• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.569-577
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• 2008

The purpose of this study is focused on the development of compound classification process by mixing multitemporal data and annexing a specific image enhancement technique with a specific image classification algorithm, to gain more accurate land information from satellite imagery. That is, this study suggests the classification process using canonical correlation classification technique after principal component analysis for the mixed multitemporal data. The result of this proposed classification process is compared with the canonical correlation classification result of one date images, multitemporal imagery and a mixed image after principal component analysis for one date images. The satellite images which are used are the Landsat 5 TM images acquired on July 26, 1994 and September 1, 1996. Ground truth data for accuracy assessment is obtained from topographic map and aerial photograph, and all of the study area is used for accuracy assessment. The proposed compound classification process showed superior efficiency to appling canonical correlation classification technique for only one date image in classification accuracy by 8.2%. Especially, it was valid in classifying mixed urban area correctly. Conclusively, to improve the classification accuracy when extracting land cover information using Landsat TM image, appling canonical correlation classification technique after principal component analysis for multitemporal imagery is very useful.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.210-213
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• 2006

이 연구에서는 다중시기 Landsat 영상을 이용하여 강원도 일대 임상의 변화를 살펴보고 상록수의 영급을 구분하는 알고리즘을 개발하여 적용하였다. 1980년대에서 현재까지 축적된 Landsat-5와 Landsat-7영상 중에서, 대부분 지역에 활잡목 및 활엽수가 낙엽이 지고 눈이 아직 쌓이지 않을 시기인 11월에 촬영된 영상만을 이용하였다. 각 영상에서 양지바른 상록수, 활엽수, 그늘진 지역, 도시 및 바다 등을 클래스로 지정하여 감돌분류를 하였다. 분류 결과에서 양지바른 상록수만 추출하여 5개의 영상을 이진 분류체계로 조합한 후 임상의 시기적 변화 양상을 관찰한 결과, 강원대 연습림의 조림 기록 및 현황도와 상당히 일치함을 확인하였으며, Path 115, Row 34에 해당하는 강원도 일대로 연구지역을 확대하였다. 향후 Kompsat-2를 비롯한 고해상도 11월 영상이 지속적으로 촬영된다면, 이 연구에서 개발된 이진 분류체계 방법을 통하여 산림변화의 모니터링을 보다 용이하고 효율적으로 할 수 있을 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.621-630
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• 2017

In this paper, a study on classification targeting a main production area of garlic and onion was carried out in order to figure out the applicability of multi-temporal high-resolution satellite imagery for field crop classification. After collecting satellite imagery in accordance with the growth cycle of garlic and onion, classifications using each sing date imagery and various combinations of multi-temporal dataset were conducted. In the case of single date imagery, high classification accuracy was obtained in December when the planting was completed and March when garlic and onion started to grow vigorously. Meanwhile, higher classification accuracy was obtained when using multi-temporal dataset rather than single date imagery. However, more images did not guarantee higher classification accuracy. Rather, the imagery at the planting season or right after planting reduced classification accuracy. The highest classification accuracy was obtained when using the combination of March, April and May data corresponding the growth season of garlic and onion. Therefore, it is recommended to secure imagery at main growth season in order to classify garlic and onion field using multi-temporal satellite imagery.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.560-574
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• 2011

Satellite images that have been obtained periodically and continuously are very effective data to monitor the changes of Earth's surface. Traditionally, the studies on change detection using satellite images have mainly focused on comparison between two results after analyzing two images respectively. However, the interests in researches to catch smooth trends and short duration events from continual multi-temporal images have been increased recently. In this study, we introduce and test an approach based on 3D wavelet transform to analyze the multi-temporal satellite images. 3D wavelet transform can reduce the dimensions of data conserving main trends. Also, it is possible to extract important patterns and to analyze spatial and temporal relations with neighboring pixels using 3D wavelet transform. As a result, 3D wavelet transform is useful to capture the long term trends and short-term events rapidly. In addition, we can expect to get new information through sub-bands of 3D wavelet transform which provide different information by decomposed direction.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.71-79
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• 2002

This research presents particulate distribution map of tidal flats of Hampyung bay using reflectance which extracted from satellite data and field survey data during same periods. The spectrum of particulate composition obtained from Landsat TM data was analysed and 7 scenes of satellite image were classified with ISODATA and K-MEANS methods. The results of unsupervised classification were estimated with in-situ data. The classification accuracy of ISODATA and K-MAMS methods were 84.3% and 85.7%. For validation of classified results of multi-temporal satellite images, TM image of May 1999(reference data), which was classified with field survey data was compared with classified results of multi-temporary satellite data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.125-132
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• 2017

Even after applying an image registration, Very High Resolution (VHR) multi-temporal images acquired from different optical satellite sensors such as IKONOS, QuickBird, and Kompsat-2 show a local misalignment due to dissimilarities in sensor properties and acquisition conditions. As the local misalignment, also referred to as Registration Noise (RN), is likely to have a negative impact on multi-temporal information extraction, detecting and reducing the RN can improve the multi-temporal image processing performance. In this paper, an approach to fine registration between VHR multi-temporal images by considering local distribution of RN is proposed. Since the dominant RN mainly exists along boundaries of objects, we use edge information in high frequency regions to identify it. In order to validate the proposed approach, datasets are built from VHR multi-temporal images acquired by optical satellite sensors. Both qualitative and quantitative assessments confirm the effectiveness of the proposed RN-based fine registration approach compared to the manual registration.

