Extraction and Classification of the Supervised Coastal Objects Based on HSRIs and a Novel Lightweight Fully Connected Spatial Dropout Network

For the protection and management of coastal ecosystems, it is crucial to monitor typical coastal objects and examine their characteristics of spatial and temporal variation. There are limitations to the conventional object-oriented and spectrum-based approaches to HSRIs interpretation. The majority of recently conducted studies on semantic segmentation based on DCNNs concentrate on improving the accuracy of single objects at local scales. The completeness, generalization, and edge accuracy of the extraction and classification of multiple objects with the complex background at regional scales still need to be improved. We created a benchmark dataset CSRSD for coastal supervision using HSRIs and GIS in this study to address the aforementioned problems. In the meantime, by combining the traditional U-Net and DeepLabV3+ feature fusion strategies, we propose a novel fully connected fusion pattern by switching to deepwise separable convolution from conventional convolution and introducing spatial dropout to create a brand new CBS module. The LFCSDN, a new lightweight fully connected spatial dropout network, has been suggested. The findings demonstrate that our constructed semantic segmentation dataset, which has produced reliable results on U-Net and DeepLabV3+, can be used as a benchmark for applications based on DCNNs for coastal scenes. While maintaining high accuracy, LFCSDN can significantly reduce the number of parameters. Our suggested CBS module can increase the model’s generalization by reducing overfitting. In order to analyze the spatiotemporal characteristics of target changes in the study area, tests on expansive remote sensing imagery were also conducted. The findings can be applied to ecological restoration, coastal area mapping, and integrated management. Additionally, it serves as a resource for studies on multiscale semantic segmentation in computer vision.