Image Mosaicing Applied on UAVs Survey

Gómez-Reyes, Jean K.; Benítez-Rangel, Juan P.; Morales-Hernandez, Luis A.; Resendiz-Ochoa, Emmanuel; Camarillo‐Gómez, Karla A.

doi:10.3390/app12052729

Cited by 14 publications

(9 citation statements)

References 108 publications

(126 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This positive impact of augmentation aligns with findings from previous studies [42]. It is worth noting that the use of noise, blur, and cutouts [43], while not common in augmentation techniques, was intentionally incorporated to address the peculiarities of less-than-ideal orthomosaic images in nature [44]. Mosaiced images are susceptible to blur, ghosting, and other irregularities, and the inclusion of noise, blur, and cutouts contributes to the development of a robust model.…”

Section: Data Preparationsupporting

confidence: 76%

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Nasimi,

Wood

2024

Remote Sensing

View full text Add to dashboard Cite

Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation.

show abstract

Section: Data Preparationsupporting

confidence: 76%

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Nasimi,

Wood

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…To obtain images with a broader field of view, image stitching is a necessary preprocessing step in unmanned aerial vehicle (UAV) remote sensing applications [1]. The common issues involved in stitched images include irregular boundaries and stitching seams due to the inability to achieve perfect alignment of multiple images.…”

Section: Introductionmentioning

confidence: 99%

Elimination of Irregular Boundaries and Seams for UAV Image Stitching with a Diffusion Model

Chen,

Luo,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) image stitching refers to the process of combining multiple UAV images into a single large-format, wide-field image, and the stitched image often contains large irregular boundaries and multiple stitching seams. Usually, irregular boundaries are addressed using grid-constrained methods, while seams are optimized through the design of energy functions and penalty terms applied to the pixels at the seams. The above-mentioned two solutions can only address one of the two issues individually and are often limited to pairwise stitching of images. To the best of our knowledge, there is no unified approach that can handle both seams and irregular boundaries in the context of multi-image stitching for UAV images. Considering that addressing irregular boundaries involves completing missing information for regularization and that mitigating seams involves generating images near the stitching seams, both of these challenges can be viewed as instances of a mask-based image completion problem. This paper proposes a UAV image stitching method based on a diffusion model. This method uniformly designs masks for irregular boundaries and stitching seams, and the unconditional score function of the diffusion model is then utilized to reverse the process. Additional manifold gradient constraints are applied to restore masked images, eliminating both irregular boundaries and stitching seams and resulting in higher perceptual quality. The restoration maintains high consistency in texture and semantics. This method not only simultaneously addresses irregular boundaries and stitching seams but also is unaffected by factors such as the number of stitched images, the shape of irregular boundaries, and the distribution of stitching seams, demonstrating its robustness.

show abstract

“…Therefore, feature matching for UAV color images is crucial. Some researchers have proposed improvement methods such as [16] [17], which have achieved certain results in certain specific environments. In response to the problem of multiple incorrect matching points and low matching accuracy in color image stitching of UAVs, this article improves the representation method of color images by introducing quaternion [18] to represent color images.…”

Section: Introductionmentioning

confidence: 99%