Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features

Zhu, Bai; Ye, Yuanxin; Zhou, Liang; Li, Zhilin; Yin, Gaofei

doi:10.1016/j.isprsjprs.2021.09.010

Cited by 30 publications

(6 citation statements)

References 44 publications

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“…However, due to the existence of sensor differences and illumination differences in PRSIs, these methods based on spatial domain features have proved difficult to apply to the images with the above problems [24]. Different from the grey change relationship of images captured from the spatial domain, similarity measurement built in the frequency domain can better reflect the structural features of images [25][26][27][28]. Pedrosa et al [25] constructs matching templates through fast Fourier transform (FFT) and calculates the probability volume between the templates and the images, thus realizing the detection of Mars impact craters.…”

Section: A Area-based Methodsmentioning

confidence: 99%

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

Huang,

Wan,

Zhou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Matching planetary remote sensing images (PRSI) is essential for deep space exploration. Through matching optical images collected by different probes, the terrain of planets can be accurately mapped; however, PRSI lacks surface texture information and exhibits noticeable nonlinear radiation differences, e.g., illumination differences. It is not easy to achieve satisfactory results through traditional matching methods. In order to cope with the above problems, a new PRSI matching method has been proposed. It can extract low-high frequency features by building doublefrequency scale space. In addition, we also use the nonmaximum suppression strategy for rejecting overlapped feature points, which reduces the time consumption and improves the matching accuracy. The experimental results show that the proposed method can effectively match PRSI and be superior to comparable methods.

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Section: A Area-based Methodsmentioning

confidence: 99%

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

Huang,

Wan,

Zhou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Multi-source images, also known as multimodal images, typically refer to image data acquired using sensors with two or more different types of imaging mechanisms for the same scene or object. Such images comprise visible images, infrared images [94], hyperspectral images [95], optical Synthetic Aperture Radar (SAR) images [96], and Light Detection and Ranging (LiDAR) [97]. These different modality images can provide diverse and complementary feature information for the same scene or object [98].…”

Section: Multimodel Image Homography Estimation Methodsmentioning

confidence: 99%

A Review of Homography Estimation: Advances and Challenges

Luo,

Wang,

Liao

et al. 2023

Electronics

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Images captured from different viewpoints or devices have often exhibited significant geometric and photometric differences due to factors such as environmental variations, camera technology differences, and shooting conditions’ instability. To address this problem, homography estimation has attracted much attention as a method to describe the geometric projection relationship between images. Researchers have proposed numerous homography estimation methods for single-source and multimodal images in the past decades. However, the comprehensive review and analysis of homography estimation methods, from feature-based to deep learning-based, is still lacking. Therefore, we provide a comprehensive overview of research advances in homography estimation methods. First, we provide a detailed introduction to homography estimation’s core principles and matrix representations. Then, we review homography estimation methods for single-source and multimodal images, from feature-based to deep learning-based methods. Specifically, we analyze traditional and learning-based methods for feature-based homography estimation methods in detail. For deep learning-based homography estimation methods, we explore supervised, unsupervised, and other methods in-depth. Subsequently, we specifically review several metrics used to evaluate these methods. After that, we analyze the relevant applications of homography estimation and show the broad application prospects of this technique. Finally, we discuss current challenges and future research directions, providing a reference for computer vision researchers and engineers.

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“…As shown in Figure 3, there are some examples of the spatial mismatch of individual trees in the manually delineated datasets, which are caused by local displacement between aerial photograph and CHMs. These mismatches are spatially inconsistent in both distance and orientation, and consequently cannot be directly addressed through traditional global geographic calibration for images [69,[128][129][130].…”

Section: Visualization Of Individual Tree Mismatches Between Aerial P...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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Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features

Cited by 30 publications

References 44 publications

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

A Review of Homography Estimation: Advances and Challenges

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

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