Jun Heo
Yonsei University · Environmental Science
About the Lab
Professor Jun Heo's research lab specializes in advanced 3D sensing, indoor spatial modeling, and remote sensing technologies, with a strong focus on automated data processing and geometric modeling using laser scanning and imaging data. The lab develops innovative methods for tunnel and building interior modeling from terrestrial laser scanner (TLS) data, emphasizing accuracy, efficiency, and integration with Building Information Modeling (BIM). It also conducts cutting-edge research in radiometric normalization of multitemporal images and atmospheric correction for hyperspectral remote sensing, aiming to enhance data reliability for environmental monitoring and change detection. The lab’s work bridges the gap between sensor technology, data processing, and practical applications in civil infrastructure, energy efficiency, and environmental science.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15The image normalization process aims to remove radiometric differences between multitemporal images that are due to nonsurface factors. Accurate normalization is essential for image processing procedures that use multi-date imagery, such as change detection. Linear regression using temporally invariant targets is a widely accepted method for normalization. However, except for the criteria for selecting target points, there is no standard method for conducting this important procedure. This paper
An automated and efficient method for extraction of tunnel cross sections using terrestrial laser scanner (TLS) data is presented. In this process, a three-dimensional (3D) point cloud acquired from the TLS is projected onto a horizon plane, converted to a two-dimensional (2D) planar image, and skeletonized to estimate the tunnel centerline. Stations from which cross sections are extracted are estimated from that centerline, which is vectorized and smoothed for better estimation. From those stat
Automated three-dimensional (3D) modeling of building interiors for an as-built building information model (BIM) incurs critical difficulties because of the complex design of indoor structures and a variety of clutter from scanned point clouds. This paper proposes a scheme for automated 3D geometric modeling of indoor structures, including detailed components such as windows and open doors. Moreover, to produce a regularized model, we imposed constrained least-squares adjustment according to an
The growing interest and use of indoor mapping is driving a demand for improved data-acquisition facility, efficiency and productivity in the era of the Building Information Model (BIM). The conventional static laser scanning method suffers from some limitations on its operability in complex indoor environments, due to the presence of occlusions. Full scanning of indoor spaces without loss of information requires that surveyors change the scanner position many times, which incurs extra work for
In other to save energy, several countries recently made laws related to standby power consumption. To success this exertion, we should consider not only power reduction of consumer electronics itself but also efficient automatic control in networked home environment. In this paper, we present a design approach and implementation result of control mechanism for standby power reduction. Proposed mechanism has the host-agent based structure and uses the IEEE 802.15.4 based ZigBee protocol for comm
The reflectance of the Earth's surface is significantly influenced by atmospheric conditions such as water vapor content and aerosols. Particularly, the absorption and scattering effects become stronger when the target features are non-bright objects, such as in aqueous or vegetated areas. For any remote-sensing approach, atmospheric correction is thus required to minimize those effects and to convert digital number (DN) values to surface reflectance. The main aim of this study was to test the t
Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attr
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 dea
Research Areas
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