Semantic Segmentation of High Resolution Remote Sensing Image Based on Batch-Attention Mechanism

Deep convolution neural network has been widely used in recent works for semantic segmentation of High Resolution Remote Sensing(HRRS) images. Because of the limitation of GPU memory, HRRS images are usually split into several sub-images for training convolutional neural networks. For each sub-image, the segmentation model may not have enough information to predict the segmentation map very well. In order to alleviate this problem, we propose to apply a batch-attention module to capture the discriminative information from similar objects, which come from other sub-images in a mini-batch. We also utilize global attention upsample module as the decoder to provide global context and fuse high and low level information better. We evaluate our model on the Potsdam dataset and achieve 88.30% pixAcc and 73.78% mIoU.