  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.
