Global image matching and surface reconstruction in object space using aerial images

1993 ◽  
Author(s):  
Heinrich Ebner ◽  
Christian Heipke ◽  
Mikael Holm
Keyword(s):  
Surface Reconstruction ◽  
Image Matching ◽  
Aerial Images ◽  
Object Space

scholarly journals A discovery about the positional distribution pattern among candidate homologous pixels and its potential application in aerial multi-view image matching

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ka Zhang ◽  
Wen Xiao ◽  
Yehua Sheng ◽  
Junshu Wang ◽  
Shan Zhang ◽  
...  
Keyword(s):  
Distribution Pattern ◽  
Image Matching ◽  
Positional Distribution ◽  
Reference Value ◽  
Aerial Images ◽  
Matching Method ◽  
Object Space ◽  
Theoretical Inference ◽  
View Matching ◽  
Computing Method

AbstractIn aerial multi-view photogrammetry, whether there is a special positional distribution pattern among candidate homologous pixels of a matching pixel in the multi-view images? If so, can this positional pattern be used to precisely confirm the real homologous pixels? These problems have not been studied at present. Therefore, the study of the positional distribution pattern among candidate homologous pixels based on the adjustment theory in surveying is investigated in this paper. Firstly, the definition and computing method of pixel’s pseudo object-space coordinates are given, which can transform the problem of multi-view matching for confirming real homologous pixels into the problem of surveying adjustment for computing the pseudo object-space coordinates of the matching pixel. Secondly, according to the surveying adjustment theory, the standardized residual of each candidate homologous pixel of the matching pixel is figured out, and the positional distribution pattern among these candidate pixels is theoretically inferred utilizing the quantitative index of standardized residual. Lastly, actual aerial images acquired by different sensors are used to carry out experimental verification of the theoretical inference. Experimental results prove not only that there is a specific positional distribution pattern among candidate homologous pixels, but also that this positional distribution pattern can be used to develop a new object-side multi-view image matching method. The proposed study has an important reference value on resolving the defects of existing image-side multi-view matching methods at the mechanism level.

scholarly journals A CASE STUDY ON THROUGH-WATER DENSE IMAGE MATCHING

2018 ◽  
Vol XLII-2 ◽  
pp. 659-666 ◽  
Author(s):  
G. Mandlburger
Keyword(s):  
Image Matching ◽  
Water Surface ◽  
Point Clouds ◽  
Water Clarity ◽  
Aerial Images ◽  
Mean Deviation ◽  
Range Image ◽  
Surface Mapping ◽  
Dense Image

In the last years, the tremendous progress in image processing and camera technology has reactivated the interest in photogrammetrybased surface mapping. With the advent of Dense Image Matching (DIM), the derivation of height values on a per-pixel basis became feasible, allowing the derivation of Digital Elevation Models (DEM) with a spatial resolution in the range of the ground sampling distance of the aerial images, which is often below 10 cm today. While mapping topography and vegetation constitutes the primary field of application for image based surface reconstruction, multi-spectral images also allow to see through the water surface to the bottom underneath provided sufficient water clarity. In this contribution, the feasibility of through-water dense image matching for mapping shallow water bathymetry using off-the-shelf software is evaluated. In a case study, the SURE software is applied to three different coastal and inland water bodies. After refraction correction, the DIM point clouds and the DEMs derived thereof are compared to concurrently acquired laser bathymetry data. The results confirm the general suitability of through-water dense image matching, but sufficient bottom texture and favorable environmental conditions (clear water, calm water surface) are a preconditions for achieving accurate results. Water depths of up to 5 m could be mapped with a mean deviation between laser and trough-water DIM in the dm-range. Image based water depth estimates, however, become unreliable in case of turbid or wavy water and poor bottom texture.

scholarly journals AN AUTOMATIC FILTER ALGORITHM FOR DENSE IMAGE MATCHING POINT CLOUDS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 703-709 ◽  
Author(s):  
Y. Q. Dong ◽  
L. Zhang ◽  
X. M. Cui ◽  
H. B. Ai
Keyword(s):  
Image Matching ◽  
Iterative Process ◽  
Point Clouds ◽  
High Density ◽  
Aerial Images ◽  
Dense Image ◽  
Seed Points ◽  
Filter Algorithms

Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.

scholarly journals COMPARISON OF SEMI AUTOMATIC DTM FROM IMAGE MATCHING WITH DTM FROM LIDAR

