Distinctive Order Based Self-Similarity descriptor for multi-sensor remote sensing image matching

Sedaghat, Amin; Ebadi, Hamid

doi:10.1016/j.isprsjprs.2015.06.003

Cited by 86 publications

(56 citation statements)

References 37 publications

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“…These findings are consistent with the findings of previous research conducted by Long et al and Sedaghat et al, which proposed outlier removal to increase the accuracy of SIFT-based GCPs. However, our research proposed an iterative process for outlier removal which evaluates the GCP after performing a complete RPC refinement process, thus taking the topographic effects into account by use of DEM [25,26]. Additionally, the proposed outlier removal strategy is simpler than the one conducted in Sedaghat et al, which used combinations of contrast matching, curvature analysis and entropy calculation [25].…”

Section: Resultsmentioning

confidence: 99%

“…However, our research proposed an iterative process for outlier removal which evaluates the GCP after performing a complete RPC refinement process, thus taking the topographic effects into account by use of DEM [25,26]. Additionally, the proposed outlier removal strategy is simpler than the one conducted in Sedaghat et al, which used combinations of contrast matching, curvature analysis and entropy calculation [25]. Moreover, our evaluation related to surface topography and LC characteristics meant that the accuracy and the number of GCPs produced by SIFT was not affected by topographic variation.…”

Section: Resultsmentioning

confidence: 99%

“…The rotation invariance is ensured using the gradient orientations and magnitudes of the pixels around the key points, and the scale invariance is ensured using the scale space approach. The steps of the algorithm are scale space production, difference of Gaussian (DOG), finding local maximum and minimum, eliminating bad points, assigning orientation to key points and feature transformation [25,[37][38][39][40].…”

Section: Sift Algorithmmentioning

confidence: 99%

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Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement

Kartal

Algancı

Sertel

2018

IJGI

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Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the "Forest" and "Semi Natural Area" LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Sift Algorithmmentioning

confidence: 99%

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Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement

Kartal

Algancı

Sertel

2018

IJGI

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“…As a premise operation of feature extraction, the input image will be smoothened in the scale space. After that, one or several features of the image will be calculated through local derivative operation (Amin and Hamid, 2015;Gholam and Davar, 2015).…”

Section: Image Feature Extractionmentioning

confidence: 99%

Local Feature Point Extraction Method Based on SIFT

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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Feature extraction uses the calculation of the image features as the intermediate result to make decisions on the local information contents of the image. To extract the image local feature points is to extract the feature points which include effective information from the image and then describe these feature points. Moreover, this process is required to have certain robustness in the viewpoint change, grey-level change, noises and other factors. This paper optimizes the structure process and feature descriptor of scale space to overcome the defects of the optimization space in the structure and generation feature descriptor of the scale space of the conventional SIFT (Scale-Invariant Feature Transform) algorithm and proposes an image feature point extraction method based on improved SIFT algorithm. It abstracts the edge information of the shape of the image objects into the feature graph constituted by the relative orientations of the feature points and the edges. Based on the feature graph, the object shape recognition algorithm with invariance in translation, scale and rotation can be designed so as to improve the precision and accuracy of the traditional SIFT algorithm.

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“…However, when the standard SIFT operator is applied to multi-source optical satellite image matching, the number of matched feature points is limited; a large percentage of mismatches occur and the correctly matched points are not uniformly distributed, resulting in match failure. To improve the performance of SIFT, researchers have reformed it mainly by: (1) improving the feature extraction operator [15][16][17][18]; (2) improving the feature descriptor [19][20][21]; and (3) improving the matching strategy [22][23][24][25][26]. For example, to improve the feature extraction ability in textureless areas, Sedaggat et al [17] proposed the uniform robust SIFT (UR-SIFT) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach

Liu

Tao

2017

Remote Sensing

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Geometric distortions and intensity differences always exist in multi-source optical satellite imagery, seriously reducing the similarity between images, making it difficult to obtain adequate, accurate, stable, and well-distributed matches for image registration. With the goal of solving these problems, an effective image matching method is presented in this study for multi-source optical satellite imagery. The proposed method includes three steps: feature extraction, initial matching, and matching propagation. Firstly, a uniform robust scale invariant feature transform (UR-SIFT) detector was used to extract adequate and well-distributed feature points. Secondly, initial matching was conducted based on the Euclidean distance to obtain a few correct matches and the initial projective transformation between the image pair. Finally, two matching strategies were used to propagate matches and produce more reliable matching results. By using the geometric relationship between the image pair, geometric correspondence matching found more matches than the initial UR-SIFT feature points. Further probability relaxation matching propagated some new matches around the initial UR-SIFT feature points. Comprehensive experiments on Chinese ZY3 and GaoFen (GF) satellite images revealed that the proposed algorithm performs well in terms of the number of correct matches, correct matching rate, spatial distribution, and matching accuracy, compared to the standard UR-SIFT and triangulation-based propagation method.

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Distinctive Order Based Self-Similarity descriptor for multi-sensor remote sensing image matching

Cited by 86 publications

References 37 publications

Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement

Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement

Local Feature Point Extraction Method Based on SIFT

Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach

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