• Wind and Structures
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• v.19 no.3
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• pp.321-337
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• 2014

This study has developed an Enhanced Remote-Sensing (ERS) scale to improve the accuracy and efficiency of using remote-sensing images of residential building to predict their damage conditions. The new scale, by incorporating multiple damage states observable on remote-sensing imagery, substantially reduces measurement errors and increases the amount of information retained. A ground damage survey was conducted six days after the Joplin EF 5 tornado in 2011. A total of 1,400 one- and two-family residences (FR12) were selected and their damage states were evaluated based on Degree of Damage (DOD) in the Enhanced Fujita (EF) scale. A subsequent remote-sensing survey was performed to rate damages with the ERS scale using high-resolution aerial imagery. Results from Ordinary Least Square regression indicate that ERS-derived damage states could reliably predict the ground level damage with 94% of variance in DOD explained by ERS. The superior performance is mainly because ERS extracts more information. The regression model developed can be used for future rapid assessment of tornado damages. In addition, this study provides strong empirical evidence for the effectiveness of the ERS scale and remote-sensing technology for assessment of damages from tornadoes and other wind events.

• Wind and Structures
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• v.15 no.5
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• pp.369-383
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• 2012

Researchers have recently begun using high spatial resolution remote-sensing data, which are automatically captured and georeferenced, to assess damage following natural and man-made disasters, in addition to, or instead of employing the older methods of walking house-to-house for surveys, or photographing individual buildings from an airplane. This research establishes quantitative relationships between the damage states observed at ground-level, and those observed from space using high spatial resolution remote-sensing data, for windstorms, for individual site-built one- or two-family residences (FR12). "Degrees of Damage" (DOD) from the Enhanced Fujita (EF) Scale were determined for ground-based damage states; damage states were also assigned for remote-sensing imagery, using a modified version of Womble's Remote-Sensing (RS) Damage Scale. The preliminary developed model can be used to predict the ground-level damage state using remote-sensing imagery, which could significantly lessen the time and expense required to assess the damage following a windstorm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3364-3382
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• 2023

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC, mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1299-1312
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• 2019

As land remote sensing applications are expanding to the extraction of quantitative information, the importance of atmospheric correction is increasing. Considering the difficulty of atmospheric correction for land images, it should be applied when it is necessary. The quantitative information extraction and time-series analysis on biophysical variables in land surfaces are two major applications that need atmospheric correction. Atmospheric aerosol content and column water vapor, which are very dynamic in spatial and temporal domain, are the most influential elements and obstacles in retrieving accurate surface reflectance. It is difficult to obtain aerosol and water vapor data that have suitable spatio-temporal scale for high- and medium-resolution multispectral imagery. Selection of atmospheric correction method should be based on the availability of appropriate aerosol and water vapor data. Most atmospheric correction of land imagery assumes the Lambertian surface, which is not the case for most natural surfaces. Further BRDF correction should be considered to remove or reduce the anisotropic effects caused by different sun and viewing angles. The atmospheric correction methods of optical imagery over land will be enhanced to meet the need of quantitative remote sensing. Further, imaging sensor system may include pertinent spectral bands that can help to extract atmospheric data simultaneously.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.343-346
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• 2002

Toward more accurate determination of the water cycle in association with climate variability and change as well as baseline data on the impacts of this variability on water resources, the Coordinated Enhanced Observing Period (CEOP) was launched on July 1,2001. The preliminary data period, EOP-1, was implemented from July to September in 2001. The first annual enhanced observing period, EOP-3, is going to start on October 1,2002. CEOP is seeking to achieve a database of common measurements from both in situ and satellite remote sensing, model output, and four-dimensional data analyses (4DDA; including global and regional reanalyses) for a specified period. In this context a number of carefully selected reference stations are linked closely with the existing network of observing sites involved in the GEWEX Continental Scale Experiments, which are distributed across the world. The initial step of CEOP is to develop a pilot global hydro-climatological dataset with global consistency under the climate variability that can be used to help validate satellite hydrology products and evaluate, develop and eventually predict water and energy cycle processes in global and regional models. Based on the dataset, we will address the studies on the inter-comparison and inter-connectivity of the monsoon systems and regional water and energy budget, and a path to down-scaling from the global climate to local water resources, as the second step.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.65-69
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• 2007

The processes of regional scales in the East Korean coastal waters were investigated by analysis of the Synthetic Aperture Radar (SAR) images taken by the European Research Satellites ERS-1, ERS-2 and Envisat. More than 500 quick look frames taken in 1991-2003 were examined to detect the frames with clearly surface expressions of oceanic phenomena. 26 ERS-1/2 SAR and 11 Envisat wide swath Advanced SAR (ASAR) frames were selected and obtained from the European Space Agency in a form of the precision high-resolution images. The following oceanic phenomena and processes were evident in the radar imagery through the Korean costal waters: fronts, currents, eddies, internal waves, island and ship wakes, oil pollution, etc. They manifested themselves in the field of sea surface roughness, their scale ranged from several tens meters to about 100 km. The most common morphology of these phenomena was a series of contrast dark or light curvilinear lines and bands. The joint analysis of the discussed SAR images with other satellite and in situ data supported and enhanced our interpretation of SAR signatures.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.503-516
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• 2019

