A High-Resolution Remote Sensing Image Landslide Detection Method Based on HRNet
In recent years,landslides have occurred frequently.It is of great significance to identify the landslide in time for disaster reduction and post-disaster reconstruction.In the landslide recognition task of high resolution remote sensing image,there are problems of uneven positive and negative samples and imperfect feature extraction.In this paper,a high-resolution remote sensing image landslide recognition model based on HRNet is proposed.The high-resolution feature pyramid network structure of the model can fully capture geological features at multiple scales and improve the model's adaptability to complex terrain.At the same time,Focal Loss loss function was adopted in the training process to assign greater weight to positive samples that were difficult to be classified,which effectively alleviated the problem of imbalance between positive and negative samples.The model pays more attention to the difficult to distinguish landslide area,which further improves the accuracy and robustness of landslide identification.The experimental results show that the proposed method achieves superior landslide segmentation performance in the data set,and has guiding significance for the rapid response after disaster.
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