LPMSNet: Location Pooling Multi-Scale Network for Cloud and Cloud Shadow Segmentation

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 16; p. 4005
• Main Authors: Dai, Xin, Chen, Kai, Xia, Min, Weng, Liguo, Lin, Haifeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Background noise; channel space attention; cloud and cloud shadow detection; Cloud computing; convolutional neural network; Datasets; Efficiency; Image processing; Image segmentation; Modules; multi-scale extraction; Neural networks; Recall; Remote sensing; Satellite imagery; scale fusion restoration; Semantics; Shade; Shadows
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15164005

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 (F1) in cloud segmentation tasks, respectively. In the cloud shadow segmentation task, precision (P), recall (R), and F1 score (F1) 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.