Robust High Resolution Image from the Low Resolution Satellite Image

Abstract

Abstract — In this paper, we propose a framework detecting and locating the land cover classes from a low-resolution image, which can play a very important role in the satellite surveillance image from the MODIS data. The lands cover classes by constructing superresolution images from the MODIS data. The highest resolution of the MODIS images is 250 meters per pixel. By magnifying and de-blurring the low-resolution satellite image through the kernel regression. SR reconstruction is image interpolation that has been used to increase the size of a single image. The SRKR algorithm takes a single low-resolution image and generates a de-blurred high-resolution image. We perform bi-cubic interpolation on the input low-resolution image (LR) with a desired scaling factor. Finally, the KR model is then used to generate the de-blurred HR image. K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem, which generates a specific number of disjoint, flat (non-hierarchical) clusters. K-means clustering is employ in order to compare MODIS data and recognize land cover type, i.e., “Forest”, “Land”, “sea”, and “Ice”. Index Terms — Satellite LR Image, Super-Resolution Image, MODIS Dat