Intelligent Extraction of Erosion Gully from Satellite Images in Northeast China

In order to give full play to the application efficiency of land remote sensing satellites for natural resources and strengthen the protection of cultivated land in China, this paper introduces a two-stage intelligent extraction strategy for erosion gullies. This strategy integrates deep learning object detection with interactive semantic segmentation.Initially, an iterative optimization method of object detection samples and models based on incremental learning and confidence filtering is proposed, achieving automatic identification of erosion gullies. Then, an interactive semantic segmentation method based on edge constraint is adopted to extract the precise contours of erosion gullies semi-automatically. An erosion gully extraction experiment was conducted in Heilongjiang Province of China, utilizing satellite images with a spatial resolution of 2 meters. Accuracy assessment was performed using GF-7 satellite imagery and verified through field verification. The results indicate that the Precision of erosion gully extraction in test area was 95.4%, the Recall was 93.0%, and the F1_score was 94.2%. The feasibility and accuracy of the proposed method has been verified by the experiments.