Semantic Segmentation of Remote Sensing Imagery Based on Improved Squeeze and Excitaion Block

Aiming to solve the semantic recognition error of traditional methods in semantic segmentation of remote sensing imagery with complex background, we propose a simple but effective convolutional attention module, region squeeze and excitation block (RSE-block), based on squeeze and excitation block (SE-block). This block can squeeze regional context information of features, guides the network to screen more important features and excite features expression in both spatial and channel dimensions. In addition, it can be added to any convolutional neural network and trained end-to-end with the network. Meanwhile, we propose a multi-scale integration method supported by this block to solve the recognition problem of different size ground objects, and a new semantic segmentation network, RSENet, is constructed on these bases. The experimental results show that RSENet is superior to the baseline in terms of mean F1-score and mean intersection over union by 0. 028 and 0. 021 respectively on the Potsdam dataset, and is more competitive with some current advanced methods.