论文标题
S2A：Wasserstein gan具有多光谱带合成的时空光谱laplacian的关注
S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attention for Multi-Spectral Band Synthesis
论文作者
论文摘要
对抗性学习与卫星图像处理的交集是遥感中的新兴领域。在这项研究中，我们打算使用对抗性学习来解决高分辨率多光谱卫星图像的合成。在发现注意机制的指导下，我们通过时空光谱laplacian的注意来调节谱带合成的过程。此外，我们将Wasserstein Gan带有梯度惩罚规范来改善对抗性学习的训练和稳定性。在这方面，我们基于空间注意和域的适应损失为歧视者引入了新的成本函数。与最先进的评估指标相比，我们对定性和定量结果进行了批判性分析。我们在三个不同传感器的数据集上进行的实验，即LISS-3，LISS-4和Worldview-2表明，注意力学习对最新方法的表现良好。使用所提出的方法，我们提供了与现有高分辨率频段一致的附加数据产品。此外，我们综合了4000多个高分辨率场景，涵盖了各种地形，以分析科学保真度。最后，我们展示了合成频段的合理大规模现实世界应用。
Intersection of adversarial learning and satellite image processing is an emerging field in remote sensing. In this study, we intend to address synthesis of high resolution multi-spectral satellite imagery using adversarial learning. Guided by the discovery of attention mechanism, we regulate the process of band synthesis through spatio-spectral Laplacian attention. Further, we use Wasserstein GAN with gradient penalty norm to improve training and stability of adversarial learning. In this regard, we introduce a new cost function for the discriminator based on spatial attention and domain adaptation loss. We critically analyze the qualitative and quantitative results compared with state-of-the-art methods using widely adopted evaluation metrics. Our experiments on datasets of three different sensors, namely LISS-3, LISS-4, and WorldView-2 show that attention learning performs favorably against state-of-the-art methods. Using the proposed method we provide an additional data product in consistent with existing high resolution bands. Furthermore, we synthesize over 4000 high resolution scenes covering various terrains to analyze scientific fidelity. At the end, we demonstrate plausible large scale real world applications of the synthesized band.
