Abstract
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.
Highlights
IntroductionSlope hazards (referring to unstable slopes, landslides, and collapses) are common geological phenomena that have strong effects on the environment around the hazard body and on the safety of human lives and property [1]
Slope hazards are common geological phenomena that have strong effects on the environment around the hazard body and on the safety of human lives and property [1]
An improved watershed segmentation method (Luv-RMWS) is proposed for extracting slope hazard based on a high-resolution GF-2 remote sensing image
Summary
Slope hazards (referring to unstable slopes, landslides, and collapses) are common geological phenomena that have strong effects on the environment around the hazard body and on the safety of human lives and property [1]. It requires the technicians to be highly experienced in geosciences and interpretation, and necessitates a large investment of manpower and time, leading to low production efficiency and making the extracted information subjective and imprecise. Slope areas have highly consistent textures and spectrums on a high-resolution remote sensing image, but the hues are usually different from that of the surrounding background. This provides the basis for automatic extraction of slope hazard boundaries using watershed image segmentation technology
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