A Novel Adjustment Model for Mosaicking Low-Overlap Sweeping Images

Liu, Jianchen; Gong, Jianya; Guo, Bingxuan; Zhang, Weilong

doi:10.1109/tgrs.2017.2688385

Cited by 21 publications

(8 citation statements)

References 26 publications

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“…Typical inlier ratios reported in literature with the same parameter settings are in the order of 75% [25]. The high number of mismatches is attributed to the vast structural differences in image content, and not to a small overlap between images, which is typically a major cause of matching errors [56]. As a conclusion, these results demonstrate that independent, unconstrained pairwise registrations are highly unreliable on this kind of data, and motivate to approach the task with a probabilistic, groupwise registration framework.…”

Section: Performance Of Standard Local Feature Matchingmentioning

confidence: 90%

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Zambanini

2019

Pattern Recognition

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In this paper, we address the registration of historical WWII images to presentday ortho-photo maps for the purpose of geolocalization. Due to the challenging nature of this problem, we propose to register the images jointly as a group rather than in a step-by-step manner. To this end, we exploit Hough Voting spaces as pairwise registration estimators and show how they can be integrated into a probabilistic groupwise registration framework that can be efficiently optimized. The feature-based nature of our registration framework allows to register images with a-priori unknown translational and rotational relations, and is also able to handle scale changes of up to 30% in our test data due to a final geometrically guided matching step. The superiority of the proposed method over existing pairwise and groupwise registration methods is demonstrated on eight highly challenging sets of historical images with corresponding ortho-photo maps.

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Section: Performance Of Standard Local Feature Matchingmentioning

confidence: 90%

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Zambanini

2019

Pattern Recognition

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“…A range of semiautomated [18,20,22,26,41] and fully automated frameworks [37,38,[72][73][74] have been explored to georectify UAV-based hyperspectral data captured by push-broom cameras. However, challenges related to data collection procedures, quality assessment, and optimization of algorithms require further investigation to expedite data processing and accomplish a standardized positional accuracy of retrieved data.…”

Section: Discussionmentioning

confidence: 99%

Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

et al. 2019

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Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%.Remote Sens. 2020, 12, 34 2 of 25 understanding [2]. One of the key constraints in our observation capacity relates to the compromise between spatial and temporal resolution, i.e., space-based platforms tend to suffer from either spatial or temporal restrictions that affect the frequency and fidelity of retrievals. Within the last decade, developments in remote sensing using unmanned aerial vehicles (UAV) have provided a sensor-flexible platform that has lowered operational costs, while providing unprecedented spatial (<10 cm) and on-demand temporal resolution [3,4]. To leverage these technological advances, hyperspectral camera systems capturing radiances across the visible and near-infrared portions of the spectrum [5] have been developed, with diverse applications being presented in agriculture [6][7][8], forestry [9], and mining [10] studies. However, while UAV technology has progressed rapidly and there is a level of maturity in many sensing capabilities [11], routine application of hyperspectral imaging systems remains chall...

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“…This method can obtain visually satisfactory mosaic results automatically using only the raw 2D imagery captured from an UAV. Liu et al [12] considered mosaic for low overlap UAV aerial images and found that the commonly used methods and software were not suitable for this type of data. Then, a novel adjustment model for mosaic low overlap sweeping images was proposed.…”

Section: Introductionmentioning

confidence: 99%

Smooth Stitching Method for the Texture Seams of Remote Sensing Images Based on Gradient Structure Information

Deng

2021

Processes

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Traditional smooth stitching method for the texture seams of remote sensing images is affected by gradient structure information, leading to poor stitching effect. Therefore, a smooth stitching method for the texture seams of remote sensing images based on gradient structure information is proposed in this research. By matching the feature points of remote sensing images and introducing a block link constraint and shape distortion constraint, the modified stitching image is obtained. By using remote sensing image fusion, the smooth stitching image of texture seams is obtained, and the local overlapping area of the texture is optimized. The main direction of texture seams is determined by calculating the gradient structure information of texture seams in horizontal and vertical directions. By selecting the initial point, the optimal stitching line is extracted by using the minimum mean value of the cumulative error of the smooth stitching line. By using the method of boundary correlation constraints, matching the feature points of the texture seams of remote sensing images and selecting the best matching pair, a smooth stitching algorithm for the texture seams of remote sensing image is designed, which realizes the smooth stitching of the texture seams of remote sensing images. Experimental results show that the design method has good performance in stitching accuracy and efficiency in the smooth stitching of the texture seams of remote sensing images. Specifically, the Liu et al. and the Zhang et al. methods that are the benchmark studies in the literature are introduced as a comparison, and the stitching experiment is carried out. The test is carried out according to accuracy and time and the proposed method achieves better results by almost 25%.

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A Novel Adjustment Model for Mosaicking Low-Overlap Sweeping Images

Cited by 21 publications

References 26 publications

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

Smooth Stitching Method for the Texture Seams of Remote Sensing Images Based on Gradient Structure Information

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