Efficient Remote Sensing Segmentation With Generative Adversarial Transformer
This addresses the need for efficient segmentation models for embedded devices in remote sensing applications, but it is incremental as it builds on existing transformer and GAN methods.
The paper tackled the problem of heavy deep learning models for remote sensing segmentation by proposing an efficient Generative Adversarial Transformer (GATrans), achieving an average F1 score of 90.17% and overall accuracy of 91.92% on the Vaihingen dataset.
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 paper proposes an efficient Generative Adversarial Transfomer (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 multi-level 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-level 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 of 91.92%.