Using Deep Convolutional Features for MultiTemporal Remote Sensing Image Registration

Abstract

Image Registration is a fundamental part of image classification algorithm, for example, two or more images taken from various sensors or from different points of view at different times and environmental conditions may lead to degradation of accuracy factor during classification of any traditional algorithms. Image Registration will help us to minimize tedious task to select features in such a case. Specific examples of systems in which image registration is an important component include matching a target with a real-time image of a target scene, monitoring global land use[4], geographical maps using satellite images[5], matching stereo images to recover autonomous navigation shape, and aligning pictures from different diagnostic medical modalities etc. In this paper, we propose a flexible and dynamic algorithm, which is a fusion of DeepIRDI (Image Registration Using Deep Convolutional Features and Dynamic Inlier Selection) with MS – MA (multi-scale multi-angle) and CNN (Convolutional Neural Network)[1] architecture for both rigid and non-rigid feature matching of remote sensing images. The performance of this technique is determined by using various datasets, and the results are evaluated using the hybrid DeepIRDI algorithm. For getting better and optimal result we will modify existing DeepIRDI with some hybrid features selection technique and performance analysis will be done using NVDIA GPU.