scholarly journals ADVANCED TIE FEATURE MATCHING FOR THE REGISTRATION OF MOBILE MAPPING IMAGING DATA AND AERIAL IMAGERY

2016 ◽  
Vol XLI-B1 ◽  
pp. 617-623 ◽  
Author(s):  
P. Jende ◽  
M. Peter ◽  
M. Gerke ◽  
G. Vosselman
Keyword(s):  
Urban Areas ◽  
Template Matching ◽  
Feature Detection ◽  
Feature Matching ◽  
Aerial Imagery ◽  
Exterior Orientation ◽  
Mobile Mapping ◽  
Positioning Systems ◽  
Matching Techniques ◽  
Orientation Parameters

Mobile Mapping’s ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform’s position. Consequently, acquired data products’ positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. <br><br> We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. <br><br> This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images’ geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. <br><br> Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability of putative matches and the utilisation of templates instead of feature descriptors. In our experiments discussed in this paper, typical urban scenes have been used for evaluating the proposed method. Even though no additional outlier removal techniques have been used, our method yields almost 90% of correct correspondences. However, repetitive image patterns may still induce ambiguities which cannot be fully averted by this technique. Hence and besides, possible advancements will be briefly presented.

scholarly journals ADVANCED TIE FEATURE MATCHING FOR THE REGISTRATION OF MOBILE MAPPING IMAGING DATA AND AERIAL IMAGERY

2016 ◽  
Vol XLI-B1 ◽  
pp. 617-623
Author(s):  
P. Jende ◽  
M. Peter ◽  
M. Gerke ◽  
G. Vosselman
Keyword(s):  
Urban Areas ◽  
Template Matching ◽  
Feature Detection ◽  
Feature Matching ◽  
Aerial Imagery ◽  
Exterior Orientation ◽  
Mobile Mapping ◽  
Positioning Systems ◽  
Matching Techniques ◽  
Orientation Parameters

Mobile Mapping’s ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform’s position. Consequently, acquired data products’ positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. <br><br> We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. <br><br> This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images’ geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. <br><br> Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability of putative matches and the utilisation of templates instead of feature descriptors. In our experiments discussed in this paper, typical urban scenes have been used for evaluating the proposed method. Even though no additional outlier removal techniques have been used, our method yields almost 90% of correct correspondences. However, repetitive image patterns may still induce ambiguities which cannot be fully averted by this technique. Hence and besides, possible advancements will be briefly presented.

scholarly journals LOW-LEVEL TIE FEATURE EXTRACTION OF MOBILE MAPPING DATA (MLS/IMAGES) AND AERIAL IMAGERY

2016 ◽  
Vol XL-3/W4 ◽  
pp. 19-26 ◽  
Author(s):  
P. Jende ◽  
Z. Hussnain ◽  
M. Peter ◽  
S. Oude Elberink ◽  
M. Gerke ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Urban Areas ◽  
Feature Matching ◽  
Point Clouds ◽  
Position Estimation ◽  
Global Navigation Satellite Systems ◽  
Mobile Mapping ◽  
Low Level ◽  
Data Products ◽  
Matching Techniques

Mobile Mapping (MM) is a technique to obtain geo-information using sensors mounted on a mobile platform or vehicle. The mobile platform’s position is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, especially in urban areas, building structures can obstruct a direct line-of-sight between the GNSS receiver and navigation satellites resulting in an erroneous position estimation. Therefore, derived MM data products, such as laser point clouds or images, lack the expected positioning reliability and accuracy. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary reference data. However, current approaches do not consider errors in height, cannot achieve sub-decimetre accuracy and are often not designed to work in a fully automatic fashion. We propose an automatic pipeline to rectify MM data products by employing high resolution aerial nadir and oblique imagery as horizontal and vertical reference, respectively. By exploiting the MM platform’s defective, and therefore imprecise but approximate orientation parameters, accurate feature matching techniques can be realised as a pre-processing step to minimise the MM platform’s three-dimensional positioning error. Subsequently, identified correspondences serve as constraints for an orientation update, which is conducted by an estimation or adjustment technique. Since not all MM systems employ laser scanners and imaging sensors simultaneously, and each system and data demands different approaches, two independent workflows are developed in parallel. &lt;br&gt;&lt;br&gt; Still under development, both workflows will be presented and preliminary results will be shown. The workflows comprise of three steps; feature extraction, feature matching and the orientation update. In this paper, initial results of low-level image and point cloud feature extraction methods will be discussed as well as an outline of the project and its framework will be given.

