A reliable matching algorithm for heterogeneous remote sensing images considering the spatial distribution of matched features. Issue 3 (1st February 2023)

• Record Type: Journal Article
• Authors: Xue, Li; Sheng, Yehua; Liu, Yan; Zhang, Ka
• Is Part Of: International journal of remote sensing. Volume 44:Issue 3(2023)
• Page Start: 824
• Page End: 851
• Publication Date: 2023-02-01
• Subjects: Heterogeneous remote sensing images -- image matching -- proximity -- similarity measure; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2023.2171743
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Owing to the differences in sensor types, resolutions, and imaging conditions of heterologous remote sensing images, the matching results of remote sensing images, such as low accuracy, few matched pairs, and low distribution quality, are not ideal, which makes precise registration between heterogeneous images difficult. To mitigate this, we propose a reliable matching algorithm for heterogeneous remote sensing images that considers the spatial distribution of the matched features. First, feature-based matching algorithms such as the scale-invariant feature transform (SIFT) algorithm or the speeded-up robust features algorithm are used to match images to obtain an initial set of matched pairs and a set of candidate features. Then, according to the stability of the spatial distribution of locally correctly matched features, the distance and angular proximity between matched features and their neighbours are calculated to obtain the accuracy of the matched pairs and remove incorrectly matched pairs. Finally, the random sample consensus (RANSAC) algorithm was used to fit the transformation model between images, and the final matched feature selection algorithm and automatic transformation error algorithm were used to detect candidate features to increase the number of matched pairs. Experimental analysis of heterogeneous multiscale and multitemporal optical remote sensing images demonstrates the superior capability of the proposed algorithm over commonly used algorithms, including SIFT, RANSAC, locality preserving matching, learning a two-class classifier for mismatch removal, and linear adaptive filtering algorithms. In particular, when the precision of the initially matched pair is low, the proposed algorithm can achieve excellent results.