MIRACLE: multi-satellite Island image registration using anisotropic coherence locality enhanced nonlinear diffusion and Mahalanobis distance guided marginalization

Abstract

Feature in an image plays a crucial role for geometric image registration. The geo-registration problem becomes difficult for scanty feature islands’ images captured by high and medium spatial resolution remote sensing satellites over deep ocean water. The article presents an automatic multi-satellite image registration methodology for scanty feature island scenes, termed as MIRACLE. In data pre-processing stage, the multi-spectral and reference image are enhanced using anisotropic coherence for better localized feature demarking of island regions. The input multi-spectral images are transformed using Principal Component Analysis (PCA) to maximize the variance information for improved feature matching with reference image. Enhanced features are detected and described using nonlinear diffusion filtering, and the matched control points are pruned using Mahalanobis distance guided marginalization optimization technique. The estimated affine parameters are applied to generate multi-satellite co-registered data products. MIRACLE is evaluated with multi-temporal Indian Resourcesat multi-spectral images and NASA-USGS Landsat−8 OLI panchromatic images that span from 5.0-metre spatial resolution to 15.0-metre spatial resolution and cover the Lakshadweep islands in deep ocean. The visual quality assessment indicates that different island regions are aligned at sub-pixel level registration accuracy. The matching accuracy of MIRACLE is quantified for multi-resolution images and is found to have 2.6% improvement in Correct Matching Ratio (CMR) as compared to the state-of-the-art feature based image registration techniques. The average Root Mean Square Error (RMSE) of island regions after precise geometric correction is found to be 0.45 pixel.