• 한국측량학회지
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• 제30권6_2호
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• pp.653-663
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• 2012

Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.715-720
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• 2002

There are many global/continental or large area land cover mapping projects because land cover is one of key parameters in environmental studies. Though ground truth collection is a important and difficult task in land cover mapping, it is usually performed independently in each project without any cooperation between them. This is the background of the development of Global Land Cover Ground Truth (GLCGT) database by the cooperation of many projects and researchers. The developed GLCGT database will be used freely by any researcher. This cooperative and common development of GLCGT database will realize reliable and continuously improved land cover ground truth data. It also eliminates duplicated efforts of ground truth collection among projects.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.47-49
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• 2003

Automatic land use and land cover change (LUCC) detection via remotely sensed imagery has a wide application in the area of LUCC research, nature resource and environment monitoring and protection. Under the condition that one time (T1) data is existed land use and land cover maps, and another time (T2) data is remotely sensed imagery, how to detect change automatically is still an unresolved issue. This paper developed a land use and land cover class knowledge guided method for automatic change detection under this situation. Firstly, the land use and land cover map in T1 and remote sensing images in T2 were registered and superimposed precisely. Secondly, the remotely sensed knowledge database of all land use and land cover classes was constructed based on the unchanged parcels in T1 map. Thirdly, guided by T1 land use and land cover map, feature statistics for each parcel or pixel in RS images were extracted. Finally, land use and land cover changes were found and the change class was recognized through the automatic matching between the knowledge database of remote sensing information of land use & land cover classes and the extracted statistics in that parcel or pixel. Experimental results and some actual applications show the efficiency of this method.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.13-18
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• 1999

Land cover map of 30 arc-second grid by NOAA AVHRR data for the whole Asia was produced by the authors as the project of the Asian Association on Remote Sensing(AARS). Land cover change monitoring of continental scale by satellite data needs preprocessing to remove undesirable factors due to noises, atmosphere, or the effect by solar zenith angle. The paper describes the method to remove these factors. The most important thing for better mapping/monitoring in the future is the accumulation of ground truth data by many land cover related researchers. The project of the development of Global Land Cover Ground Truth Database(GLCGT-DB) is proposed.

• 한국측량학회지
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• 제2권1호
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• pp.5-16
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• 1984

본 연구는 인공위성(LANDSAT) Data를 이용한 도시역의 토지이용도(피복도)의 작성과 그 경연변화에 관하여 논한 것이다. 구체적으로는 인구증가가 현저한 서울시를 대상으로 1979년과 1983년의 토지피복도를 작성함과 동시에 이들의 4년간에 있어서의 토지피복도의 변화를 고찰하였다. 본 연구에서 개발 정리한 Software와 그 수순을 답습함으로서, 금후 관측수집일이 상이한 LAN-DSAT Data에 대하여 같은 조건으로 도시역에 있어서의 토지피복도의 작성이 가능하게 되었다.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.629-636
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• 2003

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.277-280
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• 2010

There is a need to convert the old low- or medium-resolution satellite image-based thematic mapping to the high-resolution satellite image-based mapping of GSD 1m grade or lower. There is also a need to generate middle- or large-scale thematic maps of 1:5,000 or lower. In this study, the DEM and orthoimage is generated with the KOMPSAT-2 stereo image of Yuseong-gu, Daejeon Metropolitan City. By utilizing the orthoimage, automatic extraction experiments of land cover information are generated for buildings, roads and urban areas, raw land(agricultural land), mountains and forests, hydrosphere, grassland, and shadow. The experiment results show that it is possible to classify, in detail, for natural features such as the hydrosphere, mountains and forests, grassland, shadow, and raw land. While artificial features such as roads, buildings, and urban areas can be easily classified with automatic extraction, there are difficulties on detailed classifications along the boundaries. Further research should be performed on the automation methods using the conventional thematic maps and all sorts of geo-spatial information and mapping techniques in order to classify thematic information in detail.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.679-681
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• 2003

The objectives of this research are as follows. First, to investigate methods for a national-scale land cover map based on multi-temporal classification of MODIS data and multi-spectral classification of Landsat TM data. Second, to investigate methods to p roduce ecological zone maps of Korea based on vegetation, climate, and topographic characteristics. The results of this research can be summarized as follows. First, NDVI and EVI of MODIS can be used to ecological mapping of the country by using monthly phenological characteris tics. Second, it was found that EVI is better than NDVI in terms of atmospheric correction and vegetation mapping of dense forests of the country. Third, several ecological zones of the country can be identified from the VI maps, but exact labeling requires much field works, and sufficient field data and macro-environmental data of the country. Finally, relationship between land cover types and natural environmental factors such as temperature, precipitation, elevation, and slope could be identified.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 춘계학술대회 논문집
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• pp.113-119
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• 2007

This article deals mainly with two objectives viz, 1) the potentiality of very high-resolution(VHR) multi-spectral and pan chromatic QuickBird satellite data in resources mapping over moderate resolution satellite data (IRS LISS III) and 2) the advantages of using object oriented classification method of eCognition software in land use and land cover analysis over the ISODATA classification method. These VHR data offers widely acceptable metric characteristics for cartographic updating and increase our ability to map land use in geometric detail and improve accuracy of local scale investigations. This study has been carried out in the Sukkalampatti mini-watershed, which is situated in the Eastern Ghats of Tamil Nadu, India. The eCognition object oriented classification method succeeded in most cases to achieve a high percentage of right land cover class assignment and it showed better results than the ISODATA pixel based one, as far as the discrimination of land cover classes and boundary depiction is concerned.

• 지적과 국토정보
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• 제46권2호
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• pp.239-251
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• 2016

밴드 수가 많고 밴드 폭이 좁은 초분광영상은 기존의 다중 분광 영상에 비해 각 픽셀이 함유하고 있는 정보가 많아 영상을 이용한 토지피복분류를 하는데 있어 최적의 영상으로 평가 받고 있다. 하지만 초분광영상의 높은 분광해상도로 부터 증가된 데이터의 용량과 노이즈로 인해 다중분광영상을 분석하는 기법을 그대로 적용하기에는 효용성이 떨어진다. 초분광영상의 분석 기법으로서 벡터의 내적을 활용하는 SAM(Spectral Angle Mapping)은 연속적인 스펙트럼을 보이는 초분광영상의 특성을 해석하는데 가장 보편적인 방법이다. 이에 본 연구에서는 분광라이브러리를 이용한 초분광영상의 토지피복분류를 수행하기 위해 SAM기법을 채택하였으나 대기영향의 노이즈로 인해 낮은 정확도를 보였다. 이를 보안하기 위한 방법으로서 Decision Tree 기법을 제안하였고 그 결과, 분류 정확도를 향상시킬 수 있었다.