• Aerospace Engineering and Technology
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• v.8 no.1
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• pp.132-137
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• 2009

This study has regard to classification by using multi-temporal SAR data. Multi-temporal JERS-1 SAR images are used for extract the land cover information and possibility. So far, land cover information extracted by high resolution aerial photo, satellite images, and field survey. This study developed on multi-temporal land cover status monitoring and coherence information mapping can be processing by L band SAR image. From July, 1997 to October, 1998 JERS SAR images (9 scenes) coherence values are analyzed and then extracted land cover information factors, so on. This technique which forms the basis of what is called SAR Interferometry or InSAR for short has also been employed in spaceborne systems. In such systems the separation of the antennas, called the baseline is obtained by utilizing a single antenna in a repeat pass.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.52-67
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• 2020

Recently, the Arctic has been exposed to snow-covered land due to melting permafrost every year, and the Korea Geographic Information Institute(NGII) provides polar spatial information service by establishing spatial information of the polar region. However, there is a lack of spatial information on vegetation sensitive to climate change. This research used a multi-temporal Sentinel-2 image to perform land cover classification of the Ny-Ålesund in Arctic Svalbard. In the pre-processing step, 10 bands and 6 vegetation spectral index were generated from multi-temporal Sentinel-2 images. In image-classification step is consisted of extracting the vegetation area through 8-class land cover classification and performing the vegetation species classification. The image classification algorithm used Random Forest to evaluate the accuracy and calculate feature importance through Out-Of-Bag(OOB). To identify the advantages of multi- temporary Sentinel-2 for vegetation classification, the overall accuracy was compared according to the number of images stacked and vegetation spectral index. Overall accuracy was 77% when using single-time Sentinel-2 images, but improved to 81% when using multi-time Sentinel-2 images. In addition, the overall accuracy improved to about 83% in learning when the vegetation index was used additionally. The most important spectral variables to distinguish between vegetation classes are located in the Red, Green, and short wave infrared-1(SWIR1). This research can be used as a basic study that optimizes input characteristics in performing the classification of vegetation in the polar regions.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.135-148
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• 2015

This research is carried out for the land cover change detection in the Nakdong River basin before and after the 4 major rivers restoration project using the LiDAR DEM(Digital Elevation Model) and the multi-temporal Landsat imagery. Firstly the river basin polygon is generated by using the levee boundaries extracted from the LiDAR DEM, and the four river basin imagery are generated from the multi-temporal Landsat-5 TM(Thematic Mapper) and Landsat-8 OLI(Operational Land Imager) imagery by using the generated river basin polygon. Then the main land covers such as river, grass and bare soil are separately generated from the generated river basin imagery by using the image classification method, and the ratio of each land cover in the entire area is calculated. The calculated land cover changes show that the areas of grass and bare soil in the entire area have been significantly changed because of the seasonal change, while the area of the river has been significantly increased because of the increase of the water storage. This paper contributes to proposing an efficient methodology for the land cover change detection in the Nakdong River basin using the LiDAR DEM and the multi-temporal satellite imagery taken before and after the 4 major rivers restoration project.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.568-574
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• 2003

오늘날 급속하게 팽창하는 도시외형과 더불어 도심의 집약적이고 다양한 토지이용 패턴 속에서 계획적인 도시개발을 유도하기 위해서는 도시변화 추세 및 그 상세 정보를 주기적으로 모니터링 할 수 있는 종합적인 도시성장관리시스템이 요구된다 이에 앞서 본 연구에서는 토지이용변화를 바탕으로 한 도시성장 모니터링에 있어서 다중시기 원격탐사자료의 활용성과 그 적용범위를 명확하게 규명함과 동시에 향후 도시성장관리시스템 개발에 필요한 기초 자료를 효과적으로 생성하기 위하여 3단계로 구분하여 연구를 수행하였다. 우선 다중시기 원격탐사자료를 이용한 도시의 외형적 성장을 파악하고, 기존의 토지이용도 및 영상자료를 이용하여 시기별 토지이용도를 생성하고 이를 바탕으로 도시성장과정을 체계적으로 분석하였다. 또한 적용 결과를 통하여 기존자료의 최 신성을 확보하는 한편 막대한 예산을 투자하여 구축된 기존 토지이용자료를 원격탐사자료와 더불어 도시변화/성장 연구에 있어 보다 효과적으로 활용할 수 있는 방안을 제시하였다.