Scene Parsing For Very High Resolution Remote Sensing Images Using On Attention-Residual Block-Embedded Adversarial Networks

A novel deep learning architecture called Attention-Residual block-Embedded Adversarial Networks (AREANs) is proposed in this letter, which can give the robust pixel-wise scene understanding in remote sensing images without any post-processing and additional data. The generator of AREANs, a novel designed encoder-decoder structure network, takes full advantage of Attention-Residual block to learn local-to-global contextual information through semantic and position information enhanced aggregation. To further improve the performance, a patchGAN-based discriminator is applied to train the generator. This training method can not only promote to mine the distinguishable and inherent features from data, but also boost the feature extraction performance of the generator through fine-tuning its parameters. Moreover, the multipath composite loss is proposed as an auxiliary loss in the generator training stage to cope with the class imbalance problem. The comparative experimental results demonstrate that our proposed AREANs can achieve better performance on both Vaihingen and Potsdam datasets.