Paper Info
Title
Rigorous Line-Based Transformation Model Using the Generalized Point Strategy for the Rectification of High Resolution Satellite Imagery.
Abstract
High precision geometric rectification of High Resolution Satellite Imagery (HRSI) is the basis of digital mapping and Three-Dimensional (3D) modeling. Taking advantage of line features as basic geometric control conditions instead of control points, the Line-Based Transformation Model (LBTM) provides a practical and efficient way of image rectification. It is competent to build the mathematical relationship between image space and the corresponding object space accurately, while it reduces the workloads of ground control and feature recognition dramatically. Based on generalization and the analysis of existing LBTMs, a novel rigorous LBTM is proposed in this paper, which can further eliminate the geometric deformation caused by sensor inclination and terrain variation. This improved nonlinear LBTM is constructed based on a generalized point strategy and resolved by least squares overall adjustment. Geo-positioning accuracy experiments with IKONOS, GeoEye-1 and ZiYuan-3 satellite imagery are performed to compare rigorous LBTM with other relevant line-based and point-based transformation models. Both theoretic analysis and experimental results demonstrate that the rigorous LBTM is more accurate and reliable without adding extra ground control. The geo-positioning accuracy of satellite imagery rectified by rigorous LBTM can reach about one pixel with eight control lines and can be further improved by optimizing the horizontal and vertical distribution of control lines.
Year
DOI
Venue
2016
10.3390/rs8090743
REMOTE SENSING
Keywords
Field
DocType
High Resolution Satellite Imagery (HRSI),Line-Based Transformation Model (LBTM),rigorous affine transformation,image rectification,geo-positioning accuracy analysis
Least squares,Computer vision,Rectification,Satellite imagery,Image rectification,Digital mapping,Remote sensing,Terrain,Feature recognition,Artificial intelligence,Pixel,Geology
Journal
Volume
Issue
ISSN
8
9
2072-4292
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Kun Hu1115.64
Wenzhong Shi277886.23