Automatic Geo-location Correction of Satellite Imagery

Ozcanli, Ozge C.; Dong, Yi; Mundy, Joseph L.; Webb, Helen; Hammoud, Riad I.; Tom, Victor T.

doi:10.1109/cvprw.2014.54

Cited by 18 publications

(16 citation statements)

References 5 publications

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“…Although RPCs are expected to be precise enough, the complex system they encode is subject to measurement errors in the satellite geopositioning equipment, mainly due to the attitude angles. Such inaccuracies, also referred to as pointing errors [22], can be of the order of tens of pixels in the image domain. This implies that different satellite views are typically not consistent in a common frame of reference (i.e.…”

Section: Introductionmentioning

confidence: 99%

To Bundle Adjust or Not: A Comparison of Relative Geolocation Correction Strategies for Satellite Multi-View Stereo

Marí

Franchis²,

Meinhardt-Llopis³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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The generation of up-to-date accurate 3D models from multi-view satellite images has recently become a hot research topic. A well-known challenge of this problem is to put all cameras into a common frame of reference, since depending on the satellite geopositioning equipment the camera parameters may contain errors of up to tens of meters on the ground. In this context, bundle adjustment based techniques, relying on the identification of a set of tie-points and the correction of the camera models to make them coincident, have become a generally accepted practice. However, new approaches capable of producing state-of-the-art results without the use of prior bundle adjustment have also been proposed. This work aims to compare both strategies and assess the practical impact of using bundle adjustment for 3D reconstruction from multi-view satellite images.

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Section: Introductionmentioning

confidence: 99%

To Bundle Adjust or Not: A Comparison of Relative Geolocation Correction Strategies for Satellite Multi-View Stereo

Marí

Franchis²,

Meinhardt-Llopis³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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“…Publicly available LIDAR data exists for the search ROI of the experiments in this paper and it is used to initialize 1 m 3 resolution 3D surface model. PVR framework enables reconstruction of high resolution 3D surfaces from reference satellite imagery (Pollard et al 2009;Ozcanli et al 2014) as well, but this aspect is not pursued in this paper as the purpose is to evaluate the proposed geo-localization framework irrespective of the 3D reconstruction quality.…”

Section: Fig 3 Amentioning

confidence: 99%

Geo-localization using Volumetric Representations of Overhead Imagery

2015

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This paper addresses the problem of determining the location of a ground level image by using geo-referenced overhead imagery. The input query image is assumed to be given with no meta-data and the content of the image is to be matched to a priori constructed reference representations. The semantic breakdown of the content of the query image is provided through manual labeling; however, all processing involving the reference imagery and matching are fully automated. In this paper, a volumetric representation is proposed to fuse different modalities of overhead imagery and construct a 3D reference world. Attributes of this reference world such as orientation of the world surfaces, types of land cover, depth order of fronto-parallel surfaces are indexed and matched to the attributes of the surfaces manually marked on the query image. An exhaustive but highly parallelizable matching scheme is proposed and the performance is evaluated on a set of query images located in a coastal region in Eastern United States. The performance is compared to a baseline region reduction algorithm and to a landmark existence matcher that uses a 2D representation of the reference world. The proposed 3D geo-localization framework performs better than the 2D approach for 75 % of the query images.

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“…where ∆row and ∆col are biases in row and column directions; row and col are two bias corrected coordinates in row and column directions. According to Grodecki [31] and Ozcanli [11]'s work, the simple constant bias (∆row, ∆col) is already sufficient in correcting major geo-referencing errors of satellites.…”

Section: Initial Orientation Correctionmentioning

confidence: 99%

“…The good match first methods focus on firstly selecting good matches and then computing the accurate orientation parameters through the BA techniques [10][11][12][13]. The good matches are normally detected by using a random sample consensus (RANSAC) strategy with three steps-(1) randomly select minimum matches for the BA solutions; (2) compute orientation parameters using the selected matches and count the number of good matches/inliers whose re-projection errors under the orientation parameters are small (e.g., <1 pixel); (3) the process is iterated on until the maximum number of inliers is found and the corresponding inliers are used as the final good matches for BA.…”

Section: Introductionmentioning

confidence: 99%

Matching Confidence Constrained Bundle Adjustment for Multi-View High-Resolution Satellite Images

et al. 2019

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Bundle adjustment of multi-view satellite images is a powerful tool to align the orientations of all the images in a unified framework. However, the traditional bundle adjustment process faces a problem in detecting mismatches and evaluating low/medium/high-accuracy matches, which limits the final bundle adjustment accuracy, especially when the mismatches are several times more than the correct matches. To achieve more accurate bundle adjustment results, this paper formulates the prior knowledge of matching accuracy as matching confidences and proposes a matching confidence based bundle adjustment method. The core algorithm firstly selects several highest-confidence matches to initially correct orientations of all images, then detects and eliminates the mismatches under the initial orientation guesses and finally formulates both the matching confidences and the forward-backward projection errors as weights in an iterative bundle adjustment process for more accurate orientation results. We compared our proposed method with the famous RANSAC strategy as well as a state-of-the-art bundle adjustment method on the high-resolution multi-view satellite images. The experimental comparisons are evaluated by image checking points and ground control points, which shows that our proposed method is able to obtain more robust and more accurate mismatch detection results than the RANSAC strategy, even though the mismatches are four times more than the correct matches and it can also achieve more accurate orientation results than the state-of-the-art bundle adjustment method.

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Automatic Geo-location Correction of Satellite Imagery

Cited by 18 publications

References 5 publications

To Bundle Adjust or Not: A Comparison of Relative Geolocation Correction Strategies for Satellite Multi-View Stereo

To Bundle Adjust or Not: A Comparison of Relative Geolocation Correction Strategies for Satellite Multi-View Stereo

Geo-localization using Volumetric Representations of Overhead Imagery

Matching Confidence Constrained Bundle Adjustment for Multi-View High-Resolution Satellite Images

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