Element-Oriented Land-Use Classification of Mining Area by High Spatial Resolution Remote Sensing Image
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摘要: 为了合理开发矿产资源和有效监测矿区生态环境，采用面向基元的分类方法，对广西横县某矿区的高分辨率航空遥感影像进行了土地利用分类.通过优化分形网络演化多尺度分割方法，高效提取了矿区两个尺度上的影像基元层；基于基元信息，详细分析了各地表地物光谱特征、空间特征以及类相关特征，建立了研究区土地利用的分类知识库；采用决策支持的模糊逻辑推理法进行分类，使分类的精度从53%提高到了90%.表明面向基元的方法能较好地利于高空间分辨率矿区影像的各种特征进行高精度的土地利用分类.Abstract: For the purpose of rational exploitation of mineral resources and effective monitor of ecological environment in mining areas, we did experiments about land-use classification of the high spatial resolution airborne image from a mining area in Heng County of Guangxi province using element-oriented method. By optimizing the evolution of multi-scale fractal network segment process, two levels of image elements were extracted efficiently. Based on the multi-scale image elements, the land-use classification knowledge base of the study area was established through analyzing spectral, spatial and class-relation features in this area. The classification precision improved from 53% to 90% by decision supporting fuzzy logic reasoning of the knowledge base. The experiments show that the element-oriented method can obtain high precision land-use classification for taking full advantage of various features of the mining area from the high spatial resolution image.
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图 1 研究区位置(a)和研究区高空间分辨率航空影像(b)
Fig. 1. Location of the study area (a) and high spatial resolution aerial image of the study area (b)
图 2 面向基元的遥感影像分类流程
Fig. 2. The flow chart of remote sensing image classification by element oriented method
图 3 (a) 面向基元方法的分类结果和(b)LVQ神经网络监督分类结果
Fig. 3. (a) The classification result by element-oriented method and (b) The supervised classification result by LVQ neural network method
表 1 研究区分类知识库规则
Table 1. The classification rule of knowledge base from the study area
地类 推理规则 水体 32＜L1＜84 and 28＜L2＜62(3次高斯模糊函数) and L2/L1＞1.2 农田 107＜L1＜132 and 120＜L2＜151 and 83＜L3＜110(3次高斯模糊函数) 道路 L3＞152 and长宽比＞2 居民地 GLDV对比度L2＞980 矿区 L2＞220 and紧邻矿区的居民地 林地 非水体and非农田and非道路and非居民地and非矿区 
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表 2 面向基元分类误差矩阵及精度评价
Table 2. Error matrix and accuracy assessment on basic element-oriented
结果\参考 水体 道路 林地 矿区 农田 居民地 像元数 用户精度 水体 381 0 0 0 0 59 440 0.865 9 道路 0 406 0 0 0 24 420 0.966 7 林地 28 0 6 139 68 102 83 6 420 0.956 2 矿区 0 0 17 248 0 9 274 0.905 1 农田 0 8 90 0 1 417 0 1 515 0.935 3 居民地 0 7 39 14 37 1 254 1 351 0.928 2 像元数 409 421 6 285 330 1 556 1 429 10 430 生产精度 0.931 5 0.964 4 0.976 8 0.751 5 0.910 7 0.877 5 总精度 0.943 9 Kappa系数 0.904 2
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表 3 监督分类(LVQ)误差矩阵及精度评价
Table 3. Error matrix and accuracy assessment on supervised classification
结果\参考 水体 道路 林地 矿区 农田 居民地 像元数 水体 0 0 0 0 0 0 0 道路 0 541 0 449 0 1 080 2 070 林地 565 0 4 680 1 37 3 402 8 685 矿区 0 280 245 719 0 461 1 705 农田 0 20 759 333 4 537 1 782 7 431 居民地 0 0 0 0 0 0 0 像元数 565 841 5 684 1 502 4 574 6 725 19 891 生产精度 0.000 0.643 0.823 0.479 0.992 0.000 总精度 0.53 Kappa系数 0.45
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