• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.798-800
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• 2003

The time shift processing of ground measured surface temperature with the meteorological variables has no evaluated function. We introduce new evaluating function. To use this evaluating function, the algorithm of time-shift processing will be able to be reliable and get error-bar for all moving measured point's data. We will finally obtain the area averaged surface temperature by land observation.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.587-592
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• 2002

A new digital correlator fur an airborne synthetic aperture radiometer was designed in order to replace the conventional analog correlator unit which will become very complicated while the number of channels is increasing. The digital correlator uses digital IQ demodulator instead of the intermediate frequency (IF) phase shifter to make the correlation processing performed digitally at base band instead of analogly at IF. This technique has been applied to the digital receiver in softradio. The down-converted IF signals from each pair of receiver channels become low rate base-band digital signals after under-sampled, Digitally Down-Converted (DDC), decimated and filtered by FIR filters. The digital signals are further processed by two digital multipliers (complex correlation), the products are integrated by the integrators and finally the outputs from the integrators compose of the real part and the imaginary part of a sample of the visibility function. This design is tested by comparing the results from digital correlators and that from analog correlators. They are agreed with each other very well. Due to the fact that the digital correlators are realized with the help of Analog-Digital Converter (ADC) chips and the FPGA technology, the realized volume, mass, power consumption and complexity turned out to be greatly reduced compared with that of the analog correlators. Simulations show that the resolution of ADC has an influence on the synthesized antenna patterns, but this can be neglected if more than 2bit is used.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1067-1082
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• 2019

This review paper presents a review of evidence on systems and technologies for recent remote sensing techniques which were applied into forest and forest related sectors. The paper reviewed remote sensing techniques that will have, or already having, a substantial impact on improving data quality of forest inventory and forest management and planning. The aim of this review is to identify, categorize and discuss Korean and international sources published primarily in the last decades. The focus on remote sensing and ICT technologies examines issues related to their opportunities, limitation, use and impact on the forestry. More specifically, this literature review has focused on laser scanning, satellite imagery, and Unmanned aerial vehicles (UAV) utilization in forest management and inventory analysis.

• 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.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.661-667
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• 2022

As remote sensing and GIS have been considered to be essential technologies for disasters information production, researches on developing methods for analyzing spatial data, and developing new technologies for such purposes, have been actively conducted. Especially, it is assumed that the use of remote sensing and GIS for disaster management will continue to develop thanks to the launch of recent satellite constellations, the use of various remote sensing platforms, the improvement of acquired data processing and storage capacity, and the advancement of artificial intelligence technology. This spatial issue presents 10 research papers regarding ship detection, building information extraction, ocean environment monitoring, flood monitoring, forest fire detection, and decision making using remote sensing and GIS technologies, which can be applied at the disaster prediction, monitoring and response stages. It is anticipated that the papers published in this special issue could be a valuable reference for developing technologies for disaster management and academic advancement of related fields.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.542-543
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• 2005

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.65-65
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• 2008

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• Proceedings of the Korea Air Pollution Research Association Conference
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• 1994.10a
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• pp.82-83
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• 1994

• Proceedings of the Korea Water Resources Association Conference
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• 1995.05a
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• pp.541-547
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• 1995

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.341-369
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• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.