Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

Zhao, Jianqing; Zhang, Xiaohu; Gao, Chenxi; Qiu, Xiaolei; Tian, Yongchao; Zhu, Yan; Cao, Weixing

doi:10.3390/rs11101226

Cited by 43 publications

(18 citation statements)

References 70 publications

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“…Figure 9 presents the underwater pipe mosaicked images for other mosaicking methods based on SIFT and random sample consensus (RANSAC) [46] and shows differences between these method and the proposed model. This technique is used to keep the accuracy and quality of mosaicking by eliminating the influence of mismatched point pairs in underwater images on mosaicking.…”

Section: Discussionmentioning

confidence: 99%

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Kazerouni

Dooly

Toal

2020

JMSE

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Feature extraction and matching is a key component in image stitching and a critical step in advancing image reconstructions, machine vision and robotic perception algorithms. This paper presents a fast and robust underwater image mosaicking system based on (2D)2PCA and A-KAZE key-points extraction and optimal seam-line methods. The system utilizes image enhancement as a preprocessing step to improve quality and allow for greater keyframe extraction and matching performance, leading to better quality mosaicking. The application focus of this paper is underwater imaging and it demonstrates the suitability of the developed system in advanced underwater reconstructions. The results show that the proposed method can address the problems of noise, mismatching and quality issues which are typically found in underwater image datasets. The results demonstrate the proposed method as scale-invariant and show improvements in terms of processing speed and system robustness over other methods found in the literature.

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Section: Discussionmentioning

confidence: 99%

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Kazerouni

Dooly

Toal

2020

JMSE

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“…In addition to the handmade feature points, deep learning methods, like LIFT (Learned Invariant Feature Transform) [14], are also applied in this field. These feature algorithms are widely used in various applications of UAV image stitching [3,5,[15][16][17]. For feature matching, some excellent algorithms are applied, such as RANSAC (random sample consensus) [18], GMS (grid-based motion statistics) [19,20].…”

Section: Related Workmentioning

confidence: 99%

Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information

Shao

Chen

et al. 2020

Sensors

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With the development of unmanned aerial vehicle (UAV) techniques, UAV images are becoming more widely used. However, as an essential step of UAV image application, the computation of stitching remains time intensive, especially for emergency applications. Addressing this issue, we propose a novel approach to use the position and pose information of UAV images to speed up the process of image stitching, called FUIS (fast UAV image stitching). This stitches images by feature points. However, unlike traditional approaches, our approach rapidly finds several anchor-matches instead of a lot of feature matches to stitch the image. Firstly, from a large number of feature points, we design a method to select a small number of them that are more helpful for stitching as anchor points. Then, a method is proposed to more quickly and accurately match these anchor points, using position and pose information. Experiments show that our method significantly reduces the time consumption compared with the-state-of-art approaches with accuracy guaranteed.

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“…Among possible applications, high-throughput phenotyping for advanced plant breeding is benefiting from the increased geometric and temporal resolution of acquired data. Remote sensing data from modern mobile mapping systems have been successfully used for field phenotyping [1] and crop monitoring [2,3], replacing many of the traditional in-field manual measurements. For example, UAV imagery and orthophotos have been used to extract 2 of 28 various plant traits such as plant height, canopy cover, vegetation indices, and leaf area index (LAI) [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

et al. 2021

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Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

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Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

Cited by 43 publications

References 70 publications

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

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