• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.23.4-23
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• 2001

This paper proposes a new approach to unsupervised classification of remotely sensed images. Fusion of optic images (Landsat TM) and radar data (SAR) has beer used to increase the accuracy of classification. Number of clusters is estimated using generalized Dunns measure. Performance of the proposed method is best observed comparing the classified images with classified aerial images.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.611-619
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• 2013

Although the need of crop production has increased in Mongolia, crop cultivation is very limited because of the harsh climatic and topographic conditions. Crop lands are sparsely distributed with relatively small sizes and, therefore, it is difficult to survey the exact area of crop lands. The study aimed to find an easy and effective way of accurate classification to map crop lands in Mongolia using satellite images. To classify the crop lands over the study area in northern Mongolia, four classifications were carried out by using 1) Thematic Mapper (TM) image August 23, 2) TM image of July 6, 3) combined 12 bands of TM images of July and August, and 4) both TM images of July and August by layered classification. Wheat and potato are the major crop types and they show relatively high variation in crop conditions between July and August. On the other hands, other land cover types (forest, riparian vegetation, grassland, water and bare soil) do not show such difference between July and August. The results of four classifications clearly show that the use of multi-temporal images is essential to accurately classify the crop lands. The layered classification method, in which each class is separated by a subset of TM images, shows the highest classification accuracy (93.7%) of the crop lands. The classification accuracies are lower when we use only a single TM image of either July or August. Because of the different planting practice of potato and the growth condition of wheat, the spectral characteristics of potato and wheat cannot be fully separated from other cover types with TM image of either July or August. Further refinements on the spatial characteristics of existing crop lands may enhance the crop mapping method in Mongolia.

• Proceedings of the KSRS Conference
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• v.1
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• pp.102-105
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• 2006

Forest canopy density is an essentially important for maintaining the diversify flora and fauna in the tropic. But, the natural and human disturbances have an influence over the inconsistency of forest canopy density. So, forest canopy density (FCD) has been threatened in the tropic since a decade. The objective of this study was to examine the dynamics change of the forest canopy density in tropical forest Chitwan, Nepal combine with field survey and remote sensing data. The field survey data of 2001 such as canopy cover percentage, dbh so on and some human disturbances were used. Similarly, Landsat TM 1988 and ETM+ 2001 have also used to predict the dynamic changes of the FCD over the period. Moreover, nonparametric Kruskal- Wallis test has performed for the validation of the results. Data analysis revealed that very few factors i.e. the number of trees, path, and fire had realized statistically significance at P=<0.05. Therefore we concluded that detail analysis could be needed incorporate with additional socioeconomic, climatic, biophysical and institutional factors for the better understanding of the forest canopy dynamic in particular location.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.44-51
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• 2009

The main purpose of this study is to identify urban spatial structure by applying geographic information system and remote sensing data. This study identifies the urban spatial structure of non-megalopolis by analyzing the spatial distribution of population and employment in the case of Daegu metropolitan area. For this purpose, multi-temporal satellite image data (Landsat TM; 1995, 2000 and 2005) were utilized through the geographic information system. The distance-decay estimations in terms of population and employment density show that Daegu region as a whole shows monocentric urban characteristics. However, some evidences of polycentricism such as low explanation power of monocentric urban model, rises in multiple employment centers, decentralization of employment are emerging.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.1
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• pp.81-90
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• 2004

Because satellite images include geometry distortions according to photographing conditions and sensor property, and their spatial and radiational resolution and spectrum resolution are different, it is so difficult to make a precise results of analysis. For comparing more than two images, the precise geometric corrections should be preceded because it necessary to eliminate systematic errors due to basic sensor information difference and non-systematic errors due to topographical undulations. In this study, we did sensor modeling using satellite sensor information to make a basic map of change detection for artificial topography. We eliminated the systematic errors which can be occurred in photographing conditions using GCP and DEM data. The Kompsat EOC images relief could be reduced by precise rectification method. Classifying images which was used for change detections by city and forest zone, the accuracy of the matching results are increased by 10% and the positioning accuracies also increased. The result of change detection using basic map could be used for basic data fur GIS application and topographical renovation.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.57-68
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• 2013

Large-scale coastal construction projects, such as land reclamation and dykes, were constructed from the late twentieth century in Yoobu-Do region. Land reclamation combined with the dynamics of tidal currents may have accelerated local sedimentation and erosion resulting in rapid reformation of coastal topography. This study presents the results of the topography changes around Yoobu-Do by large-scale coastal constructions using time-series waterline extraction technique of Landsat TM/ETM+ data acquired from 1998 to 2012. Furthermore, the Freeman-Durden decomposition was applied to fully polarimetric RADARSAT-2 SAR data in order to analyze the scattering mechanisms of the deposited surface. According to the case study, the deposition areas were over 4.5 $km^2$ and distributed in the east, northeast, and west of Yoobu-Do. In the eastern deposition area, it was found that the scattering mechanism was difference from other deposition areas possibly indicating that different types of soil were deposited.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.1
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• pp.1-11
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• 2000

The hybrid coding algorithm for multi-spectral image obtained from satellite is discussed. As the spatial and spectral resolution of satellite image are rapidly increasing, there are enormous amounts of data to be processed for computer processing and data transmission. Therefore an efficient coding algorithm is essential for multi-spectral image processing. In this paper, VQ(vector quantization), quadtree decomposition, and DCT(discrete cosine transform) are combined to compress the multi-spectral image. VQ is employed for predictive coding by using the fact that each band of multi-spectral image has the same spatial feature, and DCT is for the compression of residual image. Moreover, the image is decomposed into quadtree structure in order to allocate the data bit according to the information content within the image block to improve the coding efficiency. Computer simulation on Landsat TM image shows the validity of the proposed coding algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.2
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• pp.81-91
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• 1991

This thesis aims to improve the analysis accuracy of remotely sensed digital imagery, and the improvement is achieved by considering the weight factors(a priori probabilities) of Bayesian MLC in the classification stage. To be concrete, Bayesian decision theory is studied from remote sensing field of view, and the equations in the n-dimensional form are derived from normal probability density functions. The amount of the misclassified pixels is extracted from probability function data using the thres-holding, and this is a basis of evaluating the classification accuracy. The results indicate that 5.21% of accuracy improvement was carried out. The data used in this study is LANDSAT TM(1985.10.21 ; 116-34), and the study area is within the administrative boundary of Seoul.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.4
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• pp.63-72
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• 2000

The objective of this study was to identify the applicability of AGNPS(Agricultural Nonpoint Source Pollution) model using RS data; Landsat TM merged by KOMPSAT EOC and GIS data. AGNPS model which is well-known distributed nonpoint source pollution model was used as the assessment tool. This model has the capability to adjust the level of pollutant load from farmstead and the fertilization level of upland field. A small agricultural watershed($4.12km^2$) which has 20 livestock farmhouses located in Gosan-myun, Ansung-gun was selected. AGNPS data were prepared by using Arc/Info, GRASS, ER-Mapper and Idrisi. Four storm events in 1999 were used for runoff calibration, and 2 storm events which were measured in hourly-base at 4 locations along the stream were used for water quality(TN, TP) calibration.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.159-168
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• 2004

This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.