An integral method,combining support vector ma-chine (SVM) with remote-sensing analysis techniques,was ex-plored to monitor Hanoi’s dynamic change of land cover. The landsat thematic mapper (TM) image in 1993,the enhanced the-matic mapper plus (ETM+) image in 2000,and the image with the charge-coupled device camera (CCD) on the China-Brazil earth resources satellite (CBERS) in 2008 were used. Six land-cover types,including built-up areas,woodland,cropland,sand,water body and unused land,were identified. The detected results showed visually the rapid urban expansion as well as land-cover change of Hanoi from 1993 to 2008. There were 12 637.54 hm2 cropland de-creased between 1993 and 2000,and 8 227.6 hm2 cropland de-creased between 2000 and 2008. Compared with cropland,wood-land firstly decreased and then increased,and the other types did not change significantly. The results indicate that CBERS dataset has the application potential in world resources researches.
ZHAO Jinling1,2, LIU Chuang1, 3 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
This paper proposed an algorithm in which the maximum probability and the weighted average strategy were used for the combination of member classifiers. Using parallel computing, we test the algorithm on a China-Brazil Earth Resources Satellite (CBERS) image for land cover classification. The results show that using three computers in parallel can reduce the classification time by 30%, as compared with using only one computer with a dual core processor. The accuracy of the final image is 93.34%, and Kappa is 0.92. Multiple classifier combination can enhance the precision of the image classification, and parallel computing can increase the speed of calculation so that it becomes possible to process remote sensing images with high efficiency and accuracy.
