基于面向对象的遥感影像分类研究——以河北省柏乡县为例

Classification of Remote Sensing Image based on Object-oriented Method:A Case Study of Baixiang County

遥感作为提取土地覆盖类型的主要手段对监测土地利用变化和制定国家政策具有重要意义。通过利用影像的光谱、形状和纹理信息,面向对象分类方法能够比基于像元的分类方法提供更高精度的数据。基于高分一号卫星数据提出一种自动计算最优尺度的方法,基于多尺度分割和3种监督型机器学习算法对研究区典型地物类型(农田、裸地、居民区和道路)进行面向对象分类,并用总体精度和Kappa系数对分类结果进行精度评价,分析了分类精度与训练样本占总样本比例的关系。研究表明,面向对象分类方法在训练样本占总样本比例较小的情况下就可以取得较高的分类精度,总体精度高于94%。总体来看,支持向量的分类精度比神经网络和决策树的分类精度高。

Remote sensing is the main means of extracting land cover types, which has important significance for monitoring land use change and developing national policies. Object-based classification methods can provide higher accuracy data than pixel-based methods by using spectral, shape and texture information.In this study,we choose GF-1 satellite＇s imagery and proposed a method which can automatically calculate the optimal segmentation scale. The object-based methods for classifying four typical land cover types are compared using multi-scale segmentation and three supervised machine learning algorithms. The relationship between the accuracy of classification results and the training sample proportion is analyzed and the result shows that object-based methods can achieve higher classification results in the case of small training sample ratio,overall accuracies are higher than 94 %.Overall, the classification accuracy of support vector machine is higher than that of neural network and decision tree during the process of object-oriented classification.