Registration Techniques for Multisensor Remotely Sensed Imagery

Image registration is one of the basic image processing operations in remote sensing. With the increase in the number of images collected every day from different sensors, automated registration of multisensor/multispectral images has become a very important issue. A wide range of registration techniques has been developed for many different types of applications and data. Given the diversity of the data, it i s unlikely that a single registration scheme will work satisfactorily for all different applications. A possible solution is to integrate multiple registration algorithms into a rule-based artificial intelligence system so that appropriate methods for any given set of multisensor data can be automatically selected. The first step in the development of such an expert system for remote sensing application would be to obtain a better understanding and characterization of the various existing techniques for image registration. This is the main objective of this paper as we present a comparative study of some recent image registration methods. We emphasize in particular techniques for multisensor image data, and a brief discussion of each of the techniques is given. This comprehensive study will enable the user to select algorithms that work best for hidher particular application domain. Introduction Image registration is the process of matching two images so that corresponding coordinate points in the two images correspond to the same physical region of the scene being imaged. It is a classical problem in several image processing applications where it is necessary to match two or more images of the same scene. Some examples of its applications are: Integration of information taken from different sensors (sensor or image fusion problem). In remote sensing, a great number of sensors for global monitoring are available, each of them with different spectral, spatial, and radiometric characteristics. It is useful to combine and analyze the image data to take advantage of their characteristics and improve the information extraction process. For example, the combination of images obtained from SPOT and Landsat Thematic Mapper (TM) satellites has been used in applications such as monitoring urban growth. SPOT images present better spatial resolution than do the TM images while the TM images have better multispectral resolution. The Intensity-Hue-Saturation transformation (IHS) can be used to merge the SPOT panchromatic band with TM mulCenter for Information Processing Research, Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106. L.M.G. Fonseca is with the Instituto Nacional de Pesquisas Espaciais, Av. dos Astronautas 1758, 12227-010, SBo Jos6 dos Campos, SP, Brazil. tispectral bands and generate another color enhanced image with high spatial resolution (Carper, 1990). The alignment of the images is the first step in this data transformation. Another example is combining optical and radar images. Radar images are not affected by clouds and weather conditions, and provide important complementary information about the region surveyed. For example, synthetic aperture radar (SAR) data from the Shuttle Imaging Radar-C (SIR-C) and Japanese Earth Resources Satellite-1 (JERS-I) data combined with TM optical sensor data have been used to map floodplain inundation and vegetation in the Manaus area of Brazil (Melack, 1994). SAR sensors are uniquely suited to measure floodplain inundation because they can detect flooding underneath vegetation, and they operate independently of cloud cover or solar illumination, while TM data provides additional information from the optical portion of the spectrum. The problem is that these sensors are on different platforms and in different orbits, each having different characteristics, viewing geometries, and data collection and * processing systems. This makes it necessary to register the images prior to their analysis. Analysis of changes in images taken at different times (temporal registration and change detection). In multitemporal image analysis, the objective is to detect changes which have occurred over a certain time period. A simple method to find changes in a pair of images is to overlay the images and detect the differences between them. Because these images are taken at different times and under different conditions, they have to be aligned prior to comparative processing. In computer vision, registration is necessary in extracting structure from motion, electronic image stabilization, and object recognition. Other problems such as finding cloud heights, satellite image composite generation, weather prediction, and wind direction measurements also involve the registration process. Two examples of image registration are shown in Figures 1 and 2. Figures l a and l b show balloon images from a Mojave Desert sequence taken with a CCD camera. They were part of a motion sequence with the camera attached to a floating balloon. Figure l c shows the mosaicking of Figures l a and l b after registering them. Figure 2 illustrates the multisensor registration of Landsat TM and SPOT images. As the SPOT images have higher spatial resolution than Landsat TM, the features appear at different scales and registration is necessary to integrate their information. Matching of the SPOT image after the transformation is shown in the Figure 2c. Photogrammetric Engineering & Remote Sensing, Vol. 62, No. 9, September 1996, pp. 1049-1056. 0099-1112/96/6209-1049$3.00/0 O 1996 American Society for Photogrammetry and Remote Sensing PE&RS September 1996