A robust multimodal remote sensing image registration method and system using steerable filters with first- and second-order gradients

Ye, Yuanxin; Zhu, Bai; Tang, Tengfeng; Yang, Chao; Xu, Qizhi; Zhang, Guo

doi:10.1016/j.isprsjprs.2022.04.011

Cited by 89 publications

(40 citation statements)

References 47 publications

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“…Taking the landscape roadside trees as an instance, its area is always narrow and long, thus the unevenly distributed points can easily change the shape of the image to be registered, resulting in serious distortion of the edge of the image. Therefore, though many methods have been proposed to solve multimodal images registration problems, they are usually suitable for large-scale images due to the uneven distribution and limited quantity of CPs [156,157], which is opposed to solving local registration noise.…”

Section: Analyzing the Results Using Conventional Image Registration ...mentioning

confidence: 99%

A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data

Xu,

Wang,

Skidmore

et al. 2023

Remote Sensing

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Integrating multimodal remote sensing data can optimize the mapping accuracy of individual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of individual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match individual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on individual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of individual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 ± 0.175 (standard deviation) to 0.861 ± 0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of individual trees between aerial photographs and airborne LiDAR data.

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Section: Analyzing the Results Using Conventional Image Registration ...mentioning

confidence: 99%

A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data

Xu,

Wang,

Skidmore

et al. 2023

Remote Sensing

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“…Recently, Zhu et al proposed a multimodal matching method that involve both a repeatable detector and rotation-invariant descriptor (Zhu et al, 2023). Ye et al presented a novel SFOC descriptor that makes use of the first-and second-order Gaussian steerable filters, but it is sensitive to global geometric distortions between images (Ye et al, 2022). Feature-based methods are widely used in various applications.…”

Section: Feature-based Methodsmentioning

confidence: 99%

Weak texture remote sensing image matching based on hybrid domain features and adaptive description method

Yang,

Yao,

Zhang

et al. 2023

The Photogrammetric Record

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Weak texture remote sensing image (WTRSI) has characteristics such as low reflectivity, high similarity of neighbouring pixels and insignificant differences between regions. These factors cause difficulties in feature extraction and description, which lead to unsuccessful matching. Therefore, this paper proposes a novel hybrid‐domain features and adaptive description (HFAD) approach to perform WTRSI matching. This approach mainly provides two contributions: (1) a new feature extractor that combines both the spatial domain scale space and the frequency domain scale space is established, where a weighted least square filter combined with a phase consistency filter is used to establish the frequency domain scale space; and (2) a new log‐polar descriptor of adaptive neighbourhood (LDAN) is established, where the neighbourhood window size of each descriptor is calculated according to the log‐normalised intensity value of feature points. This article prepares some remote sensing images under weak texture scenes which include deserts, dense forests, waters, ice and snow, and shadows. The data set contains 50 typical image pairs, on which the proposed HFAD was demonstrated and compared with state‐of‐the‐art matching algorithms (RIFT, HOWP, KAZE, POS‐SIFT and SIFT). The statistical results of the comparative experiment show that the HFAD can achieve the accuracy of matching within two pixels and confirm that the proposed algorithm is robust and effective.

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“…By employing rotation-invariant feature descriptors, the method captures the rotational invariance of the key points, thereby facilitating stable feature matching. To address the issues of nonlinear radiometric differences and geometric distortions, such as scale and rotation variations, between multimodal remote sensing images, Ye et al proposed a novel descriptor and a fast normalized cross-correlation similarity measure [45]. Their approach exhibits excellent performance in multimodal remote sensing image pairing.…”

Section: Related Workmentioning

confidence: 99%

Feature matching of remote‐sensing images based on bilateral local–global structure consistency

Chen,

Feng

2023

IET Image Processing

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The goal of feature matching is to establish accurate correspondences between feature points in different images depicting the same scene. To address the polymorphism of local structures, the authors propose a mismatch removal method using bilateral local–global structural consistency. This method incorporates the problem of mismatch removal into the framework of graph matching, constructs a global affinity matrix using local structural similarity and global affine transformation consistency, and optimizes it using a constrained integer quadratic programming method. To comprehensively describe the local structure, the signature quadratic form distance (SQFD) is used to measure the consistency of the neighbourhood structure. Specifically, the weights of edges are constructed based on the SQFD of the local structure, while the matching correctness of nodes and edges between the two graphs is described using local vector similarity. Furthermore, the consistency of the global affine transformation is evaluated by assessing the consistency of the local neighbourhood affine transformation between different corresponding point pairs. In estimating the local affine transformation, a bilateral correction is performed using a total least‐squares (TLS) algorithm to measure the similarity of nodes between the two different graphs. Experimental results demonstrate that the proposed algorithm outperforms state‐of‐the‐art methods in terms of accuracy and effectiveness.

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A robust multimodal remote sensing image registration method and system using steerable filters with first- and second-order gradients

Cited by 89 publications

References 47 publications

A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data

A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data

Weak texture remote sensing image matching based on hybrid domain features and adaptive description method

Feature matching of remote‐sensing images based on bilateral local–global structure consistency

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