论文标题
RSINET：使用三重GAN框架插入远程感知的图像
RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework
论文作者
论文摘要
我们解决了遥感域中图像介绍的问题。遥感图像具有较高的分辨率和地理变化，这使常规的涂上方法降低了。这进一步需要提出高复杂性的模型的要求，以充分捕获图像中的光谱，空间和纹理细微差别，从其高空间变异性出现。为此，我们提出了一种新颖的介绍方法，该方法分别专注于图像的每个方面，例如使用特定任务的GAN等边缘，颜色和纹理。此外，每个gan还结合了明确提取光谱和空间特征的注意机制。为了确保一致的梯度流，该模型使用剩余的学习范式，从而同时使用高水平和低水平的特征。我们在两个知名的遥感数据集上评估了我们的模型，以及先前的最新模型，在画布上开放城市AI和地球，并实现竞争性能。
We tackle the problem of image inpainting in the remote sensing domain. Remote sensing images possess high resolution and geographical variations, that render the conventional inpainting methods less effective. This further entails the requirement of models with high complexity to sufficiently capture the spectral, spatial and textural nuances within an image, emerging from its high spatial variability. To this end, we propose a novel inpainting method that individually focuses on each aspect of an image such as edges, colour and texture using a task specific GAN. Moreover, each individual GAN also incorporates the attention mechanism that explicitly extracts the spectral and spatial features. To ensure consistent gradient flow, the model uses residual learning paradigm, thus simultaneously working with high and low level features. We evaluate our model, alongwith previous state of the art models, on the two well known remote sensing datasets, Open Cities AI and Earth on Canvas, and achieve competitive performance.
