基于CART决策树的高分二号洞庭湖区湿地分类方法

高分卫星遥感湿地分类的关键在于解决“同物异谱、异物同谱”难题。本文将当前应用前景广泛的亚米级国产高分二号（GF-2）影像和CART决策树面向对象分类算法相结合，以湖南沅江为例进行洞庭湖区典型湿地的分类提取工作，选取包括光谱信息、几何特征、地形特征和纹理特征等多维对象特征对分类器进行训练，构建了多维特征湿地分类方法。试验区总体分类精度优于传统方法，可为基于GF-2影像的洞庭湖湿地分类提供技术参考。

Wetland classification method of Dongting Lake district based on CART using GF-2 image

In order to improve the classification accuracy of wetland classification through satellite remote sensing, it is necessary to overcome the “same object with different spectrum, different object with the same spectrum” problem which exists in the wetland classification of high spatial resolution satellite image. The research explores the combination of the Chinese sub-meter-level GF-2 image, which has a broad application prospect, and the object-based image analysis classification algorithm based on classification and regression tree. Then, the classification and extraction of wetlands in the Dongting Lake district were carried out by using the Yuanjiang city in Hunan as an example. Multi-dimensional object features including spectral information, geometric features, terrain features and texture features were selected to train the classifier. Constructed a set of wetland classification methods of classification and regression tree based on GF-2 image. Accuracy evaluation data shows that the overall classification accuracy of the method reaches higher value. The results show that the method can provide ideas for the classification of Dongting Lake district wetland based on GF-2 image.