摘要:
花岗伟晶岩是花岗伟晶岩型锂矿的重要载体及找矿标志,青海省天峻县扎卡东南部一带有较好的锂矿找矿潜力,但该地区具有海拔高、切割深等特点,使得实地地表调查难度较大,因此采用随机森林算法对研究区花岗伟晶岩进行遥感提取,以GF-2高空间分辨率遥感影像为数据源提取研究区域各类型地物的光谱特征、纹理特征、指数特征、地形特征、边缘特征及文中新引入的限制对比度自适应直方图均衡化(contrast limited adaptive hitogram equalization,CLAHE)特征等25个特征变量构建特征子集,对子集中的特征变量进行特征重要性评估,依据特征重要性得分进行特征优选,确定提取花岗伟晶岩的最优特征组合,最终选取16个特征变量进行随机森林分类,对分类结果进行精度评价。研究表明: ①CLAHE特征变量有利于突出地物间的色调差异,有助于分类精度的提升,其总体精度上升2.7百分点,Kappa系数上升0.035; ②基于GF-2影像和随机森林算法的分类结果的总体精度达到了93.1%,Kappa系数达到0.902,花岗伟晶岩用户精度达到94.24%,生产者精度达到 98.00%,证实方法的有效性,同时也为该地区进一步工作提供真实可靠的资料。
Abstract:
Granite pegmatites serve as a significant carrier and prospecting marker of granite pegmatite-type lithium deposits. The southeastern Zhaka area in Tianjun County, Qinghai Province demonstrates considerable prospecting potential for lithium deposits. Nevertheless, its high altitudes and deep cross-cutting characteristics pose challenges in surface surveys. Hence, this study extracted the granite pegmatite information within the study area from remote sensing images using the random forest algorithm. With high-spatial-resolution GF-2 remote sensing images as the primary data source, it extracted the spectral, texture, exponential, topographic, and edge features from various ground objects within the study area. These features, together with the newly introduced contrast limited adaptive histogram equalization (CLAHE) features, constituted 25 feature variables, forming a feature subset. Then, feature variables in the subset were evaluated for their feature importance, and their importance scores were used for feature selection, determining the optimal feature combination for extracting granite pegmatite information. Ultimately, 16 feature variables were chosen for random forest classification, with the accuracy of the classification results assessed. The study indicates that: ①The CLAHE feature variables emphasize the tonal variations among ground objects, thereby enhancing the classification accuracy, with the overall accuracy increased by 2.7 percentage points and the Kappa coefficient increased by 0.035; ②The classification results for granite pegmatites based on GF-2 images and the random forest algorithm exhibited overall accuracy of 93.1%, with a Kappa coefficient of 0.902, user accuracy of 94.24%, and producer accuracy of 98.00%, confirming the effectiveness of the method used in this study. Moreover, this study provides reliable data for future research in the study area.
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