基于CBERS-1图像的干旱半干旱区土地利用分类

Landuse Classification in Arid and Semi-Arid Areas Using CBERS-1 Imagery

以中巴资源卫星CBERS 1图像数据为信息源,分别采用最大似然法、BP神经网络和Fuzzy ARTMAP神经网络 3种分类器,以位于干旱区的中国新疆石河子地区为例,进行了土地利用计算机自动分类。结果认为,3种方法中以Fuzzy ARTMAP神经网络法分类精度最高,分别比最大似然法和BP神经网络法提高了 1 0.69%和 6.84%。同时也证实了CBERS

Discussed and analyzed results of different classification algorithms for land use classification in arid and semiarid areas using CBERS 1 image, Which in case of our study is Shihezi Municipality, Xinjiang Province. Three types of classifiers are included in our experiment, including the Maximum Likelihood classifier, BP neural network classifier and Fuzzy ARTMAP neural network classifier. The classification results showed that the classification accuracy of Fuzzy ARTMAP was the best among three classifiers, increased by 10.69% and 6.84% than Maximum likelihood and BP neural network, respectively. Meanwhile, the result also confirmed the practicability of CBERS 1 image in land use survey.