面向对象的无人机影像地物分类

为了对农村用地进行有效分类，本文选取面向对象的分类方法，利用某农村的无人机航摄影像提取其土地类别信息。首先对无人机获取的原始影像进行预处理；然后对研究区反复进行分割实验，选取最优的分割尺度，在不同层次进行最优尺度地物分割；最后根据地物矢量、光谱、形状等特征差异，对最优分割尺度层上的地物进行最适宜的分类规则的建立，进而在每一层提取土地利用信息。利用单一尺度分割分类进行对比实验，选取734个样本进行精度验证，研究结果表明：多尺度多层次分割分类的总体分类精度可达84.20%，kappa系数为0.806 2；单一尺度分割分类总体精度仅为77.11%，kappa系数为0.721 4。由此可见，本文研究所采用的数据和区域内的类别的分类精度更高。

Land-Use Information of Object-Oriented Classification by UAV Image

In order to effectively classify the rural land, an object-oriented classification method is selected to extract the land classification information of drone aerial photography images. First, original drone-taking images are preprocessed, then by repeatedly performing segmentation tests on the study area, the optimal segmentation scale of each feature is selected, with which the images are segmented at different levels. And based on feature differences in feature vector, spectrum, shape, etc., the most suitable classification rules are established for the features on the optimal segmentation scale layer. Accordingly, the land use information of each layer can be extracted. Experimental results with 734 samples for accuracy verification show that the overall classification accuracy of multi-scale and multi-level segmentation classification reaches 84.20%, and the kappa coefficient is 0.8062, whereas the overall accuracy of single-scale segmentation classification is only 77.11%, and the kappa coefficient is 0.7214. It shows that the data used in this study and the classification accuracy of the categories inside the region are higher.