基于光学和雷达图像的土地覆被分类

为寻求一种有效的提高多源遥感数据土地覆被分类制图精度的方法，探讨了融合 HJ1B 和 ALOS ／PALSAR图像进行遥感图像分类制图的方法。在对光学图像 HJ1B 和雷达遥感数据 ALOS ／PALSAR 进行离散小波融合的基础上，应用分类决策树 CART（Classification and Regression Tree）算法对融合的图像进行了土地覆被分类制图，并将其分类结果与支持向量机 SVM（Support Vector Machine）分类结果进行对比。研究结果表明：将光学和雷达图像数据进行离散小波融合，采用分类决策树 CART 和支持向量机 SVM进行图像分类，CART 的分类精度要优于 SVM的结果。可见，在光学图像 HJ1B 和雷达数据 ALOS ／PALSAR 融合的基础上，应用 CART 能有效进行地物识别，提高图像的分类精度。

Land-cover Classification Based on HJ1B and ALOS Data

In order to increase the accuracy of the land use and land cover (LULC)classification via multi-source remote sensing data,we explored an effective algorithm by fusion of HJ1B images from optical sensors and ALOS /PALSAR data from radar remote sensing.In the process of fusion,the discrete wavelet transform (DWT)was uti-lized.The land-cover classification mapping was performed by using the classification and regression tree (CART) approach.The classification result by CRT approach was compared with that by support vector machine (SVM)ap-proach.The results show that:1)through fusing HJ1B optical images with ALOS /PALSAR radar data,we obtain an overall Kappa coefficient (0.826 9)and total accuracy(85.60 %)by CRT approach,while by SVM approach the value is 0.816 7 and 84.82 %,respectively;2)in terms of classification accuracy,CRT approach is superior to SVM approach;3)by means of fusing optical images with radar data ,we can effectively carry out object recogni-tion and improve classification accuracy through applying CART approach.