基于多源遥感影像融合的武汉市土地利用分类方法研究

doi:10.11870/cjlyzyyhj201610014

Abstract

Abstract: It is of great significance to acquire accurate and efficient land use information for rational use and development of land resources. In areas undergoing rapid urbanization, land use activity is frequent and intensive, and the land use pattern changes very sharply, which increases the uncertainty of the precise classification of urban land use. At the same time, the impact of environment and weather conditions increases the difficulty of obtaining effective optical images. In order to improve the precision of the urban land classification, in this paper we selected the city center area of Wuhan as a case and took Sentinel-1A and Landsat 8 OLI images as the data source, using the Gram-Schmidt transformation method for image fusion. We selected four classification methods to classify the fusion image, including maximum likelihood, support vector machine, CART decision tree and BP neural network to extract the information of land use in the study area. Further, by comparing with the results of the classification of the optical image, we explored whether the Sentinel-1 A and Landsat8 OLI fusion image had the advantage in terms of land use information extraction. The research results showed that:(1) compared with the other 3 methods, the CART decision tree classification method had the highest classification accuracy for the fused image, the overall classification accuracy and Kappa coefficient reached 88.55% and 0.8414; (2) compared with the optical image, Sentinel-1A and Landsat 8 OLI fusion image can obtain high precision urban land use information more effectively; (3) the CART decision tree classification method based on multi source remote sensing image fusion was an effective technique to obtain the high precision land use information in the research area. The results can provide reference for land use classification in the rapid urbanization region.

Key words: urban land use classification, image fusion, Sentinel-1A, Landsat8 OLI, Wuhan City

CLC Number:

TP79

ZHAI Tian-lin, JIN Gui, DENG Xiang-zheng, LI Zhao-hua, WANG Run. RESEARCH OF WUHAN CITY LAND USE CLASSIFICATION METHOD BASED ON MULTI-SOURCE REMOTE SENSING IMAGE FUSION[J].RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2016, 25(10): 1594-1602.

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RESEARCH OF WUHAN CITY LAND USE CLASSIFICATION METHOD BASED ON MULTI-SOURCE REMOTE SENSING IMAGE FUSION

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