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摘要: 煤矸石山是矿山生态修复关注的重点区域, 查明煤矸石山的地理空间分布情况对区域环境治理具有重要意义。选取福建省龙岩市新罗区的部分区域为研究区, 基于GF-2遥感影像及ASTER GDEM数字高程模型数据, 提取光谱特征、纹理特征及地形特征, 利用顺序前向特征选择法对特征进行优化, 并利用随机森林算法构建地物分类模型, 结合多源数据及综合性特征组合对研究区内的地表类型进行分类并实现煤矸石山的信息识别提取。结果表明: ①并非参与分类的特征越多分类精度越高, 特征选择后数量由17个减少至9个, 煤矸石山总体提取精度达94.07%, Kappa系数达0.819; ②地形特征中高程、坡度、坡向及光谱特征中多光谱波段(B1, B2, B4)、归一化植被指数、影像灰度平均值对煤矸石堆存区识别提取具有重要作用, 而纹理特征仅对提高具有明显纹理变化的土地覆盖类型的精度有帮助, 对提高煤矸石山提取精度作用较低, 仅纹理均值特征对煤矸石山提取影响较大。结合随机森林和特征优化算法, 能够有效增强煤矸石山的提取精度, 高效整合多源特征数据, 加快模型运算速度, 为煤矸石山信息提取提供切实可行的方法。Abstract: Coal gangue mountains are key areas for the ecological restoration of coal mines. Understanding their geographical distribution holds great significance for regional environmental management. This study focused on part of Xinluo District, Longyan City, Fujian Province. Using GF-2 remote sensing images and data from the ASTER GDEM digital elevation model, this study extracted spectral, texture, and topographic features and then optimized these features using the sequential forward selection method. Subsequently, this study developed a model for surface feature classification using a random forest algorithm. Using this model, this study categorized surface features by integrating multi-source data and comprehensive feature combinations and then achieved the identification and information extraction of coal gangue mountains. The results indicate that the classification accuracy did not necessarily increase with the number of features. After feature selection, the number of features was reduced from 17 to 9, and the overall extraction accuracy of coal gangue mountains reached 94.07%, with a Kappa coefficient of 0.819. Factors playing an important role in the identification and information extraction of coal gangue deposit areas included elevation, slope, aspect, multi-spectral bands B1, B2, and B4 in the spectral characteristics, normalized vegetation index, and grayscale value of images. In contrast, texture features merely improved the accuracy of surface feature types with distinct textural variations, while producing limited effects on the information extraction of coal gangue mountains. For the study area, only the mean texture feature produced significant effects on the information extraction accuracy of coal gangue mountains. The combination of random forest and feature optimization algorithm can effectively enhance the information extraction accuracy of coal gangue mountain, efficiently integrate multi-source feature data, and accelerate model calculation, serving as a practically feasible method for the information extraction of coal gangue mountains.
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Key words:
- remote sensing /
- GF-2 image /
- random forest classification /
- coal gangue /
- feature optimization
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