Propagation strategies for stereo image matching based on the dynamic triangle constraint

Zhu, Qing; Wu, Bo; Tian, Yuan

doi:10.1016/j.isprsjprs.2007.05.010

Cited by 30 publications

(30 citation statements)

References 18 publications

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“…The EO parameters of HiRISE and CTX were acquired through interpolating the spacecraft's trajectory and pointing vectors based on the observation time. A self-adaptive triangulation-constrained image matching method (Wu et al, 2011(Wu et al, , 2012Zhu et al, 2007) was used to automatically obtain a large number of corresponding feature points (homogeneous points that represent the same texture) from the images. 3D coordinates of the matched points were then obtained by photogrammetric intersection using the image EO parameters, from which DEMs were interpolated.…”

Section: Inconsistencies Between the Dems Generated From Hirise And Cmentioning

confidence: 99%

COMPARISON AND CO-REGISTRATION OF DEMS GENERATED FROM HiRISE AND CTX IMAGES

Wu¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission IV, WG IV/8 KEY WORDS: Mars, DEM, HiRISE, CTX, Photogrammetry ABSTRACT:Images from two sensors, the High-Resolution Imaging Science Experiment (HiRISE) and the Context Camera (CTX), both onboard the Mars Reconnaissance Orbiter (MRO), were used to generate high-quality DEMs (Digital Elevation Models) of the Martian surface. However, there were discrepancies between the DEMs generated from the images acquired by these two sensors due to various reasons, such as variations in boresight alignment between the two sensors during the flight in the complex environment. This paper presents a systematic investigation of the discrepancies between the DEMs generated from the HiRISE and CTX images. A combined adjustment algorithm is presented for the co-registration of HiRISE and CTX DEMs. Experimental analysis was carried out using the HiRISE and CTX images collected at the Mars Rover landing site and several other typical regions. The results indicated that there were systematic offsets between the HiRISE and CTX DEMs in the longitude and latitude directions. However, the offset in the altitude was less obvious. After combined adjustment, the offsets were eliminated and the HiRISE and CTX DEMs were co-registered to each other. The presented research is of significance for the synergistic use of HiRISE and CTX images for precision Mars topographic mapping.

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Section: Inconsistencies Between the Dems Generated From Hirise And Cmentioning

confidence: 99%

COMPARISON AND CO-REGISTRATION OF DEMS GENERATED FROM HiRISE AND CTX IMAGES

Wu¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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%

“…The image was further processed by Region Adjacency Graph (RAG), and good performance was achieved for the matching by combining the characteristics of distance, angle, and normalized correlation coefficient (DANCC). Triangulation-based methods, which are free of any other external data, have also been proven to be effective for image matching [24][25][26]. First, a few points are matched to build the initial triangles.…”

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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“…The EO parameters of the HiRISE and CTX images were retrieved from the SPICE kernels by interpolating the spacecraft's trajectory and pointing vectors based on the observation time. A self-adaptive triangulationconstrained image matching (SATM) method (Wu et al, 2011(Wu et al, , 2012Zhu et al, 2007;Zhu et al, 2010) was used for automatic matching of the stereo images to obtain dense corresponding points in the overlapping region of the stereo images. The 3D coordinates of the matched points were then obtained by photogrammetric intersection based on the image orientation parameters, from which the DEMs were interpolated.…”

Section: Introductionmentioning

confidence: 99%

Improved Large-Scale Slope Analysis on Mars Based on Correlation of Slopes Derived With Different Baselines

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The surface slopes of planetary bodies are important factors for exploration missions, such as landing site selection and rover manoeuvre. Generally, high-resolution digital elevation models (DEMs) such as those generated from the HiRISE images on Mars are preferred to generate detailed slopes with a better fidelity of terrain features. Unfortunately, high-resolution datasets normally only cover small area and are not always available. While lower resolution datasets, such as MOLA, provide global coverage of the Martian surface. Slopes generated from the low-resolution DEM will be based on a large baseline and be smoothed from the real situation. In order to carry out slope analysis at large scale on Martian surface based low-resolution data such as MOLA data, while alleviating the smoothness problem of slopes due to its low resolution, this paper presents an amplifying function of slopes derived from lowresolution DEMs based on the relationships between DEM resolutions and slopes. First, slope maps are derived from the HiRISE DEM (meter-level resolution DEM generated from HiRISE images) and a series of down-sampled HiRISE DEMs. The latter are used to simulate low-resolution DEMs. Then the high-resolution slope map is down-sampled to the same resolution with the slope map from the lower-resolution DEMs. Thus, a comparison can be conducted pixel-wise. For each pixel on the slope map derived from the lowerresolution DEM, it can reach the same value with the down-sampled HiRISE slope by multiplying an amplifying factor. Seven sets of HiRISE images with representative terrain types are used for correlation analysis. It shows that the relationship between the amplifying factors and the original MOLA slopes can be described by the exponential function. Verifications using other datasets show that after applying the proposed amplifying function, the updated slope maps give better representations of slopes on Martian surface compared with the original slopes.

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Propagation strategies for stereo image matching based on the dynamic triangle constraint

Cited by 30 publications

References 18 publications

COMPARISON AND CO-REGISTRATION OF DEMS GENERATED FROM HiRISE AND CTX IMAGES

COMPARISON AND CO-REGISTRATION OF DEMS GENERATED FROM HiRISE AND CTX IMAGES

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

Improved Large-Scale Slope Analysis on Mars Based on Correlation of Slopes Derived With Different Baselines

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