Robust Stepwise Correspondence Refinement for Low-Altitude Remote Sensing Image Registration

Gong, Xiaoying; Liu, Yuyan; Yang, Yang

doi:10.1109/lgrs.2020.3008446

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Ma et al [2] have enhanced the matching of feature points through combined with the gradient definition method and information of key points. Gong et al [20] proposed an invariant descriptor of robust neighborhood structure and designed a strategy of dynamic matching.…”

Section: Introductionmentioning

confidence: 99%

An SIFT-Based Fast Image Alignment Algorithm for High-Resolution Image

et al. 2023

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To solve the problem of high-resolution image alignment time overhead, an SIFT-based fast image alignment algorithm is presented. The overlap region of images is computed in detail by phase correlation algorithm to avoid a lot of useless calculations of non-overlapping region. After the distribution of feature points determined in difference of Gaussian through formula derivation, the total number of feature points is limited. The more stable spatially distributed for the feature points is obtained due to the expanded detection range of extreme points and added non-maximum suppression. It is noteworthy that the range of the descriptor is calculated by the method of down-sampling. And the circular descriptor is constructed with only 56-dimensional in the feature point descriptor generation stage, which makes the time of the descriptor generation and feature point matching shorter. This indicates that the total descriptor calculation is faster in lower dimensions by the new algorithm. In addition, experimental results show that the average time (9.60s, 13.46s, and 15.81s) of the proposed algorithm is 0.86%, 0.43%, and 0.10% of the SIFT algorithm, respectively. The overall speed is 2-3 orders of magnitude faster than the SIFT algorithm, which indicates that the new algorithm can solve the problem of high-resolution image alignment time overhead. The new algorithm provides a good stitching quality and shows an excellent performance for high-resolution image compared with several existing image stitching algorithms at the current. It indicates that the algorithm has potential application value in real-time image stitching.INDEX TERMS Scale-invariant feature transform (SIFT), image alignment, high-resolution image, fast alignment.

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Section: Introductionmentioning

confidence: 99%

An SIFT-Based Fast Image Alignment Algorithm for High-Resolution Image

et al. 2023

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“…Because of its flexibility, high efficiency, and low cost, UAV (Unmanned Aerial Vehicle) remote sensing technology has gradually emerged in many fields, such as accurate agriculture, resources investigation, environment management, and disaster monitoring [1][2][3]. How to yield high-precision registered UAV images quickly has become an inevitable challenge to the wide application of UAV technology [4][5][6][7][8][9]. However, the high resolutions of UAV images have a great influence on the detection and matching of image feature points.…”

Section: Introductionmentioning

confidence: 99%

Fast Automatic Registration of UAV Images via Bidirectional Matching

Luo,

Wei,

Jin

et al. 2023

Sensors

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Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB—the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization.

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Advanced classification models II: extensions to convolutional neural networks

Meshram

2024

Machine Learning Applications in Civil Engineering

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Robust Stepwise Correspondence Refinement for Low-Altitude Remote Sensing Image Registration

Cited by 8 publications

References 13 publications

An SIFT-Based Fast Image Alignment Algorithm for High-Resolution Image

An SIFT-Based Fast Image Alignment Algorithm for High-Resolution Image

Fast Automatic Registration of UAV Images via Bidirectional Matching

Advanced classification models II: extensions to convolutional neural networks

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