A Simplified Method for UAV Multispectral Images Mosaicking

Xiang, Rong; Sun, Min; Zhang, Xianfeng; Liu, Lei

doi:10.3390/rs9090962

Cited by 20 publications

(13 citation statements)

References 31 publications

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“…The former methods generate mosaic images using digital surface models (DSMs) or ground control points (GCPs) [1][2][3][4][5][6]. The latter methods generate mosaic images based on tiepoints between adjacent images [7][8][9][10]. In many remote sensing applications, spatial-data-based methods are preferred because they can produce georeferenced or ortho-rectified mosaic images.…”

Section: Introductionmentioning

confidence: 99%

Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling

Kim

2020

Remote Sensing

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This paper proposes a robust feature-based mosaicking method that can handle images obtained by lightweight unmanned aerial vehicles (UAVs). The imaging geometry of small UAVs can be characterized by unstable flight attitudes and low flight altitudes. These can reduce mosaicking performance by causing insufficient overlaps, tilted images, and biased tiepoint distributions. To solve these problems in the mosaicking process, we introduce the tiepoint area ratio (TAR) as a geometric stability indicator and orthogonality as an image deformation indicator. The proposed method estimates pairwise transformations with optimal transformation models derived by geometric stability analysis between adjacent images. It then estimates global transformations from optimal pairwise transformations that maximize geometric stability between adjacent images and minimize mosaic deformation. The valid criterion for the TAR in selecting an optimal transformation model was found to be about 0.3 from experiments with two independent image datasets. The results of a performance evaluation showed that the problems caused by the imaging geometry characteristics of small UAVs could actually occur in image datasets and showed that the proposed method could reliably produce image mosaics for image datasets obtained in both general and extreme imaging environments.

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Section: Introductionmentioning

confidence: 99%

Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling

Kim

2020

Remote Sensing

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“…All three modes above can steadily capture and obtain images over the current field coverage, but for a large area, an appropriate image mosaicking technique is needed to mosaic farmland UAV images over the area to form a complete image of the entire target region [11]. Mosaicking of UAV images refers to the technique that merges an obtained sequence of two or more UAV images into one wide-field image, and the main procedures include image preprocessing, image registration, and image fusion [10,12].…”

Section: Introductionmentioning

confidence: 99%

“…Different from Photoscan and PIX4D, multiple images can be mosaicked or stitched together to form one contiguous file for Global mapper without 3D reconstruction. However, the computational efficiency of commercial software is relatively low [11], and when agricultural researchers want to monitor real-time crop growth in the field, they do not need to use highly accurate orthomosaics. In addition, for the purpose of real-time crop growth monitoring, the open-source method is a better choice to integrate image mosaic function in a real-time UAV monitoring system [13].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

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“…A multispectral camera mounted on a UAS allows both visible and multispectral imagery to be captured that can be used for characterizing land features, vegetation health and function. The Parrot Sequoia multispectral sensor has green (530-570 nm), red (640-680 nm), red edge (730-740 nm) and near-infrared (770-810 nm) wavebands and a camera with red, green and blue wavebands (RGB camera) (400-700 nm) [26]. Colour, structure and surface texture of different land features can influence the reflectance pattern of the wavebands [27].…”

Section: Introductionmentioning

confidence: 99%

Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS

2019

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Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral sensor used in this study has green, red, red edge and near-infrared wavebands, and a regular camer with red, green and blue wavebands (RGB camera), to capture both visible and near-infrared (NIR) imagery of the land surface. The workflow of 3D vegetation mapping of the study site included establishing coordinated ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes included an orthomosaic model, a 3D surface model and multispectral imagery of the study site, in the Irish Transverse Mercator (ITM) coordinate system. The planimetric resolution of the RGB sensor-based outcomes was 0.024 m while multispectral sensor-based outcomes had a planimetric resolution of 0.096 m. High-resolution vegetation mapping was successfully generated from these data processing outcomes. There were 235 sample areas (1 m × 1 m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using nine different classification strategies to examine the efficiency of multispectral sensor data for vegetation and contiguous land cover mapping. The nine classification strategies included combinations of spectral bands and vegetation indices. Results show classification accuracies, based on the nine different classification strategies, ranging from 52% to 75%.

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A Simplified Method for UAV Multispectral Images Mosaicking

Cited by 20 publications

References 31 publications

Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling

Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling

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

Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS

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