KEYWORDS: Statistical modeling, Chemical elements, Mathematical modeling, Unmanned aerial vehicles, Photography, Photogrammetry, Cameras, Data processing, Data modeling, 3D modeling
Resection has been one of the most important content in photogrammetry. It aims at the position and attitude information of camera at the shooting point. However in some cases, the observed values for calculating are with gross errors. This paper presents a robust algorithm that using RANSAC method with DLT model can effectually avoiding the difficulties to determine initial values when using co-linear equation. The results also show that our strategies can exclude crude handicap and lead to an accurate and efficient way to gain elements of exterior orientation.
This article proposed an algorithm combining Hough transform and RANSAC algorithm for automatic extraction of lunar craters. (1) In order to suppress noise, the images were filtered; (2) The edge of image were extracted, subsequently, eliminate false edge points by qualifying the gradient direction and the area of connected domain; (3) The edge images were segmented through Hough transform, gathering the same crater edge points together; (4) The edge images after segmentation were fitted using RANSAC algorithm, getting the high precision parameter. High precision of the algorithm was verified by the experiments of images acquired by the Chang’E-1 satellites.
Facing challenges of external environmental noise, it is necessary to find a robust, accurate and fast image-matching method. This paper proposed a method combining SIFT (Scale Invariant Feature Transform) algorithm and RANSACST (RANdom Sampling Consensus with Statistical Testing). RANSAC-ST algorithm is the improvement of RANSAC, which uses a strategy for best model determination in terms of the statistical characteristics of a deterministic mathematical model for hypothesis testing. It will generate a statistical histogram of all hypothesis fundamental matrices, and then the fundamental matrix whose convergence degree reaches the threshold is regarded as the best model. Experimental results show that with the proposed algorithm, the robustness and computation efficiency of correspondence matching can be effectively improved.
To tackle the problem that classic RANSAC (Random Sample Consensus) is limited by the assumption that a single
model accounts for all of the data inliers, an algorithm of multi-planar-feature fitting from 3D point cloud based on
BaySAC algorithm (Bayes Sample Consensus) is proposed (called multiBaySAC). First, as the mathematical models of
most of primitives to be fitted are determinate, a statistical algorithm of hypothesis model parameters histogram is
proposed to detect potential planar features. Instead of assuming constant prior probabilities of data points and choosing
initial data sets by random as RANSAC, we then implement a conditional sampling method -- BaySAC for robust
parameters estimation of potential planar features, by computing the prior probability of each data point and updating the
inlier probabilities using simplified Bayes’ rule. For the purpose of multiple feature fitting, the sequential application of
the above procedure is implemented following the removal of the detected set of inliers. The proposed approach is tested
with point cloud data of buildings acquired by RIEGL VZ-400 laser scanner. The results show that the proposed
Multi-BaySAC can achieve high computation efficiency and fitting accuracy of multiple planar feature fitting.
This paper presents a new approach to automatic registration of terrestrial laser scanning (TLS) point clouds utilizing a novel robust estimation method by an efficient BaySAC (BAYes SAmpling Consensus). The proposed method directly generates reflectance images from 3D point clouds, and then using SIFT algorithm extracts keypoints to identify corresponding image points. The 3D corresponding points, from which transformation parameters between point clouds are computed, are acquired by mapping the 2D ones onto the point cloud. To remove false accepted correspondences, we implement a conditional sampling method to select the n data points with the highest inlier probabilities as a hypothesis set and update the inlier probabilities of each data point using simplified Bayes’ rule for the purpose of improving the computation efficiency. The prior probability is estimated by the verification of the distance invariance between correspondences. The proposed approach is tested on four data sets acquired by three different scanners. The results show that, comparing with the performance of RANSAC, BaySAC leads to less iterations and cheaper computation cost when the hypothesis set is contaminated with more outliers. The registration results also indicate that, the proposed algorithm can achieve high registration accuracy on all experimental datasets.
Facing challenges of nontraditional geometry, multiple resolutions and the same features sensed from different angles, there are more difficulties of robust correspondence matching for ground images along the optic axis.
A method combining SIFT algorithm and the geometric constraint of the ratio of coordinate differences between image point and image principal point is proposed in this paper. As it can provide robust matching across a substantial range of affine distortion addition of change in 3D viewpoint and noise, we use SIFT algorithm to tackle the problem of image distortion. By analyzing the nontraditional geometry of ground image along the optic axis, this paper derivates that for one correspondence pair, the ratio of distances between image point and image principal point in an image pair should be a value not far from 1. Therefore, a geometric constraint for gross points detection is formed. The proposed approach is tested with real image data acquired by Kodak. The results show that with SIFT and the proposed geometric constraint, the robustness of correspondence matching on the ground images along the optic axis can be effectively improved, and thus prove the validity of the proposed algorithm.
The differential interferometric synthetic aperture radar (SAR)(DInSAR) technique has been applied to the earth surface deformation monitoring in many areas. In this paper, the DInSAR technique is used to process the spaceborne SAR data including C band ENVISAT ASAR, L band JERS SAR, and ALOS PALSAR data to derive the temporal land subsidence information in the Fengfeng coal mine area, Hebei province in China. Since JERS and ALOS do not have precise orbit, an orbit adjustment must be accomplished before the DInSAR interferogram was formed. Twenty-three differential interferograms are derived to show the temporal change of the land subsidence range and position. At the acquisition time of ENVISAT ASAR, the leveling in the Dashucun coal mine in Fengfeng area was carried, the historical excavation data in 8 coal mines in Fengfeng area from 1992 to 2007 were collected as well. In our analysis, the DInSAR results are compared with leveling data and historical excavation data. The comparison results show the DInSAR subsidence results are consistent with the leveling results and the historical excavation data, and the L band DInSAR shows more advantages than C band in the coal mining induced subsidence monitoring in a rural area. The feasibility and limitations in coal mining induced subsidence monitoring with DInSAR are analyzed, and the possibility of underground mining activity monitoring by spaceborne InSAR data is evaluated.The experimental results show that both C and L band can accomplish monitoring mining area subsidence, but C band has more restricted conditions of its perpendicular baseline. In order to get a satisfactory outcome in mining area subsidence by the DInSAR method, the time series of SAR images of every visit and SAR deformation interferograms should be archived.
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