Abstract: In order to quickly find out the number of houses built on arable land illegally occupied and realize the detection of houses built on encroached farmland,a unified attention fusion network is proposed to identify houses built on encroached farmland.In order to solve the problem of mutual influence of remote sensing image features in different phases,the network firstly uses siamese network instead of VGG16 network for feature extraction.Secondly,in order to reduce the size of network model on the premise of increasing the receptive field of network and obtaining more multi-scale information,simple pyramid pooling module is used at the bottom layer of coding stage.In order to improve the segmentation accuracy,highlight the useful features and use the unified attention fusion module to replace the original upsampling part for decoding to obtain the change detection results.The network is trained and tested on the data set of building houses on encroached farmland.Experimental results show that the unified attention fusion network has an accuracy rate of 98.82%,precision of 89.69%,recall rate of 82.14%,and F1 score of 85.74% on the test set.It can quickly identify illegal houses suspected of occupying farmland at different scales,and provide a technical detection method for the construction of houses in rural areas.

Key words: Remote sensing image, Change detection;building inspection, Unified attention fusion network, Simple pyramid pooling