• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.476-483
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• 2002

In the past 20 years or so, numerous methods to reduce speckle in SAR images have been proposed. The primary goal of these methods is to reduce speckle without destroying resolution and smearing edge information. But the experiments indicate that there is always a kind of tradeoff between smoothing out speckle and preserving edge information. In this paper, an optimal SAR speckle filter is developed. It can effectively smooth out speckle while preserve edge information.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1377-1379
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• 2003

Satellite and aerial remote sensing (RS) techniques have been provided to collect spatial data globally over the last few decades. However in developing countries such as China, there is still an urgent need for low cost and high resolution RS data. As an emerging RS platform, commercial Unmanned Aerial Vehicle (UAV) integrated with state-of-the-art sensors and information technologies has the potential to become a low cost tool to meet application demands. In this paper, the architecture of UAV RS operation system is mentioned. Moreover, key technologies in UAV RS system are analyzed and current work is reported.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1397-1399
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• 2003

When and Where, What kind of agricultural products will be produced and provided for the market? It is a commercial requirement, and also an academic questions to remote sensing technology. Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the hyperspectral remote sensing should do play a key role in crop growth investigation at national, regional and global scales. In the past five years, Chinese academy of sciences and Japan NTT-DATA have made great efforts to establish a prototype information service system to dynamically survey the vegetable planting situation in Nagano area of Japan mainly based on remote sensing data. For such concern, a flexible and light-duty flight system and some practical data processing system and some necessary background information should be rationally made together. In addition, some studies are also important, such as quick pre-processing for hyperspectral data, Multi-temporal vegetation index analysis, hyperspectral image classification in support of GIS data, etc. In this paper, several spectral data analysis models and a designed airborne platform are provided and discussed here.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1161-1171
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• 2019

As there is a growing interest in remote sensing applications, the Korean Society of Remote Sensing has published several special issues in which articles on specific research topics were collected. In this special issue, we first review the research topics on several special issues published in the Korean Journal of Remote Sensing by 1) the National Institute of Agricultural Sciences (NAS), the Korea Environment Institute (KEI), the Ministry of Environment (ME) and the Korea Aerospace Research Institute (KARI) in 2017 and 2) the Korea Institute of Ocean Science and Technology (KIOST), the Korea Polar Research Institute (KOPRI), KARI, and Korea Meteorological Administration (KMA) in 2018. Then, research articles on the remote sensing applications in Korea are introduced.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.123-125
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• 2003

Satellite IKONOS images are one of important remote sensing data sources as today because of their very high spatial resolution. Their detections for islands and offshore oceanic features with multi-dimension and multi-scales information, specially some small islands, are of great potential. Their application abilities in islands and offshore detections are addressed at the first of the paper. And image processing technologies and the information extracting methodologies are described. Some results on remote sensing of the islands and their nearby object features are shown in details. Discussions and conclusions are carried out simply at the final.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1400-1402
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• 2003

The objective of this paper is to determine the suitable spectral bands for monitoring growth status change during a long period. The long-term ground-level reflectance spectra as well as LAI and biomass were obtained in xiaotangshan area, Beijing, 2001. The narrow-band NDVI type spectral indices by all possible two bands were calculated their correlation coefficients R$^2$ with biomass and LAI. The best NDVIs must have higher R$^2$ with both biomass and LAI. The reasonable band centers and band widths were determined by a systematically increasing bandwidth centered over a wavelength. In addition, the first 19 bands of MODIS were simulated and investigated. Each developed spectral indices was then validated by the biomass and LAI time series using the generalized vector angle. It turned out that six new NDVI type indices within 750-1400nm were developed. NDVI(811_10,957_10) and NDVI(962_10,802_10) performed best. No satisfactory conventional NDVI formed by red and NIR bands were found effective. MODIS_NDVI(band19, band17) and MODIS_NDVI(band19, band2) were much better than MODIS_NDVI(band2,band1) for growth monitoring.

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.22-46
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• 2005

Spatial and timely information on crop and filed conditions is one of the most important basics for rational and efficient planning and management in agriculture. Remote sensing, GIS, and modeling are powerful tools for such applications. This paper presents an overview of the state of the art in remote sensing of crop and field conditions with some case studies. It is also shown that a synergistic linkage between process-based models and remote sensing signatures enables us to estimate the multiple crop/ecosystem variables at a dynamic mode. Remotely sensed information can greatly reduce the uncertainty of simulation models by compensating for insufficient availability of data or parameters. This synergistic approach allows the effective use of infrequent and multi-source remote sensing data for estimating important ecosystem variables such as biomass growth and ecosystem $CO_2$ flux. This paper also shows a geo-spatial information system that enables us to integrate, search, extract, process, transform, and calculate any part of the data based on ID#, attributes, and/or by river-basin boundary, administrative boundary, or boundaries of arbitrary shape/size all over Japan. A case study using the system demonstrates that the nitrogen load from fertilizer was closely related to nitrate concentration of groundwater. The combined use of remote sensing, GIS and modeling would have great potential for various agro-ecosystem applications.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.125-127
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• 2004

High-resolution satellite images offer abundance information of the earth surface for remote sensing applications. The information includes geometry, texture and attribute characteristic. The pixel-based image classification can't satisfy high-resolution satellite image's classification precision and produce large data redundancy. Object-oriented information extraction not only depends on spectrum character, but also use geometry and structure information. It can provide an accessible and truly revolutionary approach. Using Beijing Spot 5 high-resolution image and object-oriented classification with the eCognition software, we accomplish the cultures' precise classification. The test areas have five culture types including water, vegetation, road, building and bare lands. We use nearest neighbor classification and appraise the overall classification accuracy. The average of five species reaches 0.90. All of maximum is 1. The standard deviation is less than 0.11. The overall accuracy can reach $95.47\%.$ This method offers a new technology for high-resolution satellite images' available applications in remote sensing culture classification.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1052-1054
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• 2003

RAZAKSAT is a second micro-satellite mission by Malaysian Satellite Program and is expected for launch in June 2004. Designed to orbit the earth at low-equatorial orbit, RAZAKSAT will meet Malaysia’s immediate needs to rapid data acquisition (real time and more repetitions) to address many operational issues of remote sensing applications, which require availability of current data sets. RAZAKSAT will be among the first remote sensing satellite to orbit the earth at low inclination along the equator, 9$^{\circ}$ with 685km altitude, hence, allows optimal geographical information and environment change within equatorial region be observed with a unique revisit characteristics. The satellite primary payload is MAC, a push-broom type camera with 2.5m of ground sampling distance (GSD) in panchromatic band and 5m of GSD in four multi-spectral bands. This paper describes on the variation of illumination anticipated from simulated RAZAKSAT image, examine its implication to its ground leaving radiances for major applications.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1581-1587
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• 2022

Recently, deep learning has become more important in remote sensing data processing. Huge amounts of data for artificial intelligence (AI) has been designed and built to develop new technologies for remote sensing, and AI models have been learned by the AI training dataset. Artificial intelligence models have developed rapidly, and model accuracy is increasing accordingly. However, there are variations in the model accuracy depending on the person who trains the AI model. Eventually, experts who can train AI models well are required more and more. Moreover, the deep learning technique enables us to automate methods for remote sensing applications. Methods having the performance of less than about 60% in the past are now over 90% and entering about 100%. In this special issue, thirteen papers on how deep learning techniques are used for remote sensing applications will be introduced.