Registration of multiple source imagery is one of the most important issues when dealing with Earth science remote sensing data where information from multiple sensors exhibiting various resolutions must be integrated. Issues ranging from different sensor geometries, different spectral responses, to various illumination conditions, various seasons and various amounts of noise, need to be dealt with when designing a new image registration algorithm. This chapter represents a first attempt at characterizing a framework that addresses these issues, in which possible choices for the three components of any registration algorithm are validated and combined to provide different registration algorithms. A few of these algorithms were tested on three different types of datasets – synthetic, multitemporal and multispectral. This chapter presents the results of these experiments and introduces a prototype registration toolbox.
In Chapter 1, we showed how the analysis of Earth science data for applications, such as the study of global environmental changes, involves the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, spectral, and spatial resolutions. For such applications, the first required step is fast and automatic image registration which can provide precision correction of satellite imagery, band-to-band calibration, and data reduction for ease of transmission. Furthermore, future decision support systems, intelligent sensors and adaptive constellations will rely on real- or near-real-time interpretation of Earth observation data, performed both onboard and at ground-based stations.