Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Korean Journal of Remote Sensing
Journal Basic Information
Journal DOI :
The Korean Society of Remote Sensing
Editor in Chief :
Volume & Issues
Volume 26, Issue 6 - Dec 2010
Volume 26, Issue 5 - Oct 2010
Volume 26, Issue 4 - Aug 2010
Volume 26, Issue 3 - Jun 2010
Volume 26, Issue 2 - Apr 2010
Volume 26, Issue 1 - Feb 2010
Selecting the target year
Change Detection of the Tonle Sap Floodplain, Cambodia, using ALOS PALSAR Data
Trung, Nguyen Van ; Choi, Jung-Hyun ; Won, Joong-Sun ;
Korean Journal of Remote Sensing, volume 26, issue 3, 2010, Pages 287~295
Water level of the Tonle Sap is largely influenced by the Mekong River. During the wet season, the lacustrine landform and vegetated areas are covered with water. Change detection in this area provides information required for human activities and sustainable development around the Tonle Sap. In order to detect the changes in the Tonle Sap floodplain, fifteen ALOS-PALSAR L-band data acquired from January 2007 to January 2009 and examined in this study. Since L-band is able to penetrate into vegetation cover, it enables us to study the changes according to water level of floodplain developed in the rainforest. Four types of images were constructed and studied include 1) ratio images, 2) correlation coefficient images, 3) texture feature ratio images and 4) multi-color composite images. Change images (in each 46 day interval) extracted from the ratio images, coherence images and texture feature ratio images were formed for detecting land cover change. Two RGB images are also obtained by compositing three images acquired in the early, in the middle and at the end of the rainy season in 2007 and 2008. Combination of the methods results that the change images present the relationship between vegetation and water level, leaf fall forest as well as cultivation and harvest crop.
Velocity Estimation of Moving Targets on the Sea Surface by Azimuth Differentials of Simulated-SAR Image
Yang, Chang-Su ; Kim, Youn-Seop ; Ouchi, Kazuo ;
Korean Journal of Remote Sensing, volume 26, issue 3, 2010, Pages 297~304
Since the change in Doppler centroid according to moving targets brings alteration to the phase in azimuth differential signals of synthetic aperture radar (SAR) data, one can measure the velocity of the moving targets using this effect. In this study, we will investigate theoretically measuring the velocity of an object from azimuth differential signals by using range compressed data which is the interim outcome of treatment from the simulated SAR raw data of moving targets on the background of sea clutter. Also, it will provide evaluation for the elements that affect the estimation error of velocity from a single SAR sensor. By making RADARSAT-1 simulated image as a specific case, the research includes comparisons for the means of velocity measurement classified by the directions of movement in the four following cases. 1. A case of a single target without currents, 2. A case of a single target with tidal currents of 0.5 m/s, 1 m/s, and 3 m/s, 3. A case of two targets on a same azimuth line moving in a same direction and velocity, 4. A case of a single target contiguous to land where radar backscatter is strong. As a result, when two moving targets exist in SAR image outside the range of approximately 256 pixels, the velocity of the object can be measured with high accuracy. However, when other moving targets exist in the range of approximately 128 pixels or when the target was contiguous to the land of strong backscatter coefficient (NRCS: normalized radar cross section), the estimated velocity was in error by 10% at the maximum. This is because in the process of assuming the target's location, an error occurs due to the differential signals affected by other scatterers.
Comparison of Fusion Methods for Generating 250m MODIS Image
Kim, Sun-Hwa ; Kang, Sung-Jin ; Lee, Kyu-Sung ;
Korean Journal of Remote Sensing, volume 26, issue 3, 2010, Pages 305~316
The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor has 36 bands at 250m, 500m, 1km spatial resolution. However, 500m or 1km MODIS data exhibits a few limitations when low resolution data is applied at small areas that possess complex land cover types. In this study, we produce seven 250m spectral bands by fusing two MODIS 250m bands into five 500m bands. In order to recommend the best fusion method by which one acquires MODIS data, we compare seven fusion methods including the Brovey transform, principle components algorithm (PCA) fusion method, the Gram-Schmidt fusion method, the least mean and variance matching method, the least square fusion method, the discrete wavelet fusion method, and the wavelet-PCA fusion method. Results of the above fusion methods are compared using various evaluation indicators such as correlation, relative difference of mean, relative variation, deviation index, peak signal-to-noise ratio index and universal image quality index, as well as visual interpretation method. Among various fusion methods, the local mean and variance matching method provides the best fusion result for the visual interpretation and the evaluation indicators. The fusion algorithm of 250m MODIS data may be used to effectively improve the accuracy of various MODIS land products.
