Multi-modality and multi-sensor image registration for satellite images

Abstract

Satellite imagery provides information which is fundamental to remote sensing applications. Two of such applications are image registration and fusion of hyperspectral and multispectral imagery. Image registration is a fundamental pre-processing step to image fusion. Conjunctively, in remote sensing little previous work aimed at the registration of satellite imagery with significant scale differences and the registration of multi-modal satellite images. The aim of this work was to do a comprehensive analysis of registration techniques in remote sensing. The SIFT algorithm with different parameter sets was utilised to register thermal-thermal satellite imagery with significant scale differences. The work also examined and compared the use of other feature-based, area-based and optical flow-based techniques for the registration of multi-modal and multi-sensorial satellite imagery. The multi-modal data includes optical satellite imagery from Sentinel-2 and Landsat-8. SAR images from Sentinel-1 and thermal images from Landsat-8 and Sentinel-3. The findings of this study show that the most common type of modality utilised in the image registration of remote sensing data is Optical-Optical and synthetic aperure radar (SAR)-Optical. For the registration of thermal Landsat-8 to Landsat-8 and thermal Landsat-8 to Sentinel-3, the general pattern was that as one upscaled the sensed image, the misregistration and RMSE increased due to a higher scale difference. For the registration of SAR-Optical satellite imagery the overall best performing was SIFT-Flow. For the registration of single modality data, the overall best was SIFT followed by the Enhanced Correlation Coefficient (ECC). For the registration of multi-modal satellite imagery, the overall best was SIFT.