2016 ◽  
Vol XLI-B3 ◽  
pp. 373-380
Author(s):  
Aji Rahmayudi ◽  
Aldino Rizaldy
Keyword(s):  
Image Matching ◽  
Point Cloud ◽  
Statistical Data ◽  
Aerial Images ◽  
Mountainous Terrain ◽  
Topographic Map ◽  
Matching Algorithm ◽  
Time Calculation ◽  
Accuracy Difference ◽  
Map Scale

Nowadays DTM LIDAR was used extensively for generating contour line in Topographic Map. This method is very superior compared to traditionally stereomodel compilation from aerial images that consume large resource of human operator and very time consuming. Since the improvement of computer vision and digital image processing, it is possible to generate point cloud DSM from aerial images using image matching algorithm. It is also possible to classify point cloud DSM to DTM using the same technique with LIDAR classification and producing DTM which is comparable to DTM LIDAR. This research will study the accuracy difference of both DTMs and the result of DTM in several different condition including urban area and forest area, flat terrain and mountainous terrain, also time calculation for mass production Topographic Map. From statistical data, both methods are able to produce 1:5.000 Topographic Map scale.

scholarly journals UNSUPERVISED MULTI-CONSTRAINT DEEP NEURAL NETWORK FOR DENSE IMAGE MATCHING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 163-167
Author(s):  
W. Yuan ◽  
Z. Fan ◽  
X. Yuan ◽  
J. Gong ◽  
R. Shibasaki
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Image Matching ◽  
Deep Neural Network ◽  
Aerial Images ◽  
Surface Model ◽  
Learning Technology ◽  
Ground Sample ◽  
Volume Calculation ◽  
Dense Image

Abstract. Dense image matching is essential to photogrammetry applications, including Digital Surface Model (DSM) generation, three dimensional (3D) reconstruction, and object detection and recognition. The development of an efficient and robust method for dense image matching has been one of the technical challenges due to high variations in illumination and ground features of aerial images of large areas. Nowadays, due to the development of deep learning technology, deep neural network-based algorithms outperform traditional methods on a variety of tasks such as object detection, semantic segmentation and stereo matching. The proposed network includes cost-volume computation, cost-volume aggregation, and disparity prediction. It starts with a pre-trained VGG-16 network as a backend and using the U-net architecture with nine layers for feature map extraction and a correlation layer for cost volume calculation, after that a guided filter based cost aggregation is adopted for cost volume filtering and finally the soft Argmax function is utilized for disparity prediction. The experimental conducted on a UAV dataset demonstrated that the proposed method achieved the RMSE (root mean square error) of the reprojection error better than 1 pixel in image coordinate and in-ground positioning accuracy within 2.5 ground sample distance. The comparison experiments on KITTI 2015 dataset shows the proposed unsupervised method even comparably with other supervised methods.

scholarly journals DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 1005-1009 ◽  
Author(s):  
S. Rhee ◽  
T. Kim
Keyword(s):  
Global Optimization ◽  
Image Matching ◽  
Point Clouds ◽  
Image Space ◽  
Object Space ◽  
3D Point Clouds ◽  
Local Point ◽  
Matching Technique ◽  
Uav Images ◽  
Orientation Parameters

3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

scholarly journals COMPARISON OF SEMI AUTOMATIC DTM FROM IMAGE MATCHING WITH DTM FROM LIDAR

2016 ◽  
Vol XLI-B3 ◽  
pp. 373-380 ◽  
Author(s):  
Aji Rahmayudi ◽  
Aldino Rizaldy
Keyword(s):  
Image Matching ◽  
Point Cloud ◽  
Statistical Data ◽  
Aerial Images ◽  
Mountainous Terrain ◽  
Topographic Map ◽  
Matching Algorithm ◽  
Time Calculation ◽  
Accuracy Difference ◽  
Map Scale

Nowadays DTM LIDAR was used extensively for generating contour line in Topographic Map. This method is very superior compared to traditionally stereomodel compilation from aerial images that consume large resource of human operator and very time consuming. Since the improvement of computer vision and digital image processing, it is possible to generate point cloud DSM from aerial images using image matching algorithm. It is also possible to classify point cloud DSM to DTM using the same technique with LIDAR classification and producing DTM which is comparable to DTM LIDAR. This research will study the accuracy difference of both DTMs and the result of DTM in several different condition including urban area and forest area, flat terrain and mountainous terrain, also time calculation for mass production Topographic Map. From statistical data, both methods are able to produce 1:5.000 Topographic Map scale.

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