Conjugate image points by an adaptive estimator for the gray‐level variance

Abstract

A least‐squares image matching algorithm was extended to adjust the different possible variances, auto‐ and cross‐covariances found in a gray‐level covariance matrix. Scaling variance and covariance components were estimated iteratively, based on some positive‐semidefinite accompanying matrices. The theory of a best linear, unbiased estimator for the scale factors has been presented in detail. Conjugate‐point accuracy results were obtained from 114 pairs of Radarsat‐1 crossroads and pond‐corner image windows. A 14% improvement in sub‐pixel matching accuracy was achieved by adhering to the proposed BLU‐Estimating algorithm, in particular for a category of the 11×11 image‐window size.

Key words:

• image matching
• variance‐component estimation
• Radarsat‐1 SAR images
• densification

Notes

Corresponding author. (Tel: [phone] ext. 57626; Fax: [phone]; Email: [email protected])

Additional information

Notes on contributors

Joz Wu

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