Abstract: Under the complex coastal land environment, how to improve the extraction accuracy of land cover information by remote sensing is a key problem. This paper, taking southern coastal land of Hangzhou Bay as the study area, using most newly launched GF-1 satellite and Resources satellite No.3 remote sensing images extract the land cover information by the object-oriented classification method compared with maximum likelihood method. The results show that comprising with the maximum likelihood method, the object oriented method with GF-1 image are more suitable for extraction of coastal land information. It not only considers the object spectral, spatial and texture features, but also makes full use of the rich texture and spatial information of GF-1 image, which has better recognition ability for various land types, distribution of fuzzy coastal land boundary of mixed pixels, thus getting higher classification accuracy of 90.4% and Kappa coefficient of 0.8767. The choice of segmentation scale is an important influence on high precision image classification, and the results show that image segmentation optimal scales of GF-1 2 m and 8 m images are 63% and 65%, and the optimal segmentation scale of Resources No.3 images is 66%. Comprising with resources satellite No.3, GF-1 image can reflect more advantages in land cover information extraction in the aspects of ground vegetation and water.