• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.203-211
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• 2011

Recently, information communication technologies such as smartphone or mobile app greatly affect various application fields including geo-spatial domain. And development scheme of mobile web app or hybrid app regards as the most important computing technology which is combined each advantage of mobile app and mobile web. Despite these trends, it is general case that satellite images are used for the background image for other contents services. With this motivation, hybrid app for geo-metadata as the base for dissemination and service is designed and implemented as the prototype, in this study. At the design stage, HTML5, which is the core technology on an international standardization process for hybrid app, is applied. In the implementation, PhoneGap and Sencha Touch as mobile SDK(Software Development Kit) supporting HTML5 on cross-platform in open sources are used. In prototype, some KOMPSAT-2 images covering small area and mandatory elements in geo-metafata standard are tested. As mobile industry applications and business service models based on satellite images on mobile platform are progressing and diversifying, it is expected that this approach and implemented prototype are considered as an important reference.

• Aerospace Engineering and Technology
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• v.13 no.2
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• pp.80-86
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• 2014

In this study, we the estimated surface normalized difference vegetation index by using the KOrea Multi-Purpose SATellite-3 (KOMPSAT-3) multi-spectral images for comparative analysis. The estimated NDVI from KOMPSAT-3 is used as for comparison with the high resolution GeoEye products. The geometry conditions for atmospheric effects are selected from meta files of KOMPSAT-3 bundle data. The used geometry conditions are consist of solar zenith angle, solar azimuth angle, viewing zenith angle, viewing azimuth angle, and date. And, Atmospheric effects such as attenuation, scattering and absorption were physically simulated from water vapor, ozone and aerosol information. Generally, although ground measurements are important for accurate information, in this study, MODIS atmospheric products are used as atmospheric constituents. The surface reflectance from radiative transfer model is utilized for estimating vegetation index. The present study, to reduce atmospheric and geometry conditions between KOMPSAT-3 and GeoEye having difference observation characteristics, data acquisition time is carefully determined for reliable vegetation spectral characteristics.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.03a
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• pp.249-256
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• 2001

Optical Color Corescanner firstly developed by DMT-GeoTec, Germany and further upgraded through the Korea-Germany joint project is capable of duplicating the core surfaces. The tool uses a digital CCD line camera. As the core is rotated by an electric motor, the camera scans the uppermost line, everytime with a circumferential increment of up to 0.05mm(20pixels/mm) and hence a complete 360$^{\circ}$ unwrapped image(core image) is produced. This paper illustrated diverse research benefits of such core images from several test sites in our country. All scanned images could be stored as a data-base one and easily used with software facilities \circled1 to evaluate a percental distribution of mineral components or grain size etc. not only for the rock classification but also for e.g. the assessment of building stones, \circled2 to study potential reservoirs as a hydrocarbon indicator using ultraviolet fluorescence reflection from cores, \circled3 to facilitate the qualitative and quantitative analysis of fractures, \circled4 to evaluate the fractures and thin bedded reservoirs using spectral color responses. Based on abundant scanning experiments, it would seem that this imaging work should lead to reflecting the future trend in underground survey toward a more comprehensive understanding of the properties and behaviors of in situ rocks.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.343-350
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• 2024

Since the release of Meta's Segment Anything Model (SAM), a large-scale vision transformer generation model with rapid image segmentation capabilities, several studies have been conducted to apply this technology in various fields. In this study, we aimed to investigate the applicability of SAM for water bodies detection and extraction using the QGIS Geo-SAM plugin, which enables the use of SAM with satellite imagery. The experimental data consisted of Compact Advanced Satellite 500 (CAS500)-1 images. The results obtained by applying SAM to these data were compared with manually digitized water objects, Open Street Map (OSM), and water body data from the National Geographic Information Institute (NGII)-based hydrological digital map. The mean Intersection over Union (mIoU) calculated for all features extracted using SAM and these three-comparison data were 0.7490, 0.5905, and 0.4921, respectively. For features commonly appeared or extracted in all datasets, the results were 0.9189, 0.8779, and 0.7715, respectively. Based on analysis of the spatial consistency between SAM results and other comparison data, SAM showed limitations in detecting small-scale or poorly defined streams but provided meaningful segmentation results for water body classification.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.225-227
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• 2003

