论文标题
低分辨率遥感语义分段的端到端框架
An End-to-end Framework For Low-Resolution Remote Sensing Semantic Segmentation
论文作者
论文摘要
用于遥感应用程序的高分辨率图像通常是不起作用或无法访问的，尤其是在需要广泛的录音时。考虑到可以轻松访问卫星的低分辨率（LR）图像，许多遥感作品都取决于此类数据。问题在于，由于需要高质量的数据来确定此任务的准确像素预测，因此LR图像不适合语义分割。在本文中，我们提出了一个端到端的框架，该框架将超分辨率和语义分割模块团结起来，以从LR输入中产生准确的主题图。它允许语义分割网络进行重建过程，并使用有用的纹理修改输入图像。我们使用三个遥感数据集评估了框架。结果表明，该框架能够实现接近天然高分辨率数据的语义分割性能，同时还超过了接受LR输入训练的网络的性能。
High-resolution images for remote sensing applications are often not affordable or accessible, especially when in need of a wide temporal span of recordings. Given the easy access to low-resolution (LR) images from satellites, many remote sensing works rely on this type of data. The problem is that LR images are not appropriate for semantic segmentation, due to the need for high-quality data for accurate pixel prediction for this task. In this paper, we propose an end-to-end framework that unites a super-resolution and a semantic segmentation module in order to produce accurate thematic maps from LR inputs. It allows the semantic segmentation network to conduct the reconstruction process, modifying the input image with helpful textures. We evaluate the framework with three remote sensing datasets. The results show that the framework is capable of achieving a semantic segmentation performance close to native high-resolution data, while also surpassing the performance of a network trained with LR inputs.
