• 대한원격탐사학회지
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• 제22권3호
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• pp.211-219
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• 2006

A random forests classifier is applied to multi-sensor data fusion for supervised land-cover classification in order to account for the importance of variable. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. The distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Two different multi-sensor data sets for supervised classification were used to illustrate the applicability of random forests: one with optical and polarimetric SAR data and the other with multi-temporal Radarsat-l and ENVISAT ASAR data sets. From the experimental results, the random forests approach could extract important variables or bands for land-cover discrimination and showed reasonably good performance in terms of classification accuracy.

• 대한원격탐사학회지
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• 제39권1호
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• pp.65-76
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• 2023

The Gamma Portable Radar Interferometer (GPRI) is a ground-based real aperture radar (RAR) that can acquire images with high spatial and temporal resolution. The GPRI ground-based radar used in this study composes three antennas with a Ku-band frequency of 17.1-17.3 GHz (1.73-1.75 cm of wavelength). It can measure displacement over time with millimeter-scale precision. It is also possible to adjust the observation mode by arranging the transmitting and receiving antennas for various applications: i) obtaining differential interferograms through the application of interferometric techniques, ii) generation of digital elevation models and iii) acquisition of full polarimetric data. We introduced the hardware configuration of the GPRI ground-based radar, image acquisition, and characteristics of the collected radar images. The interferometric phase difference has been evaluated to apply the multi-temporal interferometric SAR application (MT-InSAR) using the first observation campaigns at Pusan National University in Geumjeong-gu, Busan.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 춘계학술대회 논문집
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• pp.303-306
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• 2006

이 연구에서는 C 밴드 SAR 자료이면서 서로 다른 편광 상태의 자료를 제공할 수 있는 다중 시기 Radarsat-1 자료와 ENVISAT ASAR 자료를 이용한 토지 피복 분류를 수행하였다. 다중 시기/편광 자료로부터 평균 후방산란계수, 시간적 변이도, 긴밀도 등의 특징을 기본적으로 추출하였고, 이외에 상호 비교를 위해 주성분 분석을 이용한 특징 추출을 시도하였다. 특징들을 이용한 분류기법으로는 Random Forests를 적용하였다. 충남 예당평야 일대를 대상으로 사례연구를 수행한 결과, 주성분 분석을 통한 특징과 다편광 자료를 이용하였을 때 분류 정확도가 향상되는 것으로 나타났다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.37-37
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• 2003

Rice is one of the most important crop in the world and is a major export of Thailand. Optical sensors are not useful for rice monitoring, because most cultivated areas are often obscured by cloud during the growing period, especially in South East Asia. Spaceborne Synthetic Aperture Radar (SAR) such as RADARSAT, can see through regardless of weather condition which make it possible to monitor rice growth and to retrieve rice acreage, using the unique temporal signature of rice fields. This paper presents the result of a study of examining the backscatter behavior of rice using multi-temporal RADARSAT dataset. Ground measurements of paddy parameters and water and soil condition were collected. The ground truth information was also used to identify mature rice crops, orchard, road, residence, and aquaculture ponds. Land use class distributions from the RADARSAT image were analyzed. Comparison of the mean DB of each land use class indicated significant differences. Schematic representation of temporal backscatter of rice crop were plotted. Based on the study carried out in Pathum Thani Province test site, the results showed variation of sigma naught from first tillering vegatative phase until ripenning phase. It is suggested that at least, three radar data acquisitions taken at 3 stages of rice growth circle namely; those are at the beginning of rice growth when the field is still covered with water, in the ear differentiation period, and at the beginning of the harvest season, are required for rice monitoring. This pilot project was an experimental one aiming at future operational rice monitoring and potential yield predicttion.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2004년도 대한지구물리학회.한국지구물리탐사학회 공동학술대회 초록집
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• pp.3-17
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• 2004

