A new asymmetrical corner detector(ACD) for a semi-automatic image co-registration scheme

Co-registration of multi-sensor and multi-temporal images is essential for remote sensing applications. In the image co-registration process, automatic Ground Control Points (GCPs) selection is a key technical issue and the accuracy of GCPs localization largely accounts for the final image co-registration accuracy. In this thesis, a novel Asymmetrical Corner Detector (ACD) algorithm based on auto-correlation is presented and a semi-automatic image co-registration scheme is proposed. The ACD is designed with the consideration of the fact that asymmetrical corner points are the most common reality in remotely sensed imagery data. The ACD selects points more favourable to asymmetrical points rather than symmetrical points to avoid incorrect selection of flat points which are often highly symmetrical. The experimental results using images taken by different sensors indicate that the ACD has obtained excellent performance in terms of point localization and computation efficiency. It is more capable of selecting high quality GCPs than some well established corner detectors favourable to symmetrical corner points such as the Harris Corner Detector (Harris and Stephens, 1988). A semi-automatic image co-registration scheme is then proposed, which employs the ACD algorithm to extract evenly distributed GCPs across the overlapped area in the reference image. The scheme uses three manually selected pairs of GCPs to determine the initial transformation model and the overlapped area. Grid-control and nonmaximum suppression methods are used to secure the high quality and spread distribution of GCPs selected. It also involves the FNCC (fast normalised crosscorrelation) algorithm (Lewis, 1995) to refine the corresponding point locations in the input image and thus the GCPs are semi-automatically selected to proceed to the polynomial fitting image rectification. The performance of the proposed coregistration scheme has been demonstrated by registering multi-temporal, multi-sensor and multi-resolution images taken by Landsat TM, ETM+ and SPOT sensors. Experimental results show that consistent high registration accuracy of less than 0.7 pixels RMSE has been achieved. Keywords: Asymmetrical corner points, image co-registration, AC

A Knowledge Based Approach to Automatic Image Registration

The presented work addresses the problem of automatic control point matching for the registration of remotely sensed images. The inaccuracy of flight parameters and the sensor specific appearance of objects are the difficulties automatic registration suffers from. To overcome these problems the presented system uses prior knowledge to select appropriate structures for matching, i.e. control points, from a GIS and to extract their corresponding features from the sensor data. The knowledge is represented explicitly using semantic nets and rules. The best correspondence between the GIS data and the image is found by an A*--Algorithm. The automatic control point matching is demonstrated for crossroads in aerial and SAR imagery. 1. Introduction The evaluation of remotely sensed images from multiple sensors requires the registration of all data in a common (geographic) coordinate system. This is especially true for multiple sensors that differ in geometry, spectrum, and time. Prerequisite f..