Journal:International Journal of Applied Earth Observation and Geoinformation

Abstract:Existing methods for matching multi-modal remote sensing images (MRSI) demonstrate considerable adapt ability. However, high-precision matching for rectification remains challenging due to differing imaging mechanisms in cross-modal remote sensing images, leading to numerous non-repeated detailed feature points. Additionally, assuming linear transformations between images conflicts with the complex aberrations present in remote sensing images, limiting matching accuracy. This paper aims to elevate matching accuracy by imple menting a detailed texture removal strategy that effectively isolates repeatable structural features. Subsequently, we construct a radiation-invariant similarity function within a generalized gradient framework for least-squares matching, specifically designed to mitigate nonlinear geometric and radiometric distortions across MRSIs. Comprehensive qualitative and quantitative evaluations across multiple datasets, employing substantial manual checkpoints, demonstrate that our method significantly enhances matching accuracy for image data involving multiple modal combinations and outperforms the current state-of-the-art solutions in matching accuracy. Additionally, rectification experiments employing WorldView and TanDEM-X images validate our method’s ability to achieve a matching accuracy of 1.05 pixels, thereby indicating its practical utility and generalization capacity.

Co-author:Ke Xi,Huijin Fu,Lai Wei,Qiang Xiong,Qi Chen

First Author:Yifan Liao,Shuo Li

Indexed by:Journal paper

Correspondence Author:Pengjie Tao,Tao Ke

Volume:134

Issue:2024

Page Number:104186

Translation or Not:no

Date of Publication:2024-10-03

Included Journals:SCI