In the black soil region of Northeast China, gully erosion is severe and widespread. Currently, gully monitoring in this area relies predominantly on manual interpretation, highlighting the urgent need for a rapid extraction method. This study selects the Mashezi River Basin in Binxian country, Heilongjiang province, a region heavily affected by gully erosion, as study area. Utilizing GF-7 satellite imagery and comparing with manual interpretation results, the accuracy of three automatic gully extraction methods is evaluated:flow-directional detection, machine learning and deep learning. The findings are as follows:① The flow-directional detection method depends on high-precision topographic data. The vertical accuracy of topographic data generated from GF-7 stereo images is poor, resulting in an overall extraction accuracy of only 6. 7％, and this method is unable to automatically extract permanent gullies and ephemeral gullies from GF-7. ②The machine learning approach requires manual setting of segmentation parameters and design of classification features, limiting its degree of automation. It achieves an overall extraction accuracy of 50. 7％, with a precision of 83. 1％ for permanent gullies and only 9. 2％ for ephemeral gullies. ③The deep learning method adopts an end-to-end approach, without the need to design feature extractors. It offers a high degree of automation with an overall extraction accuracy of 60. 8％, achieving 68. 1％ accuracy in identifying permanent gullies and 69. 7％ in recognizing ephemeral gullies.
black soil region in Northeast ChinaGF-7 satellite imageflow-directional detectionmachine learningdeep learning
维普
万方数据
