• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.95-97
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• 2005

Communication, Ocean, and Meteorological Satellite (COMS) to be launched in year 2008 will be the first Korean multi-purpose geostationary satellite aiming at three major missions, i.e.: communication, ocean, and meteorological applications. The development of systems for the meteorological mission sponsored by the Korea Meteorological Administration (KMA) consists of payloads, ground system, and data processing system. The program called COMS Meteorological Data Processing System (CMDPS) has been initiated for the development of data processing system. The primary objective ofCMDPS is to derive the level-2 environmental products from geo-Iocated and calibrated level 1.5 COMS data. Preliminary design for the level-2 data processing system consists of 16 baseline products and will be refined by end of 3rd project year. Also considered for the development are the necessary initial information such as land use and digital elevation map, algorithms for the vicarious calibration and procedures for the calibration monitoring, and radiative transfer model. Here, we briefly introduce the overall development strategy, flow chart for the intended baseline products, a few preliminary algorithm results and future plans.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.337-346
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• 2012

In the view from most application system developers and users, cloud computing becomes popular in recent years and is still evolving. But in fact it is not easy to reach at the level of actual operations. Despite, it is known that the cloud in the practical stage provides a new pattern for deploying a geo-spatial application. However, domestically geo-spatial application implementation and operation based on this concept or scheme is on the beginning stage. It is the motivation of this works. Although this study is an introductory level, a simple and practical processed result was presented. This study was carried out on Amazon web services platform, as infrastructure as a service in the geo-spatial areas. Under this environment, cloud instance, a web and mobile system being previously implemented in the multi-layered structure for geo-spatial open sources of database and application server, was generated. Judging from this example, it is highly possible that cloud services with the functions of geo-processing service and large volume data handling are the crucial point, leading a new business model for civilian remote sensing application and geo-spatial enterprise industry. The further works to extend geo-spatial applications in cloud computing paradigm are left.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.141-150
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• 2018

Crop type classification is essential for supporting agricultural decisions and resource monitoring. Remote sensing techniques, especially using hyperspectral imagery, have been effective in agricultural applications. Hyperspectral imagery acquires contiguous and narrow spectral bands in a wide range. However, large dimensionality results in unreliable estimates of classifiers and high computational burdens. Therefore, reducing the dimensionality of hyperspectral imagery is necessary. In this study, the Random Forest (RF) classifier was utilized for dimensionality reduction as well as classification purpose. RF is an ensemble-learning algorithm created based on the Classification and Regression Tree (CART), which has gained attention due to its high classification accuracy and fast processing speed. The RF performance for crop classification with airborne hyperspectral imagery was assessed. The study area was the cultivated area in Chogye-myeon, Habcheon-gun, Gyeongsangnam-do, South Korea, where the main crops are garlic, onion, and wheat. Parameter optimization was conducted to maximize the classification accuracy. Then, the dimensionality reduction was conducted based on RF variable importance. The result shows that using the selected bands presents an excellent classification accuracy without using whole datasets. Moreover, a majority of selected bands are concentrated on visible (VIS) region, especially region related to chlorophyll content. Therefore, it can be inferred that the phenological status after the mature stage influences red-edge spectral reflectance.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.325-333
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• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.243-252
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• 2005

Wavelet transform and its inverse processing provide the effective framework for data fusion. The purpose of this study is to investigate applicability of wavelet transform using texture images for the urban remote sensing application. We tried several experiments regarding image fusion by wavelet transform and texture imaging using high resolution images such as IKONOS and KOMPSAT EOC. As for texture images, we used homogeneity and ASM (Angular Second Moment) images according that these two types of texture images reveal detailed information of complex features of urban environment well. To find out the useful combination scheme for further applications, we performed DWT(Discrete Wavelet Transform) and IDWT(Inverse Discrete Wavelet Transform) using texture images and original images, with adding edge information on the fused images to display texture-wavelet information within edge boundaries. The edge images were obtained by the LoG (Laplacian of Gaussian) processing of original image. As the qualitative result by the visual interpretation of these experiments, the resultant image by each fusion scheme will be utilized to extract unique details of surface characterization on urban features around edge boundaries.

• Proceedings of the KSRS Conference
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• v.1
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• pp.459-462
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• 2006

The diffuse attenuation coefficient for down-welling irradiance ($K_d$) is an important parameter for ocean studies including remote sensing applications. For the vast ocean, ocean color remote sensing is the only possible means to get the fine-scale measurements of $K_d$. To develop a technique of estimating $K_d$ from remotely sensed data, the following underwater optical parameters (absorption coefficient (a), attenuation coefficient (c), scattering coefficient (b), diffuse attenuation coefficient ($K_d$), etc.) have been studied. For this research we conducted the field campaign around the Yellow Sea at $8{\sim}9$ June, 2006. We obtained a set of underwater optical parameter data: down-welling irradiance ($E_d$), up-welling irradiance ($E_u$) and up-welling radiance ($L_u$) using TriOS optical sensors and a, c coefficient using Spectral Absorption and Attenuation Meter (AC-S). We then derived $K_d$ values from $E_d$ for each depth.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.75-85
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• 2006

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.894-896
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• 2003

Koh Chang Island is located near the east border of Thailand. The government of Thailand promotes the island as a model of eco-tourism spots. The Island undeveloped until recent years, is expected to change to major tourist attraction. 'Digital Koh Chang project' has thus. The main objective of this project is to monitor the environment and land use status of the island and to support its sound development. In March 2003, a field survey of this project was planned and field data were collected using both airborne and ground platforms and an ocean vessel. These data were combined with satellite data in the laboratory. This presentation is all balloon-born system field operation. A 5-meter length balloon filled with Helium gas was used, whose payload consisted of two RGB standard color digital still cameras, two directional rotating servo motors, a camera mount cradle as well as signal transmitting and receiving components. A series of aerial high-resolution digital images were rather easily obtained using this inexpensive system, making it possible to monitor intended landscape features in a specific field. Design of simple, low-cost and easily transportable flying platforms and local field surveys using them are useful for getting local ground truth data to calibrate satellite or airborne-based RS data. The design analysis to upgrade the system is further investigated.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.287-305
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• 2016

Over the past half century, satellites have continuously expanded their roles in remote sensing applications. As the number of satellites to be launched are expected to continuously increase in coming years, the research on satellite payloads will be in high demands. Earth Observation (EO) satellites are nowadays widely utilized for various purposes. Especially, Agriculture and forestry applications are considered as their major application areas. Since about 85% of domestic land cover is classified as forest or cropland areas, it would be reasonable to suggest that the demand for these satellites should be of high priority. In this paper, a comprehensive analysis is performed on the technical specifications of satellite payloads that may be applicable to agricultural applications. We attempted to build a solid database on payload specifications by collecting relevant information available from various related institutes and academic research works. A number of experts involved in national agricultural research and satellite development programs have been invited to investigate required payload design. Based on the current technology development status and future plan, multiple options for future satellite payload designs have been suggested bearing in mind that the results may be applicable to the future agriculture and forestry satellite payload design. The proposed payload specifications are analyzed in depth through satellite operation simulations under the mission of observing the national agriculture areas. The proposed design scheme and simulation results may be used as technical references to satellite payload design for future space missions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.423-432
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• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.