Hong-Gyoo Sohn

Productive high-complexity 3D city modeling with point clouds collected from terrestrial LiDAR

Abstract To satisfy the needs of photo-realistic and ground-based representation of three-dimensional (3D) city models for a variety of applications, significant efforts have been made to automatically reconstruct detailed 3D building facades from terrestrial LiDAR data. Nonetheless, in real-world applications for high-quality 3D city modeling, three major problems are typically encountered: (1) very low productivity due to fully manual operation, (2) low geometric accuracy of 3D modeling resulting from the process of reducing original LiDAR data, and (3) system failure when importing huge LiDAR data to 3D drawing software. To overcome these limitations, the present study proposes a semi-automatic method entailing a plane component detection based on RANSAC segmentation, boundary tracing of the planar components, and manual drawing of details using the remaining, significantly reduced points. The proposed method was applied to point clouds of various buildings in a high-density area in Korea. In comparison with manual operation, the proposed method was proved to improve modeling productivity in the time-consumption aspect and to facilitate operators’ accurate object drawing. However, for additional automation and completeness of 3D modeling, further study is necessary. The proposed method requires a segmentation algorithm to heuristically determine parameters for the most desirable results as well as to detect curvilinear surfaces in modeling complex and curved facades.

Parallel Processing Method for Airborne Laser Scanning Data Using a PC Cluster and a Virtual Grid

In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning (ALS) data. The method creates a raster digital surface model (DSM) by interpolating point data with inverse distance weighting (IDW), and produces a digital terrain model (DTM) by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing.

Water Area Extraction Using RADARSAT SAR Imagery Combined with Landsat Imagery and Terrain Information

This paper exploits an effective water extraction method using SAR imagery in preparation for flood mapping in unpredictable flood situations. The proposed method is based on the thresholding method using SAR amplitude, terrain information, and object-based classification techniques for noise removal. Since the water areas in SAR images have the lowest amplitude value, the thresholding method using SAR amplitude could effectively extract water bodies. However, the reflective properties of water areas in SAR imagery cannot distinguish the occluded areas caused by steep relief and they can be eliminated with terrain information. In spite of the thresholding method using SAR amplitude and terrain information, noises which interfered with users’ interpretation of water maps still remained and the object-based classification using an object size criterion was applied for the noise removal and the criterion was determined by a histogram-based technique. When only using SAR amplitude information, the overall accuracy was 83.67%. However, using SAR amplitude, terrain information and the noise removal technique, the overall classification accuracy over the study area turned out to be 96.42%. In particular, user accuracy was improved by 46.00%.

Efficient water area classification using radarsat-1 SAR imagery in a high relief mountainous environment

It is important to determine quickly the extent of flooding during extreme cases. Even though SAR imagery with its own energy sources is highly applicable to flood monitoring owing to its sensitivity to the water area, topographic effects caused by local terrain relief must be carefully considered before the actual classification process. Since backscattering coefficients of the shadow area in high relief regions are very similar to those of the water area, it is essential to regard these areas before and after the classification procedure, although the process is a difficult and time-consuming task. In this study, efficient and economical methods for water area classification during floods in mountainous area are described. We tested five different cases using various synthetic aperture radar (SAR) image processing techniques, texture measures, and terrain shape information such as elevation and slope. The case whereby the SAR image was classified with the local slope information exhibited the best result for water area classification, even in small streams of different elevation categories. Consequently in mountainous areas, the combination of a SAR image and local slope information was the most appropriate method in estimating flooded areas.

Road Infrastructure Data Acquisition Using a Vehicle‐Based Mobile Mapping System

This study presents the technology of a vehicle-based mobile mapping system to maintain an up- dated transportation database. The mobile mapping sys- tem that integrates the global positioning system (GPS), the inertial navigation system (INS), and digital cameras has been developed to collect data on position and at- tributes of road infrastructure. The vehicle-based mobile mapping system works by having the GPS and INS record the position and attitude data, and digital cameras take road images. The stereovision system can determine the position of objects that are visible on the image pair in the global coordinate system with GPS and INS data. As field data acquisition is a very expensive task, a mo- bile mapping system offers a greatly improved solution. In this study, we successfully created a road infrastruc- ture map with mobile mapping technology and proposed automatic algorithms for detecting and identifying road signs from road images. The proposed detection algo- rithm includes line and color region extraction processes and uses the Hopfield neural networks. The identification algorithm uses seven invariant moments and parameters that present geometric characteristics. With this combined method, we could successfully detect and identify road signs.

