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In the emerging commercial space industry there is a drastic increase inaccess to low cost satellite imagery. The price for satellite images depends onthe sensor quality and revisit rate. This work proposes to bridge the gapbetween image quality and the price by improving the image quality viasuper-resolution (SR). Recently, a number of deep SR techniques have beenproposed to enhance satellite images. However, none of these methods utilizethe region-level context information, giving equal importance to each region inthe image. This, along with the fact that most state-of-the-art SR methods arecomplex and cumbersome deep models, the time taken to process very largesatellite images can be impractically high. We, propose to handle thischallenge by designing an SR framework that analyzes the regional informationcontent on each patch of the low-resolution image and judiciously chooses touse more computationally complex deep models to super-resolve morestructure-rich regions on the image, while using less resource-intensivenon-deep methods on non-salient regions. Through extensive experiments on alarge satellite image, we show substantial decrease in inference time whileachieving similar performance to that of existing deep SR methods over severalevaluation measures like PSNR, MSE and SSIM.

Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images