scholarly journals LOW-LEVEL TIE FEATURE EXTRACTION OF MOBILE MAPPING DATA (MLS/IMAGES) AND AERIAL IMAGERY

2016 ◽  
Vol XL-3/W4 ◽  
pp. 19-26
Author(s):  
P. Jende ◽  
Z. Hussnain ◽  
M. Peter ◽  
S. Oude Elberink ◽  
M. Gerke ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Urban Areas ◽  
Feature Matching ◽  
Point Clouds ◽  
Position Estimation ◽  
Global Navigation Satellite Systems ◽  
Mobile Mapping ◽  
Low Level ◽  
Data Products ◽  
Matching Techniques

Mobile Mapping (MM) is a technique to obtain geo-information using sensors mounted on a mobile platform or vehicle. The mobile platform’s position is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, especially in urban areas, building structures can obstruct a direct line-of-sight between the GNSS receiver and navigation satellites resulting in an erroneous position estimation. Therefore, derived MM data products, such as laser point clouds or images, lack the expected positioning reliability and accuracy. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary reference data. However, current approaches do not consider errors in height, cannot achieve sub-decimetre accuracy and are often not designed to work in a fully automatic fashion. We propose an automatic pipeline to rectify MM data products by employing high resolution aerial nadir and oblique imagery as horizontal and vertical reference, respectively. By exploiting the MM platform’s defective, and therefore imprecise but approximate orientation parameters, accurate feature matching techniques can be realised as a pre-processing step to minimise the MM platform’s three-dimensional positioning error. Subsequently, identified correspondences serve as constraints for an orientation update, which is conducted by an estimation or adjustment technique. Since not all MM systems employ laser scanners and imaging sensors simultaneously, and each system and data demands different approaches, two independent workflows are developed in parallel. <br><br> Still under development, both workflows will be presented and preliminary results will be shown. The workflows comprise of three steps; feature extraction, feature matching and the orientation update. In this paper, initial results of low-level image and point cloud feature extraction methods will be discussed as well as an outline of the project and its framework will be given.

scholarly journals LAPM: a tool for underwater large-area photo-mosaicking

2013 ◽  
Vol 2 (2) ◽  
pp. 189-198 ◽  
Author(s):  
Y. Marcon ◽  
H. Sahling ◽  
G. Bohrmann
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
End User ◽  
Large Area ◽  
Functional Program ◽  
Navigation Data ◽  
Modern Computer ◽  
Photo Mosaic ◽  
Matching Techniques ◽  
Video Material

Abstract. This paper presents a new tool for large-area photo-mosaicking (LAPM tool). This tool was developed specifically for the purpose of underwater mosaicking, and it is aimed at providing end-user scientists with an easy and robust way to construct large photo-mosaics from any set of images. It is notably capable of constructing mosaics with an unlimited number of images on any modern computer (minimum 1.30 GHz, 2 GB RAM). The mosaicking process can rely on both feature matching and navigation data. This is complemented by an intuitive graphical user interface, which gives the user the ability to select feature matches between any pair of overlapping images. Finally, mosaic files are given geographic attributes that permit direct import into ArcGIS. So far, the LAPM tool has been successfully used to construct geo-referenced photo-mosaics with photo and video material from several scientific cruises. The largest photo-mosaic contained more than 5000 images for a total area of about 105 000 m2. This is the first article to present and to provide a finished and functional program to construct large geo-referenced photo-mosaics of the seafloor using feature detection and matching techniques. It also presents concrete examples of photo-mosaics produced with the LAPM tool.

scholarly journals A PHOTOGRAMMETRY-BASED STRUCTURE FROM MOTION ALGORITHM USING ROBUST ITERATIVE BUNDLE ADJUSTMENT TECHNIQUES