Shadow-Effect Correction in Aerial Color Imagery

Due to the existence of shadows, especially in urban environments, it is difficult to extract semantic information from aerial and high-resolution satellite images. In this paper, an efficient method of correcting shadow effects using multisource data sets in aerial color images is proposed. The proposed method has three steps. First, it accurately detects the shadowed regions using the image geometry and the solar position of the image acquisition data. Then, the detected shadowed regions are segmented according to land surface type. Finally, the shadow effects of the segmented regions are corrected by directly comparing the same nonshadow features with the segmented shadows. In the application part of this paper, the proposed techniques were applied in the extraction of an asphalt road from an image.

Analysis of image fusion methods using IKONOS imagery

In this paper, efforts were made to merge IKONOS panchromatic image with multispectral images using wavelet transform, Intensity-Hue-Saturation (IHS), multiplicative, and Principal Components Analysis (PCA) methods. Numerical comparisons were made to evaluate the effect of different fusion methods on the distortion of spectral characteristics. Likewise, analysis was done on different image fusion results based on land surface materials. Finally, the results of the building outline extraction from fused images and panchromatic image were compared. They show that the extraction of the building boundary from the fused image is better than from the panchromatic image in terms of boundary connection.

Horizontal Positional Accuracy Assessment of Historical ARGON Satellite Photography

A sophisticated photogrammetric adjustment technique was investigated to derive accurate positional information from the digitized declassified historical satellite imagery. We used extended Gauss-Markov model to adjust observation equations for differential rectification. Results show that we can obtain about 150 m horizontal accuracy of orthorectified ARGON image, which corresponds to the horizontal resolution of the image. The result is very promising concerning very wide of coverage (540 km×540 km) of single frame film ARGON imagery whose size is 120mm×120 mm. We identified that the consideration of Earth curvature effect for input photo coordinates is essential for the accurate orthorectification using the very wide ground coverage film. Result also suggests that the usefulness of declassified historical satellite imagery for scientific studies of very wide area when they are employed a sophisticated photogrammetric technique such as differential rectification.

The Waterless Portable Private Toilet: An Innovative Sanitation Solution in Disaster Zones.

Catastrophes can occur without warning and inevitably cause short-term and long-term problems. In disaster zones, having an action plan to alleviate difficulties can reduce or prevent many long-lasting complications. One of the most critical and urgent issues is sanitation. Water, energy, personnel, transportation, and the allocation of resources in disaster areas tend to become very limited during emergencies. Sanitation systems suffer in the process, potentially leading to crises due to unsafe and unhygienic surroundings. This article explores the problems of current sanitation practices in disaster areas and identifies the essential characteristics of sustainable sanitation systems. This study also presents a plan for an innovative and sustainable sanitation system using a waterless, portable, private toilet, in addition to a procedure for collecting and disposing waste. The system is agronomic, is socially acceptable, prevents contact with human waste, and can be used for individuals or families. Environmental pollution and social problems (such as sexual harassment) can be reduced both during and after restoration.

Infiltration route analysis using thermal observation devices (TOD) and optimization techniques in a GIS environment.

Infiltration-route analysis is a military application of geospatial information system (GIS) technology. In order to find susceptible routes, optimal-path-searching algorithms are applied to minimize the cost function, which is the summed result of detection probability. The cost function was determined according to the thermal observation device (TOD) detection probability, the viewshed analysis results, and two feature layers extracted from the vector product interim terrain data. The detection probability is computed and recorded for an individual cell (50 m × 50 m), and the optimal infiltration routes are determined with A* algorithm by minimizing the summed costs on the routes from a start point to an end point. In the present study, in order to simulate the dynamic nature of a real-world problem, one thousand cost surfaces in the GIS environment were generated with randomly located TODs and randomly selected infiltration start points. Accordingly, one thousand sets of vulnerable routes for infiltration purposes could be found, which could be accumulated and presented as an infiltration vulnerability map. This application can be further utilized for both optimal infiltration routing and surveillance network design. Indeed, dynamic simulation in the GIS environment is considered to be a powerful and practical solution for optimization problems. A similar approach can be applied to the dynamic optimal routing for civil infrastructure, which requires consideration of terrain-related constraints and cost functions.