Automatic Registration and Mosaicking for Airborne Multispectral Image Sequences

Du, Qian; Raksuntorn, Nareenart; Orduyilmaz, Adnan; Bruce, Lori

doi:10.14358/pers.74.2.169

Cited by 45 publications

(18 citation statements)

References 19 publications

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“…However, the preliminary experiments of our coastal shoreline UAS images have shown that Autopano Pro [31], one of the widely used commercial versions of the SIFT algorithm, could produce inappropriate tie points for some neighboring images due to no distinctive features being present. Feature-based image registration algorithms also failed in some rangeland and agricultural areas [32,33]. Our experiments showed that the area-based image similarity could find correct locations at some failure cases by the SIFT algorithm.…”

Section: Co-registrationmentioning

confidence: 82%

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

Huang

Gibeaut

et al. 2016

Geoinformatica

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Unmanned aerial systems (UAS) have been used as a robust tool for agricultural and environmental applications in recent years. Remote sensing systems based on UAS typically acquire massive hyper-spatial images in its short turnaround. This paper takes advantage of graphics processing unit (GPU) massive parallel computation in order to process the huge data timely and efficiently. More specifically, this paper presents an index array approach for lens distortion correction and geo-referencing. They are the two essential components in UAS hyper-spatial image processing. The index array approach is also capable of parallelizing image file I/O and the orthoimage generation. In addition, this paper presents the dual tiled similarity algorithm for the image co-registration. The index array approach and the dual tiled similarity algorithm were evaluated using two UAS remote sensing datasets of South Padre island shorelines. The results show that this index array approach was able to speed up at least 10 times the lens distortion correction and the geo-referencing relative to the central processing unit (CPU) computation. This dual tiled algorithm could provide 12 times speedup compared with the CPU similarity computation.

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Section: Co-registrationmentioning

confidence: 82%

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

Huang

Gibeaut

et al. 2016

Geoinformatica

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“…An efficient workflow is necessary for the entire processing chain so that end products of a given accuracy can be obtained in a reasonable time. The position and attitude data acquired from UAS often suffer from lower accuracy compared to data acquired with piloted aircraft, and various custom approaches have been developed for orthorectification and mosaicking of UAS imagery [28][29][30][31]. Radiometric correction can also be challenging due to the (potentially) large number of individual images, image quality, and limits over the control of image acquisition parameters [24].…”

Section: Introductionmentioning

confidence: 99%

Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments

Goforth²,

et al. 2011

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Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R 2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery.

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“…However, this research was based on a camera that was modified from a consumer-level RGB camera that had only three bands, and choosing an optimal band to conduct feature matching was not discussed. Du [29] directly used the NIR band for feature matching, and the variation of feature quality among images was not taken into consideration. For the mosaic data from the AISA Eagle hyperspectral sensor, YK Han [30] used the SIFT matching result to select the optimal band for feature matching.…”

Section: Related Researchmentioning

confidence: 99%

“…By using the perspective projection model, the camera motion can be restored from the image matching results, and the 3D positions of the matched points can also be estimated if necessary. In some studies, the affine transformation is used to simplify the estimation process [29]. In the method, affine transformation, the eight-parameter projective model, and the thin-plate spline model were applied to characterize the locations and poses of the images.…”

Section: Related Researchmentioning

confidence: 99%

A Simplified Method for UAV Multispectral Images Mosaicking

et al. 2017

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This paper presents a method for mosaicking unmanned aerial vehicle (UAV) multispectral images. The main purpose of the proposed method is to reduce spatial distortion in the mosaicking process and increase robustness and the speed of the operation. Most UAV multispectral images have multiple bands, and in every band, ground targets have a variety of reflection characteristics that will result in diverse feature quality for feature matching. In this research, an information entropy-based evaluation method is used to select the optimal band for feature matching among the UAV images. To produce more robust matching results for the following alignment step, the evaluation method takes the contrast and spatial distribution of the feature points into consideration at the same time. In most common image mosaicking processes, the digital orthophoto map (DOM) is generated to achieve maximum spatial accuracy. During this process, the original image data will experience considerable irregular resampling, and the process is also unstable in some circumstances. The alignment step uses a simplified projection model that treats the ground as planar is provided, by which the alignment parameters are applied directly to the images instead of generating 3D points, to avoid irregular resampling and unstable 3D reconstruction. The proposed method is proved to be more efficient and accurate and has lower spectral distortion than state-of-the-art mosaicking software.

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Automatic Registration and Mosaicking for Airborne Multispectral Image Sequences

Cited by 45 publications

References 19 publications

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments

A Simplified Method for UAV Multispectral Images Mosaicking

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