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 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 ACCELERATION OF TOPOGRAPHIC MAP PRODUCTION USING SEMI-AUTOMATIC DTM FROM DSM RADAR DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 47-54
Author(s):  
Aldino Rizaldy ◽  
Ratna Mayasari
Keyword(s):  
Conventional Method ◽  
Point Cloud ◽  
Radar Data ◽  
Stereo Image ◽  
Human Operator ◽  
Topographic Map ◽  
Tropical Area ◽  
Check Points ◽  
Map Scale ◽  
Government Institution

Badan Informasi Geospasial (BIG) is government institution in Indonesia which is responsible to provide Topographic Map at several map scale. For medium map scale, e.g. 1:25.000 or 1:50.000, DSM from Radar data is very good solution since Radar is able to penetrate cloud that usually covering tropical area in Indonesia. DSM Radar is produced using Radargrammetry and Interferrometry technique. The conventional method of DTM production is using “stereo-mate”, the stereo image created from DSM Radar and ORRI (Ortho Rectified Radar Image), and human operator will digitizing masspoint and breakline manually using digital stereoplotter workstation. This technique is accurate but very costly and time consuming, also needs large resource of human operator. Since DSMs are already generated, it is possible to filter DSM to DTM using several techniques. This paper will study the possibility of DSM to DTM filtering using technique that usually used in point cloud LIDAR filtering. Accuracy of this method will also be calculated using enough numbers of check points. If the accuracy meets the requirement, this method is very potential to accelerate the production of Topographic Map in Indonesia.

scholarly journals 3D RECONSTRUCTION WITH A COLLABORATIVE APPROACH BASED ON SMARTPHONES AND A CLOUD-BASED SERVER

2017 ◽  
Vol XLII-2/W8 ◽  
pp. 187-194 ◽  
Author(s):  
E. Nocerino ◽  
F. Poiesi ◽  
A. Locher ◽  
Y. T. Tefera ◽  
F. Remondino ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Image Matching ◽  
Point Cloud ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Collaborative Approach ◽  
Matching Algorithm ◽  
Dense Image ◽  
Video Feed ◽  
And Performance

The paper presents a collaborative image-based 3D reconstruction pipeline to perform image acquisition with a smartphone and geometric 3D reconstruction on a server during concurrent or disjoint acquisition sessions. Images are selected from the video feed of the smartphone’s camera based on their quality and novelty. The smartphone’s app provides on-the-fly reconstruction feedback to users co-involved in the acquisitions. The server is composed of an incremental SfM algorithm that processes the received images by seamlessly merging them into a single sparse point cloud using bundle adjustment. Dense image matching algorithm can be lunched to derive denser point clouds. The reconstruction details, experiments and performance evaluation are presented and discussed.

scholarly journals HIERARCHICAL APPROACH FOR DETECTING CHANGES WITH THE USE OF DIFFERENT PYRAMID LEVELS IN DENSE IMAGE MATCHING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1615-1620
Author(s):  
M. Pilarska
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Spatial Data ◽  
Image Matching ◽  
Point Cloud ◽  
Main Idea ◽  
Aerial Images ◽  
Major Drawback ◽  
Dense Image ◽  
3D Information

Abstract. Many cities order spatial data systematically, in particular aerial nadir images and orthophotomaps. However, only the orthoimages and orthophotomaps are usually used by the city administration, particularly in spatial planning. Some of the users are not aware of the possibilities as to how the aerial images can be used. Spatial data users, who may not be specialists in photogrammetry, are sometimes not aware that it is possible to obtain 3D information from 2D images as a point cloud. The idea of dense image matching (DIM) is well-known and described in the field of photogrammetry. Although dense image matching is a time- and memory-consuming process, this does not present a major drawback with modern computing. Images for the test area – Warsaw – are characterised by Ground Sampling Distance (GSD) equal to 8 cm. These images can be successfully used in change detection processes, comparing the dense image matching point cloud from two different dates. What is important while considering land cover change detection, is that it is not necessary to generate a detailed and high-density point cloud, e.g. in order to detect changes in buildings. The main idea of the article is to present the possibility of using higher levels of images pyramid in dense image matching within the change detection process as a way to optimize the processing time and point cloud accuracy. Which level of pyramid is needed to detect different changes in urban land cover will also be discussed.

scholarly journals ACCELERATION OF TOPOGRAPHIC MAP PRODUCTION USING SEMI-AUTOMATIC DTM FROM DSM RADAR DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 47-54
Author(s):  
Aldino Rizaldy ◽  
Ratna Mayasari
Keyword(s):  
Conventional Method ◽  
Point Cloud ◽  
Radar Data ◽  
Stereo Image ◽  
Human Operator ◽  
Topographic Map ◽  
Tropical Area ◽  
Check Points ◽  
Map Scale ◽  
Government Institution

Badan Informasi Geospasial (BIG) is government institution in Indonesia which is responsible to provide Topographic Map at several map scale. For medium map scale, e.g. 1:25.000 or 1:50.000, DSM from Radar data is very good solution since Radar is able to penetrate cloud that usually covering tropical area in Indonesia. DSM Radar is produced using Radargrammetry and Interferrometry technique. The conventional method of DTM production is using “stereo-mate”, the stereo image created from DSM Radar and ORRI (Ortho Rectified Radar Image), and human operator will digitizing masspoint and breakline manually using digital stereoplotter workstation. This technique is accurate but very costly and time consuming, also needs large resource of human operator. Since DSMs are already generated, it is possible to filter DSM to DTM using several techniques. This paper will study the possibility of DSM to DTM filtering using technique that usually used in point cloud LIDAR filtering. Accuracy of this method will also be calculated using enough numbers of check points. If the accuracy meets the requirement, this method is very potential to accelerate the production of Topographic Map in Indonesia.

An Image Matching Algorithm Based on SCCH Feature Descriptor

2011 ◽  
Vol 33 (9) ◽  
pp. 2152-2157 ◽  
Author(s):  
Yong-he Tang ◽  
Huan-zhang Lu ◽  
Mou-fa Hu
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Matching Algorithm

An Image Matching Algorithm Based on SIFT and Invariability of Feature Points Set

2011 ◽  
Vol 121-126 ◽  
pp. 701-704
Author(s):  
Xue Tong Wang ◽  
Yao Xu ◽  
Feng Gao ◽  
Jing Yi Bai
Keyword(s):  
Image Matching ◽  
Singular Value ◽  
Feature Points ◽  
Matching Algorithm ◽  
Candidate Feature

Feature points can be used to match images. Candidate feature points are extracted through SIFT firstly. Then feature points are selected from candidate points through singular value decomposing. Distance between feature points sets is computed According to theory of invariability of feature points set, images are matched if the distance is less than a threshold. Experiment showed that this algorithm is available.

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.

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