Anisotropic Scattered Data Interpolation for Pushbroom Image Rectification

Ringaby, Erik; Friman, Ola; Forssén, Per-Erik; Opsahl, Thomas Olsvik; Haavardsholm, Trym Vegard; Kåsen, Ingebjørg

doi:10.1109/tip.2014.2316377

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Normal Kriging works very well in interpolation (e.g. gabean et al, 2010)), image rectification (Ringaby et al, 2014) and related applications, however we found that it underperformed in our application and was therefore omitted. Even Kriging refinement improved the results only marginally.…”

Section: Resultsmentioning

confidence: 73%

“…To counter these effects, we introduce an adaptive splat kernel, adjusted to the local density of projected points. This is a generalization of ideas found in (Ringaby et al, 2014), where the shape of the region is defined for the application of an aircraftmounted push-broom camera. Here we generalize this by instead estimating the shape from neighboring projected points: For each candidate point, we calculate the distances between its projected position in the output image and the projected positions of its eight nearest neighbors in the input image.…”

Section: Global Parametersmentioning

confidence: 97%

“…This step implements the occlusion aware projection box shown in figure 1. Finally, even more details can be preserved by taking the anisotropy of the texture into account, using Kriging (Panggabean et al, 2010;Ringaby et al, 2014). Note that many of these methods have not been applied in DIBR before.…”

Section: Motion Compensationmentioning

confidence: 99%

“…We also tested Kriging (Panggabean et al, 2010;Ringaby et al, 2014) as a method to merge the candidates. In Kriging, the blending weight calculation described earlier is replaced by a best linear unbiased estimator using the covariance matrix of different samples in a predetermined neighborhood.…”

Section: Krigingmentioning

confidence: 99%

See 3 more Smart Citations

Pushing the Limits for View Prediction in Video Coding

Ogniewski

Forssén

2017

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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More and more devices have depth sensors, making RGB+D video (colour+depth video) increasingly common. RGB+D video allows the use of depth image based rendering (DIBR) to render a given scene from different viewpoints, thus making it a useful asset in view prediction for 3D and free-viewpoint video coding. In this paper we evaluate a multitude of algorithms for scattered data interpolation, in order to optimize the performance of DIBR for video coding. This also includes novel contributions like a Kriging refinement step, an edge suppression step to suppress artifacts, and a scale-adaptive kernel. Our evaluation uses the depth extension of the Sintel datasets. Using ground-truth sequences is crucial for such an optimization, as it ensures that all errors and artifacts are caused by the prediction itself rather than noisy or erroneous data. We also present a comparison with the commonly used mesh-based projection.

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Section: Resultsmentioning

confidence: 73%

Section: Global Parametersmentioning

confidence: 97%

Section: Motion Compensationmentioning

confidence: 99%

Section: Krigingmentioning

confidence: 99%

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Pushing the Limits for View Prediction in Video Coding

Ogniewski

Forssén

2017

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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“…The inverse interpolation scheme guarantees that each point in the output grid is assigned a predicted value [44]. We assumed that the k-means clustering can accurately stratify the fragmented landscape.…”

Section: Statistical Upscaling Of Pft Distribution On the Landscapementioning

confidence: 99%

Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets

et al. 2016

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Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June-August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m × 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy (R 2 = 0.75, RMSE = 9.94) than the single image upscaling (R 2 = 0.63, RMSE = 12.05) predicted from IDW. We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model (R 2 = 0.61, RMSE = 13.78) than only using the original 48 plots for the phenology model (R 2 = 0.23, RMSE = 17.49). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems.

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Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors

Pepe

Fregonese²,

Scaioni³

2018

European Journal of Remote Sensing

116

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The mission planning in airborne Photogrammetry and Remote Sensing applications, depending on the system of acquisition and by the adopted platform (such as rotary and fixed wing aircrafts, glider, airship, manned or unmanned), is the first and essential step to ensure the success of a survey mission. The purpose of this paper is to provide an overview on mission planning techniques using passive optical sensors. The basic concepts related to the usage of the most common sensor technologies are described, along with the several possible scenarios that may be afforded by using modern airborne sensors. Several examples of flight plans are illustrated and discussed to highlight correct methods, procedures and tools for data acquisition in the case of different types of manned and unmanned airborne missions. In particular, the flight planning with more recent technologies of digital passive optical airborne sensors will be dealt with, including frame cameras and multi-/hyperspectral push-broom sensors. Furthermore, in order to ensure the complete success of an airborne mission, some up-to-date solutions to know in advance the weather conditions (cloud cover, height of the sun, wind, etc.) and the GNSS satellite configuration are illustrated.

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Anisotropic Scattered Data Interpolation for Pushbroom Image Rectification

Cited by 9 publications

References 22 publications

Pushing the Limits for View Prediction in Video Coding

Pushing the Limits for View Prediction in Video Coding

Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets

Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors

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