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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Remote Sensing
Journal Basic Information
Journal DOI :
The Korean Society of Remote Sensing
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Volume & Issues
Volume 18, Issue 6 - Dec 2002
Volume 18, Issue 5 - Oct 2002
Volume 18, Issue 4 - Aug 2002
Volume 18, Issue 3 - Jun 2002
Volume 18, Issue 2 - Apr 2002
Volume 18, Issue 1 - Feb 2002
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Particulate Distribution Map of Tidal Flat using Unsupervised Classification of Multi-Temporary Satellite Data
Korean Journal of Remote Sensing, volume 18, issue 2, 2002, Pages 71~79
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.
DEM Extraction from KOMPSAT-1 EOC Stereo Images and Accuracy Assessment
Korean Journal of Remote Sensing, volume 18, issue 2, 2002, Pages 81~90
We carried out accuracy assessment for DEM extraction from the KOMPSAT-1 EOC stereo images over Daejeon and Nonsan in Korea. DEM generation divided into two parts. One is camera modeling and the other stereo matching. We used Orun & Natarajan's(1994) model and Gupta & Hartley's(1997) model in the camera modeling step and checked the possibility using Orun & Natarajan and Gupta & Hartley's models in EOC stereo pairs. For stereo matching, we used an algorithms developed in-house for SPOT images and showed that this algorithm could work with EOC images. Using these algorithms, DEMs were successfully generated from EOC images. The comparison of DEM from EOC Images with a DEM from SPOT Images showed that EOC could be used for high-accuracy DEM generation.
Performance Characteristics for the Variation of Altitude and Tilt Angle in the Satellite Imager Using Time Delay and Integration(TDI)
Korean Journal of Remote Sensing, volume 18, issue 2, 2002, Pages 91~96
The performance characteristics of a satellite imager using a Time Delay and Integration(TDI) Charge Coupled Device (CCD) with fixed integration time is investigated for the variation of satellite altitude and tilt angle. In consequence of the investigation TDI synchronization using tilt imaging is proposed as a solution to compensate geometric performance degradation due to altitude decrease. The tilt angle optimized for the TDI synchronization at decreased altitude is presented. This result can be used for a TDI CCD imager with variable integration time in a certain range as well.
A Simplified Strategy for the Epipolar Geometry of Linear Pushbroom Imagery
Korean Journal of Remote Sensing, volume 18, issue 2, 2002, Pages 97~105
In this paper, we proposed a simplified strategy for the epipolarity of linear pushbroom imagery. The proposed strategy is verified on "Gupta and Hartly" sensor model and "Orun and Natarajan" sensor model. It is also compared with the precise epipolarity model of each sensor model on SPOT and KOMPSAT imagery. For the quantitative analysis, 20 ground control points are used as independent checking points. Based on the results, the accuracy of the proposed strategy is not different from that of the precise epipolarity model of each sensor model (below 0.1 pixels). Under the worst circumstance, the proposed strategy is robust. We can assure that the proposed strategy will show high accuracy on most of sensor models based on the co-linearity equations.
Remote Sensing Image Segmentation by a Hybrid Algorithm
Korean Journal of Remote Sensing, volume 18, issue 2, 2002, Pages 107~116
A hybrid image segmentation algorithm is proposed which integrates edge-based and region-based techniques through the watershed algorithm. First, by using mean curvature diffusion coupled to min/max flow, noise is eliminated and thin edges are preserved. After images are segmented by watershed algorithm, the segmented regions are combined with neighbor regions. Region adjacency graph (RAG) is employed to analyze the relationship among the segmented regions. The graph nodes and edge costs in RAG correspond to segmented regions and dissimilarities between two adjacent regions respectively. After the most similar pair of regions is determined by searching minimum cost RAG edge, regions are merged and the RAG is updated. The proposed method efficiently reduces noise and provides one-pixel wide, closed contours.
Reconstruction and Change Analysis for Temporal Series of Remotely-sensed Data
Korean Journal of Remote Sensing, volume 18, issue 2, 2002, Pages 117~125
Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. Using an adaptive reconstruction system, dynamic compositing approach was developed to recover missing/bad observations. The reconstruction method incorporates temporal variation in physical properties of targets and anisotropic spatial optical properties into image processing. The adaptive system performs the dynamic compositing by obtaining a composite image as a weighted sum of the observed value and the value predicted according to local temporal trend. The proposed system was applied to the sequence of NDVI images of AVHRR observed on the Korean Peninsula from 1999 year to 2000 year. The experiment shows that the reconstructed series can be used as an estimated series with complete data for the observations including bad/missing values. Additionally, the gradient image, which represents the amount of temporal change at the corresponding time, was generated by the proposed system. It shows more clearly temporal variation than the data image series.