scholarly journals An SRTM-Aided Epipolar Resampling Method for Multi-Source High-Resolution Satellite Stereo Observation

2019 ◽  
Vol 11 (6) ◽  
pp. 678
Author(s):  
Jingwen Hu ◽  
Gui-Song Xia ◽  
Hong Sun
Keyword(s):  
Satellite Images ◽  
Stereo Matching ◽  
Shuttle Radar Topography Mission ◽  
Image Space ◽  
Source Image ◽  
Resampling Method ◽  
Binocular Stereo ◽  
Grid Points ◽  
Image Pairs ◽  
Homography Transformation

Binocular stereo observation with multi-source satellite images used to be challenging and impractical, but is now a valuable research issue with the introduction of powerful deep-learning-based stereo matching approaches. However, epipolar resampling, which is critical for binocular stereo observation, has rarely been studied with multi-source satellite images. The main problem is that, under the multi-source stereo mode, the epipolar-line-direction (ELD) at an image location may vary when computed with different elevations. Thus, a novel SRTM (Shuttle Radar Topography Mission)-aided approach is proposed, where a point is transformed from the original image-space to the epipolar image-space through a global rotation, followed by a block-wise homography transformation. The global rotation transfers the ELDs at the center of the overlapping area to the x-axis, and then block-wise transformation shifts the ELDs of all grid-points to the x-axis and eliminates the y-disparities between the virtual corresponding points. Experiments with both single-source and multi-source stereo images showed that the proposed method is obviously more accurate than the previous methods that do not use SRTM. Moreover, with some of the multi-source image pairs, only the proposed method ensured the y-disparities remained within ±1 pixel.

A Hybrid Dense Matching Method for Satellite Images

2013 ◽  
Vol 726-731 ◽  
pp. 4547-4551
Author(s):  
Sheng Hua Teng ◽  
Ning Yang
Keyword(s):  
Satellite Images ◽  
Matching Problem ◽  
Canny Operator ◽  
Dense Matching ◽  
Edge Points ◽  
Feature Based ◽  
Matching Element ◽  
Grid Points ◽  
Geometrically Constrained ◽  
Image Pairs

To solve the dense matching problem for stereoscopic satellite images, a hybrid matching scheme integrating multiple methods is proposed. This scheme utilizes two types of matching element including grid points and edge points. First the geometrically constrained cross-correlation (GC3) method is used to extract matching grids. While in less textured regions using GC3 cannot get sufficient matching grids, so the local affine transformation is used to establish the region correspondence, and more matching grids can be generated. Edges are extracted by Canny operator and approximated with a series of straight edge segments using a polygon approximation. Based on these approximated edges, edge correspondences between image pairs are established using GC3. This scheme fuses the region based and the feature based matching methods. It has been tested with real satellite images and the results demonstrate its accuracy and efficiency.

scholarly journals High-Accuracy Recognition and Localization of Moving Targets in an Indoor Environment Using Binocular Stereo Vision

2021 ◽  
Vol 10 (4) ◽  
pp. 234
Author(s):  
Jing Ding ◽  
Zhigang Yan ◽  
Xuchen We
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Target Localization ◽  
Parallel Structure ◽  
Moving Target ◽  
Target Area ◽  
Moving Targets ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

To obtain effective indoor moving target localization, a reliable and stable moving target localization method based on binocular stereo vision is proposed in this paper. A moving target recognition extraction algorithm, which integrates displacement pyramid Horn–Schunck (HS) optical flow, Delaunay triangulation and Otsu threshold segmentation, is presented to separate a moving target from a complex background, called the Otsu Delaunay HS (O-DHS) method. Additionally, a stereo matching algorithm based on deep matching and stereo vision is presented to obtain dense stereo matching points pairs, called stereo deep matching (S-DM). The stereo matching point pairs of the moving target were extracted with the moving target area and stereo deep matching point pairs, then the three dimensional coordinates of the points in the moving target area were reconstructed according to the principle of binocular vision’s parallel structure. Finally, the moving target was located by the centroid method. The experimental results showed that this method can better resist image noise and repeated texture, can effectively detect and separate moving targets, and can match stereo image points in repeated textured areas more accurately and stability. This method can effectively improve the effectiveness, accuracy and robustness of three-dimensional moving target coordinates.

