scholarly journals EVALUATION OF STEREO MATCHING COSTS ON CLOSE RANGE, AERIAL AND SATELLITE IMAGES

2012 ◽  
Keyword(s):  
Satellite Images ◽  
Stereo Matching ◽  
Close Range

scholarly journals 3D SURFACE RECONSTRUCTION FROM MULTI-DATE SATELLITE IMAGES

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 313-320
Author(s):  
S. Bullinger ◽  
C. Bodensteiner ◽  
M. Arens
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Stereo Matching ◽  
State Of The Art ◽  
Satellite Image ◽  
Three Dimensional ◽  
Point Clouds ◽  
Reconstruction Algorithms ◽  
Depth Maps ◽  
Median Error

Abstract. The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, a Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available.

scholarly journals A NEW ALGORITHM FOR VOID FILLING IN A DSM FROM STEREO SATELLITE IMAGES IN URBAN AREAS

2016 ◽  
Vol III-1 ◽  
pp. 55-61
Author(s):  
Z. Gharib Bafghi ◽  
J. Tian ◽  
P. d'Angelo ◽  
P. Reinartz
Keyword(s):  
Image Segmentation ◽  
Change Detection ◽  
Urban Areas ◽  
Satellite Images ◽  
Stereo Matching ◽  
Multispectral Image ◽  
Shadow Detection ◽  
Stereo Pair ◽  
Urban Scenes ◽  
Surface Models

Digital Surface Models (DSM) derived from stereo-pair satellite images are the main sources for many Geo-Informatics applications like 3D change detection, object classification and recognition. However since occlusion especially in urban scenes result in some deficiencies in the stereo matching phase, these DSMs contain some voids. In order to fill the voids a range of algorithms have been proposed, mainly including interpolation alone or along with auxiliary DSM. In this paper an algorithm for void filling in DSM from stereo satellite images has been developed. Unlike common previous approaches we didn’t use any external DSM to fill the voids. Our proposed algorithm uses only the original images and the unfilled DSM itself. First a neighborhood around every void in the unfilled DSM and its corresponding area in multispectral image is defined. Then it is analysed to extract both spectral and geometric texture and accordingly to assign labels to each cell in the voids. This step contains three phases comprising shadow detection, height thresholding and image segmentation. Thus every cell in void has a label and is filled by the median value of its co-labelled neighbors. The results for datasets from WorldView-2 and IKONOS are shown and discussed.

scholarly journals Evaluation of Close-Range Stereo Matching Algorithms Using Stereoscopic Measurements

2018 ◽  
Vol 84 (3) ◽  
pp. 159-167
Author(s):  
Dongjoe Shin ◽  
Yu Tao ◽  
Jan-Peter Muller
Keyword(s):  
Stereo Matching ◽  
Close Range

A STEREO VISION-BASED BIN PICKING SYSTEM USING HOPFIELD NEURAL NETWORKS

2009 ◽  
Vol 18 (03) ◽  
pp. 443-463 ◽  
Author(s):  
HOUMAN RASTGAR ◽  
MAJID AHMADI ◽  
MAHER SID-AHMED
Keyword(s):  
Clustering Algorithm ◽  
Stereo Matching ◽  
Vision System ◽  
Hopfield Neural Network ◽  
Position Sensing ◽  
Matching Process ◽  
Bin Picking ◽  
Close Range ◽  
Vision Algorithm ◽  
New Machine

In this paper, a new machine vision algorithm for close-range position sensing and bin picking is presented where a Hopfield Neural Network (HNN) is used for the stereo matching process. Stereo Matching is formulated as an energy minimization task and this minimization is accomplished using the HNNs. Various other important aspects of this Vision System are discussed including camera calibration and objects localization using a clustering algorithm.

A Stereo Matching Algorithm for Non-Metric Digital Images and the Application

2013 ◽  
Vol 838-841 ◽  
pp. 2040-2046
Author(s):  
Jin Chang ◽  
Hao Li ◽  
Ming Fei Wu ◽  
Biao Yang
Keyword(s):  
Stereo Matching ◽  
Digital Images ◽  
Initial Point ◽  
Aerial Images ◽  
Point Matching ◽  
Matching Algorithm ◽  
Matching Process ◽  
Searching Strategy ◽  
Close Range ◽  
Relaxation Matching

Compared to aerial images, there are more complicated image distortion and occlusion in non-metric digital images in the aspect of close-range photogrammetry, which increase the difficulty of the image matching dramatically. Due to the particularity of non-metric digital images taken in alpine and gorge regions, this paper proposes a probability relaxation matching algorithm with an improved searching strategy. The algorithm integrates the gird points with feature points to determine the initial point matching process and conducts multiple constraints in the respects of epipolar lines and parallax to ensure the continuity and correctness of matching. The experiment shows the algorithm is applicable for the matching of non-metric digital images in alpine and gorge regions, whose stereo matching correctness can reach up to 98% in alpine and gorge regions.

scholarly journals An automatic and modular stereo pipeline for pushbroom images

2014 ◽  
Vol II-3 ◽  
pp. 49-56 ◽  
Author(s):  
C. de Franchis ◽  
E. Meinhardt-Llopis ◽  
J. Michel ◽  
J.-M. Morel ◽  
G. Facciolo
Keyword(s):  
Computer Vision ◽  
Satellite Images ◽  
Stereo Matching ◽  
Ad Hoc ◽  
Digital Elevation Models ◽  
Stereo Image ◽  
Satellite Imaging ◽  
Digital Elevation ◽  
Pointing Accuracy ◽  
Earth Observation Satellites

The increasing availability of high resolution stereo images from Earth observation satellites has boosted the development of tools for producing 3D elevation models. The objective of these tools is to produce digital elevation models of very large areas with minimal human intervention. The development of these tools has been shaped by the constraints of the remote sensing acquisition, for example, using ad hoc stereo matching tools to deal with the pushbroom image geometry. However, this specialization has also created a gap with respect to the fields of computer vision and image processing, where these constraints are usually factored out. In this work we propose a fully automatic and modular stereo pipeline to produce digital elevation models from satellite images. The aim of this new pipeline, called <i>Satellite Stereo Pipeline</i> and abbreviated as <i>s2p</i>, is to use (and test) off-the-shelf computer vision tools while abstracting from the complexity associated to satellite imaging. To this aim, images are cut in small tiles for which we proved that the pushbroom geometry is very accurately approximated by the pinhole model. These tiles are then processed with standard stereo image rectification and stereo matching tools. The specifics of satellite imaging such as pointing accuracy refinement, estimation of the initial elevation from SRTM data, and geodetic coordinate systems are handled transparently by s2p. We demonstrate the robustness of our approach on a large database of satellite images and by providing an online demo of s2p.

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