To accurately identify slope hazards based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm is proposed. The color difference of the Luv color space was used as the regional similarity measure for region merging. Furthermore, the area relative error for evaluating the image segmentation accuracy was improved and supplemented with the pixel quantity error to evaluate the segmentation accuracy. An unstable slope was identified to validate the algorithm on Chinese Gaofen-2 (GF-2) remote sensing imagery by a multiscale segmentation extraction experiment. The results show the following: (1) the optimal segmentation and merging scale parameters were, respectively, minimum threshold constant C
for minimum area Amin
of 500 and optimal threshold D
for a color difference of 400. (2) The total processing time for segmentation and merging of unstable slopes was 39.702 s, much lower than the maximum likelihood classification method and a little more than the object-oriented classification method. The relative error of the slope hazard area was 4.92% and the pixel quantity error was 1.60%, which were superior to the two classification methods. (3) The evaluation criteria of segmentation accuracy were consistent with the results of visual interpretation and the confusion matrix, indicating that the criteria established in this study are reliable. By comparing the time efficiency, visual effect and classification accuracies, the proposed method has a good comprehensive extraction effect. It can provide a technical reference for promoting the rapid extraction of slope hazards based on remote sensing imagery. Meanwhile, it also provides a theoretical and practical experience reference for improving the watershed segmentation algorithm.
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