologies in segmentation accuracy.

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.

The analysis of land cover types is helpful for detecting changes in land use categories and evaluating land resources. It is of great significance in environmental monitoring, land management, land planning, and mapping. At present, remote sensing imagery obtained by remote sensing is widely employed in the classification of land types. However, most of the existing methods have problems such as low classification accuracy, vulnerability to noise interference, and poor generalization ability. Here, a multi-scale contextual semantic guidance network is proposed for the classification of land cover types by deep learning. The whole model combines an attention mechanism with convolution to make up for the limitation that the convolution structure can only focus on local features. In the process of feature extraction, an interactive structure combining attention and convolution is introduced in the deep layer of the network to fully extract the abstract information. In this paper, the semantic information guidance module is introduced in the cross-layer connection part, ensuring that the semantic information between different levels can be used for mutual guidance, which is conducive to the classification process. A multi-scale fusion module is proposed at the decoder to fuse the features between different layers and avoid loss of information during the recovery process. Experiments on two public datasets demonstrate that the suggested approach has higher accuracy than existing models as well as strong generalization ability.

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.