基于影像与坡度数据融合的梯田田块分割方法

梯田在很大程度上开发了坡耕地的农业生长潜力,具有蓄水、保土作用。由于梯田数量、面积等分布信息较难准确获得,使其定量研究难以深入展开。随着无人机技术的不断发展,高精度梯田地形信息的获取成为可能。本文基于无人机正射影像并结合坡度数据,通过Canny边缘检测算子对梯田的粗轮廓进行提取,结合梯田的结构特性,对梯田中的伪边缘进行剔除;再通过对梯田边缘强度叠加和边缘连接;最后利用区域生长算法对梯田进行分割。该方法有效解决了梯田形状不规则、田面堆积物干扰、图像光谱特征复杂等问题。与手工标注的梯田样区田块数据的对比结果表明,本文算法对梯田区的提取总精度可达84.9%,可为梯田区的快速制图提供解决方案。

Segmentation Method of Terraced Fields Based on Image and Gradient Data

Terraced fields are a kind of soil and water conservation measures explored by humans on sloping fields. The construction of terraces largely develops the agricultural growth potential of sloping arable land, which has the functions of water storage and soil conservation. Due to the difficulty in obtaining information such as the number of terraces and distribution of area, it is difficult to carry out the quantitative research on the terraced fields. With the continuous development of unmanned aerial vehicle (UAV) technology, it becomes possible to access high-precision terrain information. Based on the UAV orthorectified images and slope data calculated by digital elevation model (DEM), the rough contour of terraced fields was extracted by Canny edge detection operator, and the false edges of terraced fields were removed according to the structural characteristics of terraced fields. According to edge strength superposition and edge connection operation, the terraces were divided by region growing algorithm. The method effectively solved the problems of uneven terraced fields in the hilly areas, interference of the surface sediments and complicated spectral characteristics of the images. Compared with the field data of terraced plots marked by hand, the results showed that the total accuracy of the proposed algorithm in terraced fields can reach 84.9%. The research result can provide a solution for the rapid mapping of terraced fields.