A Generic, Multimodal Geospatial Data Alignment System for Aerial Navigation

Abstract

We present a template matching algorithm based on local descriptors for aligning two geospatial products of different modalities with a large area asymmetry. Our system is generic with regards to the modalities of the geospatial products and is applicable to the self-localization of aerial devices such as drones and missiles. This algorithm consists in finding a superposition such that the average dissimilarity of the superposed points is minimal. The dissimilarity of two points belonging to two different geospatial products is the distance between their respective local descriptors. These local descriptors are learned. We performed experiments consisting in estimating a translation between optical (Pléiades) and SAR (Miranda) images onto vector data (OpenStreetMap), onto optical images (DOP) and onto SAR images (KOMPSAT-5). Each remote sensing image to be aligned covered 0.64 km2, and each reference geospatial product spanned over 225 km2. We conducted a total of 381 alignment experiments, with six unique modality combinations. In aggregate, the precision reached was finer than 10 m with 72% probability and finer than 20 m with 96% probability. This is considerably more than with traditional methods such as normalized cross-correlation and mutual information.

Keywords

• geospatial data fusion
• deep learning
• multimodality
• local descriptors
• image alignment
• SAR