Efficient Remote-Sensing Segmentation With Generative Adversarial Transformer

Most deep-learning methods that achieve high segmentation accuracy require deep network architectures that are too heavy and complex to run on embedded devices with limited storage and memory space. To address this issue, this letter proposes an efficient generative adversarial transformer (GATrans) for achieving high-precision semantic segmentation while maintaining an extremely efficient size. The framework utilizes a global transformer network (GTNet) as the generator, efficiently extracting multilevel features through residual connections. GTNet employs global transformer blocks with progressively linear computational complexity to reassign global features based on a learnable similarity function. To focus on object- and pixel-level information, the GATrans optimizes the objective function by combining structural similarity losses. We validate the effectiveness of our approach through extensive experiments on the Vaihingen dataset, achieving an average
$F1$
score of 90.17% and an overall accuracy (OA) of 91.92%. Codes are available at
https://github.com/qiuluyi/GATrans
.