Remote sensing satellite image，which can provide accurate location information because of instantaneity and objectivity，has been widely used in agricultural production，environmental protection and other fields． In order to solve the difficulties in recognizing the mass remote sensing satellite images，the image segmentation method based on convolutional neural networks was used to extract the typical land spectral information and spatial information in the remote sensing image and to identify the remote sensing satellite image． Firstly，the experimental data was generated by clipping remote sensing ima-ge data and annotating data． The categories in the data were counted and oversampling was used to deal with the problem of data imbalance． Then，a new image segmentation network named W-NET was proposed to train the data by adding a top-down feature pyramid structure to the U-Net framework combining with global context module． Finally，large-scale remote sensing satellite images were recognized by image overlap strategy． Compared with three popular semantic segmentation networks and two special networks for remote sensing satellite image segmentation，our method achieves 74. 7% mean IoU and 95. 1% accuracy in 2017 AI classification and recognition contest of CCF satellite images，and achieves 69. 6% precision and 79. 9% recall in Massachusetts building segmentation task． W-Net has the highest accuracy and mean IoU among the six networks． Experimental results show that the feature pyramid structure and global context module can improve the segmentation accuracy of the semantic segmentation network，and this method is feasible for remote sensing satellite image segmentation．