基于改进OTSU算法的采煤沉陷耕地作物绝产边界识别

Recognition of out-of-production boundary of crops in mining subsidence arable land based on improved OTSU algorithm

采煤沉陷耕地作物生长状况是确定土地损毁程度进而影响土地复垦难度和复垦方案选择的关键,也是矿山企业赔付的依据。近年来,随着计算机图像处理技术的逐渐成熟,使得图像处理与识别技术得到了广泛应用,采用图像识别方法确定采煤沉陷耕地作物绝产边界,可降低野外实测工作量、减小人为因素的影响。基于不同生长状况作物在高度上的差异会表现为数字表面模型上的高程差异这一原理,将作物高度差异作为绝产边界的表征指标。利用无人机遥感获取高分辨率影像,经Pix4Dmapper软件自动化处理后得到数字表面模型(DSM);利用作物高程作为图像分割阈值,对最大类间方差(OTSU)阈值分割算法进行改进,包括高程数据离散化和改变灰度运算为高程运算2个方面,结合Canny算子进行边缘检测,提出了一种采煤沉陷耕地作物绝产边界识别方法,并以山东省济宁市某采煤沉陷区为研究区做方法验证。结果表明:改进的最大类间方差阈值分割算法可以对图像的高程进行运算,并能迭代出最优的高程分割阈值;利用高程分割阈值对数字表面模型(DSM)进行分割,经边缘检测后可识别出采煤沉陷耕地作物绝产边界;通过对研究区正射影像图(DOM)取样验证,该方法识别的采煤沉陷耕地作物绝产边界较精确,验证了方法的有效性。

The crop growth status of mining subsidence farmland is the key to determine the extent of land damage and then affect the difficulty of land reclamation and the choice of reclamation plan,and also the basis for compensation of mining enterprises. Using image recognition method to determine the crop yield boundary of mining subsidence farmland can reduce the workload of field measurement and the influence of human factors. Based on the principle that the height difference of crops in different growth conditions can be expressed as the height difference of digital surface model,the height difference of crops was taken as the indicator of yield boundary. Using UAV to obtain high-resolution image and digital surface model( DSM). Using crop elevation as the threshold of image segmentation improved OTSU algorithm,it included two aspects:the discretization of elevation data and the change from gray value operation to elevation value operation.Combined with Canny operator edge detection,proposed a method of recognition out-of-production boundary in mining subsidence farmland. And taken a mining subsidence area in Jining City of Shandong Province as the research area to verified the method.The study results showed that the improved OTSU algorithm could calculate the elevation value of image,and iterated the optimal threshold value of elevation segmentation. The digital surface model( DSM) was segmented by the threshold value of elevation segmentation,and the out-of-production boundary could be recognition by edge detection algorithm. Through the verification of DOM sampling in the study area,the crop yield boundary recognized by this method was accurate,the effectiveness of method was proved.