scholarly journals Exploiting the Redundancy of Multiple Overlapping Aerial Images for Dense Image Matching Based Digital Surface Model Generation

2017 ◽  
Vol 9 (5) ◽  
pp. 490 ◽  
Author(s):  
Wojciech Dominik
Keyword(s):  
Image Matching ◽  
Aerial Images ◽  
Surface Model ◽  
Model Generation ◽  
Digital Surface Model ◽  
Dense Image

scholarly journals THE POSSIBILITY OF USING IMAGES OBTAINED FROM THE UAS IN CADASTRAL WORKS

2016 ◽  
Vol XLI-B1 ◽  
pp. 909-915 ◽  
Author(s):  
Z. Kurczynski ◽  
K. Bakuła ◽  
M. Karabin ◽  
M. Kowalczyk ◽  
J. S. Markiewicz ◽  
...  
Keyword(s):  
Image Matching ◽  
Bundle Adjustment ◽  
Aerial Photographs ◽  
Surface Model ◽  
Digital Surface Model ◽  
Reference Field ◽  
Alternative Source ◽  
Dense Image ◽  
Low Altitude ◽  
Very High

Updating the cadastre requires much work carried out by surveying companies in countries that have still not solved the problem of updating the cadastral data. In terms of the required precision, these works are among the most accurate. This raises the question: to what extent may modern digital photogrammetric methods be useful in this process? The capabilities of photogrammetry have increased significantly after the introduction of digital aerial cameras and digital technologies. For the registration of cadastral objects, i.e., land parcels’ boundaries and the outlines of buildings, very high-resolution aerial photographs can be used. The paper relates an attempt to use an alternative source of data for this task - the development of images acquired from UAS platforms. Multivariate mapping of cadastral parcels was implemented to determine the scope of the suitability of low altitude photos for the cadastre. In this study, images obtained from UAS with the GSD of 3 cm were collected for an area of a few square kilometres. Bundle adjustment of these data was processed with sub-pixel accuracy. This led to photogrammetric measurements being carried out and the provision of an orthophotomap (orthogonalized with a digital surface model from dense image matching of UAS images). Geometric data related to buildings were collected with two methods: stereoscopic and multi-photo measurements. Data related to parcels’ boundaries were measured with monoplotting on an orthophotomap from low-altitude images. As reference field surveying data were used. The paper shows the potential and limits of the use of UAS in a process of updating cadastral data. It also gives recommendations when performing photogrammetric missions and presents the possible accuracy of this type of work.

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 THE POSSIBILITY OF USING IMAGES OBTAINED FROM THE UAS IN CADASTRAL WORKS

2016 ◽  
Vol XLI-B1 ◽  
pp. 909-915
Author(s):  
Z. Kurczynski ◽  
K. Bakuła ◽  
M. Karabin ◽  
M. Kowalczyk ◽  
J. S. Markiewicz ◽  
...  
Keyword(s):  
Image Matching ◽  
Bundle Adjustment ◽  
Aerial Photographs ◽  
Surface Model ◽  
Digital Surface Model ◽  
Reference Field ◽  
Alternative Source ◽  
Dense Image ◽  
Low Altitude ◽  
Very High

Updating the cadastre requires much work carried out by surveying companies in countries that have still not solved the problem of updating the cadastral data. In terms of the required precision, these works are among the most accurate. This raises the question: to what extent may modern digital photogrammetric methods be useful in this process? The capabilities of photogrammetry have increased significantly after the introduction of digital aerial cameras and digital technologies. For the registration of cadastral objects, i.e., land parcels’ boundaries and the outlines of buildings, very high-resolution aerial photographs can be used. The paper relates an attempt to use an alternative source of data for this task - the development of images acquired from UAS platforms. Multivariate mapping of cadastral parcels was implemented to determine the scope of the suitability of low altitude photos for the cadastre. In this study, images obtained from UAS with the GSD of 3 cm were collected for an area of a few square kilometres. Bundle adjustment of these data was processed with sub-pixel accuracy. This led to photogrammetric measurements being carried out and the provision of an orthophotomap (orthogonalized with a digital surface model from dense image matching of UAS images). Geometric data related to buildings were collected with two methods: stereoscopic and multi-photo measurements. Data related to parcels’ boundaries were measured with monoplotting on an orthophotomap from low-altitude images. As reference field surveying data were used. The paper shows the potential and limits of the use of UAS in a process of updating cadastral data. It also gives recommendations when performing photogrammetric missions and presents the possible accuracy of this type of work.

