THE GEOMETRIC CORRECTION MODEL BASED ON AREAL FEATURES FOR MULTISOURCE IMAGES RECTIFICATION

• Main Authors: T. Long, W. Jiao
• Format: Article
• Language: English
• Published: Copernicus Publications 2012-07-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B1/245/2012/isprsarchives-XXXIX-B1-245-2012.pdf

The geometric correction model describes the relationship between the features in image and object space. The majority models rely heavily on point-based features to do the rectification. Although it is simple, intuitional and accurate, there are some problems in establishing the accurate model based on point features. In many cases, it is difficult to acquire accurate ground control points in the areas where cross points and corners are not available. On the other hand, image registration based on feature points is greatly confined to the image acquisition time, resolution and spectrum. Due to the above limitations, linear and areal features are used as control features in order to cope with the misidentification problem of points and to register image accurately. This paper proposes a novel geometric correction model based on areal features, which is not confined to a specific imaging geometric model, and can be used for multisource image rectification. The definitions and algorithms of the distance between the areal features and how to establish the error equations using ground control areas (GCAs) are proposed, and geometric correction based on GCAs is achieved. Landsat and ALOS images are used as examples for the test. The experimental results show that the proposed method can be used for different satellite images and imaging models. The correction accuracy when using GCAs is better than one pixel, when the control data does not contain gross errors. The area-based geometric correction model is more fault-tolerant than the point-based model when the control data contains gross errors.