Abstract
In technologically underdeveloped areas, water pollution threatens the living environment of local residents, so remote sensing monitoring of the features around reservoirs is necessary. Fully convolutional networks (FCNs) offer great potential for extracting high-resolution features due to their unlimited input image size and higher accuracy compared to convolutional neural networks. Therefore, a proposal to classify WorldView-2 images is implemented with a sixty-eight thousand iterations of fine-tuning and fully trained combined training method based on a fully convolutional network (SEFCN). The chosen images depict the urban area of Yingde, which is located to the northeast of the Feilaixia Reservoir, Qingyuan, Guangdong Province, China. The SEFCN combines an FCN-32s and FCN-16s to better integrate the deep features and shallow features and effectively improve the classification accuracy. Additionally, the loss value fully converges with enough iterations, and the overfitting caused by the superposition of two full trainings is avoided. The SEFCN model achieves the highest accuracy among all compared classification models and obtains the best classified results on the WorldView-2 images, as demonstrated by attaining the highest F1 scores in each category. After the classified images are optimized using a conditional random field, the current status of the study area is analyzed, and several suggestions for land use in the urban area of Yingde are made. The experiments still have deficiencies in the application of high-resolution remote sensing image classification with different sensors, and the classification results can be optimized and improved in other aspects.
Highlights
Reservoir projects have functions such as flood control, water storage irrigation, water supply regulation, and power generation
The mean intersection over union (MIoU) and frequency-weighted intersection over union (FWIoU) were both higher in the SEFCN model than in the other four models, and the pixel accuracy and mean accuracy were both above 0.90
The SEFCN model achieved the highest F1 score among these classifiers, and the findings indicate that the SEFCN is superior to the other six models in the classification of WorldView-2 images
Summary
Reservoir projects have functions such as flood control, water storage irrigation, water supply regulation, and power generation. They play a crucial role in regulating local ecological environments and promoting local economic development. The ecological spatial patterns of landscapes, such as cultivated land, forests, and settlements, have changed significantly. Various illegal activities, such as sand mining, breeding, and reclamation, have resulted in increased water pollution. This has threatened the living environment of local residents [1]–[4]. There are a large amount of areas
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