Automatic Registration of Coastal Remotely Sensed Imagery by Affine Invariant Feature Matching with Shoreline Constraint

Cheng, Liang; Tong, Lihua; Liu, Yongchao; Li, Manchun; Wang, Jiechen

doi:10.1080/01490419.2013.868382

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The fundamental matrix F can be computed from correspondences between image points alone, without knowledge of camera internal parameters or the relative orientation required. A relatively strict RANSAC tolerance t can remove all of the wrong matches, but also many correct matches; on the contrary, a relatively loose RANSAC threshold may keep more matches, but will not eliminate all the wrong ones [63].…”

Section: Methodsmentioning

confidence: 99%

A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

Daliakopoulos

Tsanis

2019

Sensors

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A module for Very High Resolution (VHR) satellite stereo-pair imagery processing and Digital Elevation Model (DEM) extraction is presented. A large file size of VHR satellite imagery is handled using the parallel processing of cascading image tiles. The Scale-Invariant Feature Transform (SIFT) algorithm detects potentially tentative feature matches, and the resulting feature pairs are filtered using a variable distance threshold RANdom SAmple Consensus (RANSAC) algorithm. Finally, point cloud ground coordinates for DEM generation are extracted from the homologous pairs. The criteria of average point spacing irregularity is introduced to assess the effective resolution of the produced DEMs. The module is tested with a 0.5 m × 0.5 m Geoeye-1 stereo pair over the island of Crete, Greece. Sensitivity analysis determines the optimum module parameterization. The resulting 1.5-m DEM has superior detail over reference DEMs, and results in a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements.

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Section: Methodsmentioning

confidence: 99%

A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

Daliakopoulos

Tsanis

2019

Sensors

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“…The approach can be used particularly well to register images of coastal areas. Cheng et al proposed a new approach based on Affine Invariant Feature Matching (AIFM) with a filtering technique is proposed for automatic registration of remotely input image in coastal areas [9] [10]. The novelty of this approach was is an automatic filtering technique using RANdom SAmple Consensus (RANSAC) [11] with shoreline constraint for AIFM to remove all wrong matches and simultaneously keep as many correct matches as possible.…”

Section: Current State Of Problem Resolutionmentioning

confidence: 99%

Comparison of Contour Feature Based and Intensity Based Insat-3d Met Images Coregistration for Sub Pixel Accuracies

Moorthi¹,

Sivakumar²

2018

IJIVP

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Image registration in meteorological images that are acquired continuously for their use in weather forecast activities and other related scientific analysis is a critical requirement. Meteorological images are obtained from geostationary orbits in visible, infrared, water vapor channels covering a large frame of several hundreds of kilometres of geographical extent which generally involve bidirectional scanning to cover larger extents. The acquired images have to be guaranteed for their geometric fidelity to a standard of choice among themselves by image registration. Registration of such images require to deal with low contrast, cloud and snow occlusions apart from navigation data uncertainties. Nevertheless, sub pixel accuracies are demanded for image analysis and geophysical parameters derivations. Feature based registration techniques are commonly used and intensity based techniques are also put to use in these contexts rarely. The proposed feature based approach uses a land water boundary data extraction with phase correlation of image blocks and proposed the intensity based approach tackles the same problem without any preprocessing step using a sampler-metric-transform-optimizer procedure. A comparison of these two approaches is pursued here in this article using various channel data sets of INSAT-3D satellite for sub pixel accuracies.

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“… – 28 To prevent the influence of mismatched key points, attempts have been made to combine SIFT with outlier elimination methods 26 , 29 , 30 . Some researchers employed additional datasets, such as digital surface model, 31 stereo image pair, 32 , 33 point and line features, 34 and street view images 35 to extract adequate affine invariant features. Coarse-to-fine methods comprise a preregistration process and a fine-tuning process that employ the advantages of both feature-based and intensity-based methods 10 , 29 .…”

Section: Introductionmentioning

confidence: 99%

Automatic registration method for medium-resolution remote sensing images of coral reefs with morphological information pairing and constrained iterative fining

Chen,

Pian,

Chen

et al. 2023

J. Appl. Rem. Sens.

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Automatic Registration of Coastal Remotely Sensed Imagery by Affine Invariant Feature Matching with Shoreline Constraint

Cited by 9 publications

References 23 publications

A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

Comparison of Contour Feature Based and Intensity Based Insat-3d Met Images Coregistration for Sub Pixel Accuracies

Automatic registration method for medium-resolution remote sensing images of coral reefs with morphological information pairing and constrained iterative fining

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