Space-borne Earth observation technique is one of the most cost effective and rapidly advancing Earth science research tools today and the potential field and micro-wave radar applications have been leading the discipline. The traditional optical imaging systems including the well known Landsat, NOAA - AVHRR, SPOT, and IKONOS have steadily improved spatial imaging resolution but increasing cloud covers have the major deterrent. The new Earth observation satellites ENVISAT (launched on March 1 2002, specifically for Earth environment observation), ALOS (planned for launching in 2004 - 2005 period and ALOS stands for Advanced Land Observation Satellite), and RADARSAT-II (planned for launching in 2005) all have synthetic aperture radar (SAR) onboard, which all have partial or fully polarimetric imaging capabilities. These new types of polarimetric imaging radars with repeat orbit interferometric capabilities are opening up completely new possibilities in Earth system science research, in addition to the radar altimeter and scatterometer. The main advantage of a SAR system is the all weather imaging capability without Sun light and the newly developed interferometric capabilities, utilizing the phase information in SAR data further extends the observation capabilities of directional surface covers and neotectonic surface displacements. In addition, if one can utilize the newly available multiple frequency polarimetric information, the new generation of space-borne SAR systems is the future research tool for Earth observation and global environmental change monitoring. The potential field strength decreases as a function of the inverse square of the distance between the source and the observation point and geophysicists have traditionally been reluctant to make the potential field observation from any space-borne platforms. However, there have recently been a number of potential field missions such as ASTRID-2, Orsted, CHAMP, GRACE, GOCE. Of course these satellite sensors are most effective for low spatial resolution applications. For similar objects, AMPERE and NPOESS are being planned by the United States and France. The Earth science disciplines which utilize space-borne platforms most are the astronomy and atmospheric science. However in this talk we will focus our discussion on the solid Earth and physical oceanographic applications. The geodynamic applications actively being investigated from various space-borne platforms geological mapping, earthquake and volcano .elated tectonic deformation, generation of p.ecise digital elevation model (DEM), development of multi-temporal differential cross-track SAR interferometry, sea surface wind measurement, tidal flat geomorphology, sea surface wave dynamics, internal waves and high latitude cryogenics including sea ice problems.

• 대한원격탐사학회지
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• 제36권5_3호
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• pp.1109-1123
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• 2020

국토 대부분이 산림으로 구성되어 있는 대한민국은 매 년 많은 산불이 발생한다. 산불은 토양의 전단강도를 약화시켜 산사태에 취약한 토양층을 만들기도 하고, 수목의 복구가능여부에 따라 다른 계획 설립이 필요하기 때문에 산불피해면적 뿐만 아니라 피해강도에 대한 파악도 중요하다. 위성 원격탐사를 통한 산불피해강도 추정 연구가 많이 수행되어 왔으나, NDVI(Normalized Difference Vegetation Index)와 NBR(Normalized Burn Ratio) 등과 같은 단일 인자의 시계열 변화만을 이용하여 피해강도를 파악하기에는 한계가 있다. 본 연구에서는 Sentinel-1A SAR-C (Synthetic Aperture Radar-C)와 Sentinel-2A MSI(Multi Spectral Instrument)센서의 자료를 이용하여 기계학습방법을 통한 산불 피해강도 탐지 모델들을 제시하였다. 2017년 5월 삼척, 2019년 4월 강릉·동해, 2019년 4월 고성·속초 총 세개의 산불사례를 이용하여 RF(Random forest), LR(Logistic regression), SVM(Support Vector Machine)기계학습 모델을 구축하였다. 연구결과, random forest 모델이 82.3%의 총정확도로 가장 높은 성능을 보여주었다. 모델의 범용성 및 학습자료 민감도 확인을 위해 사례교차검증도 추가 시행하였는데, 그 결과 사례들의 시기적 차이에 의한 식생활력 및 재생도의 차이에 민감도가 높음을 확인하였다. 이는 추후 다양한 시공간적 사례를 추가할 시 개선이 될 것으로 보인다.