2018 ◽  
Vol IV-4/W6 ◽  
pp. 73-80 ◽  
Author(s):  
S. Verykokou ◽  
C. Ioannidis
Keyword(s):  
Structure From Motion ◽  
Image Matching ◽  
Template Matching ◽  
Bundle Adjustment ◽  
Geometric Constraints ◽  
Exterior Orientation ◽  
Commercial Software ◽  
Ground Control Points ◽  
Oblique Images ◽  
Orientation Parameters

<p><strong>Abstract.</strong> The purpose of this paper is the presentation of a novel algorithm for automatic estimation of the exterior orientation parameters of image datasets, which can be applied in the case that the scene depicted in the images has a planar surface (e.g., roof of a building). The algorithm requires the measurement of four coplanar ground control points (GCPs) in only one image. It uses a template matching method combined with a homography-based technique for transfer of the GCPs in another image, along with an incremental photogrammetry-based Structure from Motion (SfM) workflow, coupled with robust iterative bundle adjustment methods that reject any remaining outliers, which have passed through the checks and geometric constraints imposed during the image matching procedure. Its main steps consist of (i) determination of overlapping images without the need for GPS/INS data; (ii) image matching and feature tracking; (iii) estimation of the exterior orientation parameters of a starting image pair; and (iv) photogrammetry-based SfM combined with iterative bundle adjustment methods. A developed software solution implementing the proposed algorithm was tested using a set of UAV oblique images. Several tests were performed for the assessment of the errors and comparisons with well-established commercial software were made, in terms of automation and correctness of the computed exterior orientation parameters. The results show that the estimated orientation parameters via the proposed solution have comparable accuracy with those ones computed through the commercial software using the highest possible accuracy settings; in addition, double manual work was required by the commercial software compared to the proposed solution.</p>

scholarly journals ROBUST FEATURE MATCHING IN TERRESTRIAL IMAGE SEQUENCES

2018 ◽  
Vol XLII-3 ◽  
pp. 3-7
Author(s):  
A. Abbas ◽  
S. Ghuffar
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Image Sequences ◽  
Image Stitching ◽  
Urban Scenes ◽  
Structure Estimation ◽  
Scene Structure ◽  
Computer Vision Applications ◽  
Matching Techniques ◽  
Repeating Patterns

From the last decade, the feature detection, description and matching techniques are most commonly exploited in various photogrammetric and computer vision applications, which includes: 3D reconstruction of scenes, image stitching for panoramic creation, image classification, or object recognition etc. However, in terrestrial imagery of urban scenes contains various issues, which include duplicate and identical structures (i.e. repeated windows and doors) that cause the problem in feature matching phase and ultimately lead to failure of results specially in case of camera pose and scene structure estimation. In this paper, we will address the issue related to ambiguous feature matching in urban environment due to repeating patterns.

scholarly journals LIDAR AND PHOTOGRAMMETRIC DATASETS INTEGRATION USING SUB-BLOCK OF IMAGES

2020 ◽  
Vol V-1-2020 ◽  
pp. 101-107
Author(s):  
E. Mitishita ◽  
F. Costa ◽  
J. Centeno
Keyword(s):  
Root Mean Square Error ◽  
Bundle Adjustment ◽  
Aerial Imagery ◽  
Mean Square ◽  
Exterior Orientation ◽  
Sensor Orientation ◽  
The Moment ◽  
High Degree ◽  
Orientation Parameters

Abstract. Imagery and Lidar datasets have been used frequently to extract geoinformation. Datasets in the same mapping or geodetic frame is a fundamental condition for this application. Nowadays, Direct Sensor Orientation (DSO) can be considered as a mandatory technology to be used in the aerial photogrammetric survey. Although the DSO provides a high degree of automation process due to the GNSS/INS technologies, the accuracies of the obtained results from the imagery and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to improve the tridimensional accuracies of the aerial imagery and Lidar datasets integration using the 3D photogrammetric intersection of single models (pairs of images) with Exterior Orientation Parameters (EOP) estimated from DSO. A Bundle Adjustment with additional parameters (BBA) of a small sub-block of images is used to refine the Interior Orientation Parameters (IOP) and EOP in the job condition. In the 3D photogrammetric intersection experiments using the proposed approach, the horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar checkpoints, increased around of 25% and 75% respectively.

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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