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 UAV AERIAL SURVEY: ACCURACY ESTIMATION FOR AUTOMATICALLY GENERATED DENSE DIGITAL SURFACE MODEL AND ORTHOTHOTO PLAN

2016 ◽  
Vol XLI-B6 ◽  
pp. 155-159
Author(s):  
M. A. Altyntsev ◽  
S. A. Arbuzov ◽  
R. A. Popov ◽  
G. V. Tsoi ◽  
M. O. Gromov
Keyword(s):  
Survey Data ◽  
Laser Scanning ◽  
Aerial Survey ◽  
Surface Model ◽  
Model Generation ◽  
Digital Surface Model ◽  
Reliable Criterion ◽  
Accuracy Estimation ◽  
Survey Accuracy ◽  
Digital Orthophoto

A dense digital surface model is one of the products generated by using UAV aerial survey data. Today more and more specialized software are supplied with modules for generating such kind of models. The procedure for dense digital model generation can be completely or partly automated. Due to the lack of reliable criterion of accuracy estimation it is rather complicated to judge the generation validity of such models. One of such criterion can be mobile laser scanning data as a source for the detailed accuracy estimation of the dense digital surface model generation. These data may be also used to estimate the accuracy of digital orthophoto plans created by using UAV aerial survey data. The results of accuracy estimation for both kinds of products are presented in the paper.

scholarly journals Digital Surface Model Interpolation Based on 3D Mesh Models

2018 ◽  
Vol 11 (1) ◽  
pp. 24 ◽  
Author(s):  
Soohyeon Kim ◽  
Sooahm Rhee ◽  
Taejung Kim
Keyword(s):  
Image Matching ◽  
3D Visualization ◽  
Interpolation Method ◽  
Stereo Image ◽  
Distribution Model ◽  
Surface Model ◽  
Digital Surface Model ◽  
3D Mesh ◽  
Stereo Image Matching ◽  
Mesh Models

A digital surface model (DSM) is an important geospatial infrastructure used in various fields. In this paper, we deal with how to improve the quality of DSMs generated from stereo image matching. During stereo image matching, there are outliers due to mismatches, and non-matching regions due to match failure. Such outliers and non-matching regions have to be corrected accurately and efficiently for high-quality DSM generation. This process has been performed by applying a local distribution model, such as inverse distance weight (IDW), or by forming a triangulated irregular network (TIN). However, if the area of non-matching regions is large, it is not trivial to interpolate elevation values using neighboring cells. In this study, we proposed a new DSM interpolation method using a 3D mesh model, which is more robust to outliers and large holes. We compared mesh-based DSM with IDW-based DSM and analyzed the characteristics of each. The accuracy of the mesh-based DSM was a 2.80 m root mean square error (RMSE), while that for the IDW-based DSM was 3.22 m. While the mesh-based DSM successfully removed empty grid cells and outliers, the IDW-based DSM had sharper object boundaries. Because of the nature of surface reconstruction, object boundaries appeared smoother on the mesh-based DSM. We further propose a method of integrating the two DSMs. The integrated DSM maintains the sharpness of object boundaries without significant accuracy degradation. The contribution of this paper is the use of 3D mesh models (which have mainly been used for 3D visualization) for efficient removal of outliers and non-matching regions without a priori knowledge of surface types.

Sign in / Sign up

Export Citation Format

Share Document