Abstract: 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 utilized. 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) approach. 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 recognition and improve classification accuracy through applying CART approach.

Key words: environmental satellite, radar image, image fusion, CART, SVM, image classification