We have estimated the vertical column density (VCD) of formaldehyde (HCHO) on a global scale using a multiple linear regression method (MRM) with Ozone Monitoring Instrument (OMI) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. HCHO VCDs were estimated in regions of biogenic, pyrogenic, and anthropogenic emissions using independent variables, including $NO_2$ VCD, land surface temperature (LST), an enhanced vegetation index (EVI), and the mean fire radiative power (MFRP), which are strongly correlated with HCHO. To evaluate the HCHO estimates obtained using the MRM, we compared estimates of HCHO VCD data measured by OMI ($HCHO_{OMI}$) with those estimated by multiple linear regression equations (MRE) ($HCHO_{MRE}$). Good MRM performances were found, having the average statistical values (R = 0.91, slope = 1.03, mean bias = $-0.12{\times}10^{15}molecules\;cm^{-2}$, percent difference = 11.27%) between $HCHO_{MRE}$ and $HCHO_{OMI}$ in our study regions where high HCHO levels are present. Our results demonstrate that the MRM can be a useful tool for estimating atmospheric HCHO levels.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1931-1942
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• 2021

As the utility of an optical satellite image with a high spatial resolution (i.e., fine-scale) has been emphasized, recently, various studies of the land surface monitoring using those have been widely carried out. However, the usefulness of fine-scale satellite images is limited because those are acquired at a low temporal resolution. To compensate for this limitation, the spatiotemporal data fusion can be applied to generate a synthetic image with a high spatio-temporal resolution by fusing multiple satellite images with different spatial and temporal resolutions. Since the spatio-temporal data fusion models have been developed for mid or low spatial resolution satellite images in the previous studies, it is necessary to evaluate the applicability of the developed models to the satellite images with a high spatial resolution. For this, this study evaluated the applicability of the developed spatio-temporal fusion models for KOMPSAT-3A and Sentinel-2 images. Here, an Enhanced Spatial and Temporal Adaptive Fusion Model (ESTARFM) and Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM), which use the different information for prediction, were applied. As a result of this study, it was found that the prediction performance of STGDFM, which combines temporally continuous reflectance values, was better than that of ESTARFM. Particularly, the prediction performance of STGDFM was significantly improved when it is difficult to simultaneously acquire KOMPSAT and Sentinel-2 images at a same date due to the low temporal resolution of KOMPSAT images. From the results of this study, it was confirmed that STGDFM, which has relatively better prediction performance by combining continuous temporal information, can compensate for the limitation to the low revisit time of fine-scale satellite images.

• Korean Journal of Geomatics
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• v.2 no.1
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• pp.47-55
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• 2002

Remote sensing is the science and art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation./sup 1)/ EOC (Electro -Optical Camera) sensor loaded on the KOMPSAT-1 (Korea Multi- Purpose Satellite-1) performs the earth remote sensing operation. EOC can get high-resolution images of ground distance 6.6m during photographing; it is possible to get a tilt image by tilting satellite body up to 45 degrees at maximum. Accordingly, the device developed in this study enables to obtain images by photographing one pair of tilt image for the same point from two different planes. KOMPSAT-1 aims to obtain a Korean map with a scale of 1:25,000 with high resolution. The KOMPSAT-1 developed automated feature extraction system based on stereo satellite image. It overcomes the limitations of sensor and difficulties associated with preprocessing quite effectively. In case of using 6, 7 and 9 ground control points, which are evenly spread in image, with 95% of reliability for horizontal and vertical position, 3-dimensional positioning was available with accuracy of 6.0752m and 9.8274m. Therefore, less than l0m of design accuracy in KOMPSAT-1 was achieved. Also the ground position error of ortho-image, with reliability of 95%, is 17.568m. And elevation error showing 36.82m was enhanced. The reason why elevation accuracy was not good compared with the positioning accuracy used stereo image was analyzed as a problem of image matching system. Ortho-image system is advantageous if accurate altitude and production of digital elevation model are desired. The Korean map drawn on a scale of 1: 25,000 by using the new technique of KOMPSAT-1 EOC image adopted in the present study produces accurate result compared to existing mapping techniques involving high costs with less efficiency.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.598-608
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• 2023

To comprehensively grasp the dynamic changes in the coastal terrain and coastal erosion, it is imperative to incorporate temporal and spatial continuity through frequent and continuous monitoring. Recently, there has been a proliferation of research in coastal monitoring using remote sensing, accompanied by advancements in image monitoring and analysis technologies. Remote sensing, typically involves collection of images from aircraft or satellites from a distance, and offers distinct advantages in swiftly and accurately analyzing coastal terrain changes, leading to an escalating trend in its utilization. Remote satellite image-based coastal line detection involves defining measurable coastal lines from satellite images and extracting coastal lines by applying coastal line detection technology. Drawing from the various data sources surveyed in existing literature, this study has comprehensively analyzed encompassing the definition of coastal lines based on satellite images, current status of remote satellite imagery, existing research trends, and evolving landscape of technology for satellite image-based coastal line detection. Based on the results, research directions, on latest trends, practical techniques for ideal coastal line extraction, and enhanced integration with advanced digital monitoring were proposed. To effectively capture the changing trends and erosion levels across the entire Korean Peninsula in future, it is vital to move beyond localized monitoring and establish an active monitoring framework using digital monitoring, such as broad-scale satellite imagery. In light of these results, it is anticipated that the coastal line detection field will expedite the progression of ongoing research practices and analytical technologies.