This paper addresses the issue on automated registration of images from weather satellites. Traditionally, weather satellite community has employed an approach called landmark detection for automated registration. A ground point or feature with known reference coordinates is defined as landmark. A landmark is matched against a weather satellite image. Based on match results estimated is the mapping function between images and a reference datum. This landmark detection approach has been suffered from the problem of mismatches. If match results contain errors, the accuracy of estimation deteriorates. To overcome this problem, we propose the use of a robust estimation technique called randam sample consensus (RANSAC). Through intelligent strategy this robust estimator will distinguish inliers from outliers and establish the mapping function with inliers only. This estimator has been reported to work in land observation satellite applications as well as in many computer vision applications. We will show that the RANSAC can also work for our purpose. We tested our approach using a global coastline database anda GOES-9 image. A global coastline database was processed to generate 30 landmarks. They were matched against a GOES-9 image. Visible inspection revealed that the results contained 13 mismatches. With 30 match reults the RANSAC was applied. It identified all 13 mismatches correctly. We can conclude that the RANSAC is able to select correct matches. For reliable automated registration, the RANSAC needs to be incorporated in the landmark detection process of weather satellite images.
A semi-automatic road extraction method from high-resolution (1-m) satellite images is presented. As IKONOS, a high-resolution (1-m) satellite has been launched and several companies have plans to launch high-resolution satellites, extraction of man-made objects from high-resolution satellite images has been main concern of many scientists. The method consists of three phases; 1) NUBS (Non Uniform B-Spline) curve is formed by given seed points. 2) A road candidate area is made by straightening image along the NUBS curve. 3) Finally, road is extracted by a tracking algorithm which uses adaptive least squares correlation match method and linearity. Due to straightening image, the tracking algorithm extracts roads accurately even though there are road gaps, and the size of a matrix for least squares correlation match can be reduced. We test our method on high-resolution (1-m) satellite (IKONOS) image. The test result reveals our method is robust and can be one of the feasible solutions of mapping from high-resolution (1-m) satellite images.
The built environment constitutes a very important target for automated image understanding systems. Currently, these environments only have manually surveyed 2D vector information in paper or digital map form. Increasingly, there are many requirements for 3D information. Automated systems are the only viable way to acquire this information over vast areas with a temporal frequency which will keep pace with the rate of change of many of these areas. Most published image understanding techniques for the automatic extraction of man-made objects only address a specific class of scene at a specific resolution. To address the full range of circumstances which will be required and make a technique more robust, it should be applied to multi-resolution images. In this paper, two automated systems one for building height extraction (stereoscopic) and one for building detection from monoscopic images are applied to multi-resolution aerial and spaceborne imagery. These systems were originally developed with for 0.15 m resolution inner city urban area imagery. With 0.24 m resolution suburban imagery, they performed very successfully. With 0.85 m resolution urban imagery containing very complicated buildings, they show promising results. A 2 m resolution Russian DD5 image was also tested with the monoscopic building detection system and the results showed automatic extraction of large industrial buildings is possible with such imagery. In summary, it was shown that these fully automated systems can handle images with various resolutions and environments.
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