Research and Realization of a New Algorithm for Stereo Matching Based on Binocular Vision

2013 ◽  
Vol 670 ◽  
pp. 202-207 ◽  
Author(s):  
Jun Ting Cheng ◽  
C. Zhao ◽  
W.L. Zhao ◽  
W.H. Wu
Keyword(s):  
Binocular Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Cnc Machining ◽  
Matching Algorithm ◽  
Evaluation Test ◽  
Three Dimensional Measurement ◽  
Binocular Stereo ◽  
Measuring Machine ◽  
3D Solid

In the development of a three-dimensional measurement system, binocular stereo matching is the most important and difficult. In the basis of introducing selective principles of matching algorithm, a new stereo matching algorithm for binocular vision is put forward that is named noncoded difference measuring distance. The algorithm effectively grapples with the problem of searching for the coincidence relation of raster and can efficiently and accurately obtain three-dimensional world coordinates of the entities. Experiment results show that this 3D measuring machine can effectively measure the 3D solid profile of free surface. During the evaluation test for accuracy, scan a standard plane. Fit all 3D points in one plane, and then the flatness value of this plane is obtained. The flatness value of the standard plane has been ultimately measured as: ± 0.0462mm, this measuring accuracy can completely satisfy the requirements of rapid prototyping or CNC machining, it as well as achieves the stated accuracy (± 0.05mm).

scholarly journals Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network

2020 ◽  
Vol 3 (1) ◽  
pp. 491-500
Author(s):  
Matin Ghaziani ◽  
Erhan İlhan Konukseven ◽  
Ahmet Buğra Koku
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
Aerial Image ◽  
Source Image ◽  
Road Extraction ◽  
Segmentation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Neural Network Input

Road detection from the satellite images can be considered as a classification process in which pixels are divided into the road and non-road classes. In this research, an automatic road extraction using an artificial neural network (ANN) based on automatic information extraction from satellite images and self-adjusting of the hidden layer proposed. Parameters of non-urban road networks from satellite images using a histogram-based binary image segmentation technique are also presented. The segmentation method is implemented by determining a global threshold, which is obtained from a statistical analysis of a number of sample satellite images and their ground truths. The thresholding method is based on two major facts: first, the points corresponding to non-asphalt roads are brighter than other areas in non-urban images. Second, it is observed that in an aerial image, the area covered by roads is only a small fraction of total pixels. It is also observed that pixels corresponding to roads are generally populated at the very bright end of the image greyscale histogram. In this method, at first, the possible road pixels are selected by the proposed segmentation method. Then different parameters, including color, gradient, and entropy, are computed for each pixel from the source image. Finally, these features are used for the artificial neural network input. The results show that the accuracy of the proposed road extraction method is around 80%.

The Implementation of Distance Measurement Using Binocular Vision

2012 ◽  
Vol 490-495 ◽  
pp. 711-715
Author(s):  
Alin Hou ◽  
Ying Geng ◽  
Xue Cui ◽  
Wen Ju Yuan ◽  
Feng Guang Shi
Keyword(s):  
Camera Calibration ◽  
Binocular Vision ◽  
Stereo Vision ◽  
Distance Measurement ◽  
Simulation Experiments ◽  
Visual Measurement ◽  
Actual Distance ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Image Pairs

The method of distance measurement based on binocular stereo vision is presented in this paper. The model of camera imaging is established and the process of camera calibration is described. Interior and external parameters of two cameras are calculated by HALCON. The system of visual measurement is fabricated. The parallax measurement of the image pairs is accomplished. The simulation experiments of distance measurement have been done and the calculated results can be in accordance with the actual distance.

scholarly journals Vision System of Mobile Robot Combining Binocular and Depth Cameras

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yuxiang Yang ◽  
Xiang Meng ◽  
Mingyu Gao
Keyword(s):  
Mobile Robot ◽  
3D Reconstruction ◽  
Trajectory Planning ◽  
Stereo Matching ◽  
Vision System ◽  
Depth Camera ◽  
Structural Constraints ◽  
Depth Cameras ◽  
Stereo Cameras ◽  
Binocular Stereo

In order to optimize the three-dimensional (3D) reconstruction and obtain more precise actual distances of the object, a 3D reconstruction system combining binocular and depth cameras is proposed in this paper. The whole system consists of two identical color cameras, a TOF depth camera, an image processing host, a mobile robot control host, and a mobile robot. Because of structural constraints, the resolution of TOF depth camera is very low, which difficultly meets the requirement of trajectory planning. The resolution of binocular stereo cameras can be very high, but the effect of stereo matching is not ideal for low-texture scenes. Hence binocular stereo cameras also difficultly meet the requirements of high accuracy. In this paper, the proposed system integrates depth camera and stereo matching to improve the precision of the 3D reconstruction. Moreover, a double threads processing method is applied to improve the efficiency of the system. The experimental results show that the system can effectively improve the accuracy of 3D reconstruction, identify the distance from the camera accurately, and achieve the strategy of trajectory planning.

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