论文标题
带有开关引导的混合网络的单图超分辨率用于卫星图像
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images
论文作者
论文摘要
带有卫星图像的主要缺点是低分辨率，低分辨率使得难以识别卫星图像中存在的对象。我们已经尝试了几个可用于空间数据集上单图超分辨率的深层模型，并在卫星图像数据上评估了它们的性能。在过去的几年中，我们将在SISR背景下深入研究深层模型的最新演变，并将在这些模型之间进行比较研究。区域的整个卫星图像被分为等尺寸的斑块。每个补丁将独立用于培训。这些补丁在性质上会有所不同。例如，例如，由于车辆，建筑物，道路等不同类型的物体，城市地区的贴片具有非均匀的背景。另一方面，丛林上的斑块本质上会更加均匀。因此，不同的深层模型将适合不同种类的补丁。在这项研究中，我们将尝试在开关卷积网络的帮助下进一步探讨这一点。这个想法是训练开关分类器，该分类器将自动将补丁分类为最适合其的模型类别。
The major drawbacks with Satellite Images are low resolution, Low resolution makes it difficult to identify the objects present in Satellite images. We have experimented with several deep models available for Single Image Superresolution on the SpaceNet dataset and have evaluated the performance of each of them on the satellite image data. We will dive into the recent evolution of the deep models in the context of SISR over the past few years and will present a comparative study between these models. The entire Satellite image of an area is divided into equal-sized patches. Each patch will be used independently for training. These patches will differ in nature. Say, for example, the patches over urban areas have non-homogeneous backgrounds because of different types of objects like vehicles, buildings, roads, etc. On the other hand, patches over jungles will be more homogeneous in nature. Hence, different deep models will fit on different kinds of patches. In this study, we will try to explore this further with the help of a Switching Convolution Network. The idea is to train a switch classifier that will automatically classify a patch into one category of models best suited for it.