Traditional photogrammetry and satellite image rectification technique have been developed based on control-points for many decades. These techniques are driven from linked points in image space and the corresponding points in the object space in rigorous colinearity or coplanarity conditions. Recently, digital imagery facilitates the opportunity to use features as well as points for images rectification. These implementations were mainly based on rigorous models that incorporated geometric constraints into the bundle adjustment and could not be applied to the new high-resolution satellite imagery (HRSI) due to the absence of sensor calibration and satellite orbit information. This research is an attempt to establish a new Line Based Transformation Model (LBTM), which is based on linear features only or linear features with a number of ground control points instead of the traditional models that only use Ground Control Points (GCPs) for satellite imagery rectification. The new model does not require any further information about the sensor model or satellite ephemeris data. Synthetic as well as real data have been demonestrated to check the validity and fidelity of the new approach and the results showed that the LBTM can be used efficiently for rectifying HRSI.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.3
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• pp.249-255
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• 2011

The free-stereo mosaics image without GPS/INS and ground control data can be generated by using relative orientation parameters on the 3D model coordinate system. Its origin is located in one reference frame image. A 3D coordinate calculated by conjugate points on the free-stereo mosaic images is represented on the 3D model coordinate system. For determining 3D coordinate on the 3D absolute coordinate system utilizing conjugate points on the free-stereo mosaic images, transformation methodology is required for transforming 3D model coordinate into 3D absolute coordinate. Generally, the 3D similarity transformation is used for transforming each other 3D coordinates. Error of 3D model coordinates used in the free-stereo mosaic images is non-linearly increased according to distance from 3D model coordinate and origin point. For this reason, 3D model coordinates used in the free-stereo mosaic images are difficult to transform into 3D absolute coordinates by using linear transformation. Therefore, methodology for transforming nonlinear 3D model coordinate into 3D absolute coordinate is needed. Also methodology for resampling the free-stereo mosaic image to the geo-stereo mosaic image is needed for overlapping digital map on absolute coordinate and stereo mosaic images. In this paper, we propose a 3D non-linear transformation for converting 3D model coordinate in the free-stereo mosaic image to 3D absolute coordinate, and a 2D non-linear transformation based on 3D non-linear transformation converting the free-stereo mosaic image to the geo-stereo mosaic image.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.57-61
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• 2019

Nowadays, portable digital cameras such as smart phone cameras are being popularly used for entertainment and visual information recording. Given a database of geo-tagged images, a visual location recognition system can determine the place depicted in a query photo. One of the most common visual location recognition approaches is the bag-of-words method where local image features are clustered into visual words. In this paper, we propose a new bag-of-words-based visual location recognition algorithm using time-series streetview database. The proposed algorithm selects only a small subset of image features which will be used in image retrieval process. By reducing the number of features to be used, the proposed algorithm can reduce the memory requirement of the image database and accelerate the retrieval process.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.445-454
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• 2014

In recent days, trend technologies for cloud, bigdata, or mobile, as the important marketable keywords or paradigm in Information Communication Technology (ICT), are widely used and interrelated each other in the various types of platforms and web-based services. Especially, the combination of cloud and mobile is recognized as one of a profitable business models, holding benefits of their own. Despite these challenging aspects, there are a few application cases of this model dealing with geo-based data sets or imageries. Among many considering points for geo-based cloud application on mobile, this study focused on a performance test of mobile cloud of Bayesian image fusion algorithm with satellite images. Two kinds of cloud platform of Amazon and OpenStack were built for performance test by CPU time stamp. In fact, the scheme for performance test of mobile cloud is not established yet, so experiment conditions applied in this study are to check time stamp. As the result, it is revealed that performance in two platforms is almost same level. It is implied that open source mobile cloud services based on OpenStack are enough to apply further applications dealing with geo-based data sets.

• Korean Journal of Remote Sensing
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• v.31 no.1
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• pp.1-9
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• 2015

Mobility and cloud platform have become the dominant paradigm to develop web services dealing with huge and diverse digital contents for scientific solution or engineering application. These two trends are technically combined into mobile cloud computing environment taking beneficial points from each. The intention of this study is to design and implement a mobile cloud application for remotely sensed image fusion for the further practical geo-based mobile services. In this implementation, the system architecture consists of two parts: mobile web client and cloud application server. Mobile web client is for user interface regarding image fusion application processing and image visualization and for mobile web service of data listing and browsing. Cloud application server works on OpenStack, open source cloud platform. In this part, three server instances are generated as web server instance, tiling server instance, and fusion server instance. With metadata browsing of the processing data, image fusion by Bayesian approach is performed using functions within Orfeo Toolbox (OTB), open source remote sensing library. In addition, similarity of fused images with respect to input image set is estimated by histogram distance metrics. This result can be used as the reference criterion for user parameter choice on Bayesian image fusion. It is thought that the implementation strategy for mobile cloud application based on full open sources provides good points for a mobile service supporting specific remote sensing functions, besides image fusion schemes, by user demands to expand remote sensing application fields.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.