Automated Training from Landsat Image for Classification of SPOT-5 and QuickBird Images
Kim, Yong-Min ; Kim, Yong-Il ; Park, Wan-Yong ; Eo, Yang-Dam ;
Korean Journal of Remote Sensing, volume 26, issue 3, 2010, Pages 317~324
In recent years, many automatic classification approaches have been employed. An automatic classification method can be effective, time-saving and can produce objective results due to the exclusion of operator intervention. This paper proposes a classification method based on automated training for high resolution multispectral images using ancillary data. Generally, it is problematic to automatically classify high resolution images using ancillary data, because of the scale difference between the high resolution image and the ancillary data. In order to overcome this problem, the proposed method utilizes the classification results of a Landsat image as a medium for automatic classification. For the classification of a Landsat image, a maximum likelihood classification is applied to the image, and the attributes of ancillary data are entered as the training data. In the case of a high resolution image, a K-means clustering algorithm, an unsupervised classification, was conducted and the result was compared to the classification results of the Landsat image. Subsequently, the training data of the high resolution image was automatically extracted using regular rules based on a RELATIONAL matrix that shows the relation between the two results. Finally, a high resolution image was classified and updated using the extracted training data. The proposed method was applied to QuickBird and SPOT-5 images of non-accessible areas. The result showed good performance in accuracy assessments. Therefore, we expect that the method can be effectively used to automatically construct thematic maps for non-accessible areas and update areas that do not have any attributes in geographic information system.
The Comparison of the SIFT Image Descriptor by Contrast Enhancement Algorithms with Various Types of High-resolution Satellite Imagery
Choi, Jaw-Wan ; Kim, Dae-Sung ; Kim, Yong-Min ; Han, Dong-Yeob ; Kim, Yong-Il ;
Korean Journal of Remote Sensing, volume 26, issue 3, 2010, Pages 325~333
Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.
Development of an Efficient Processor for SIRAL SARIn Mode
Lee, Dong-Taek ; Jung, Hyung-Sup ; Yoon, Geun-Won ;
Korean Journal of Remote Sensing, volume 26, issue 3, 2010, Pages 335~346
Recently, ESA (European Space Agency) has launched CryoSAT-2 for polar ice observations. CryoSAT-2 is equipped with a SIRAL (SAR/interferometric radar altimeter), which is a high spatial resolution radar altimeter. Conventional altimeters cannot measure a precise three-dimensional ground position because of the large footprint diameter, while SIRAL altimeter system accomplishes a precise three-dimensional ground positioning by means of interferometric synthetic aperture radar technique. In this study, we developed an efficient SIRAL SARIn mode processing technique to measure a precise three-dimensional ground position. We first simulated SIRAL SARIn RAW data for the ideal target by assuming the flat Earth and linear flight track, and second accessed the precision of three-dimensional geopositioning achieved by the proposed algorithm. The proposed algorithm consists of 1) azimuth processing that determines the squint angle from Doppler centroid, and 2) range processing that estimates the look angle from interferometric phase. In the ideal case, the precisions of look and squint angles achieved by the proposed algorithm were about -2.0
, respectively, and the three-dimensional geopositioning accuracy was about 1.23 m, -0.02 m, and -0.30 m in X, Y and Z directions, respectively. This means that the SIRAL SARIn mode processing technique enables to measure the three-dimensional ground position with the precision of several meters.
A New True Ortho-photo Generation Algorithm for High Resolution Satellite Imagery
Bang, Ki-In ; Kim, Chang-Jae ;
Korean Journal of Remote Sensing, volume 26, issue 3, 2010, Pages 347~359
Ortho-photos provide valuable spatial and spectral information for various Geographic Information System (GIS) and mapping applications. The absence of relief displacement and the uniform scale in ortho-photos enable interested users to measure distances, compute areas, derive geographic locations, and quantify changes. Differential rectification has traditionally been used for ortho-photo generation. However, differential rectification produces serious problems (in the form of ghost images) when dealing with large scale imagery over urban areas. To avoid these artifacts, true ortho-photo generation techniques have been devised to remove ghost images through visibility analysis and occlusion detection. So far, the Z-buffer method has been one of the most popular methods for true ortho-photo generation. However, it is quite sensitive to the relationship between the cell size of the Digital Surface Model (DSM) and the Ground Sampling Distance (GSD) of the imaging sensor. Another critical issue of true ortho-photo generation using high resolution satellite imagery is the scan line search. In other words, the perspective center corresponding to each ground point should be identified since we are dealing with a line camera. This paper introduces alternative methodology for true ortho-photo generation that circumvents the drawbacks of the Z-buffer technique and the existing scan line search methods. The experiments using real data are carried out while comparing the performance of the proposed and the existing methods through qualitative and quantitative evaluations and computational efficiency. The experimental analysis proved that the proposed method provided the best success ratio of the occlusion detection and had reasonable processing time compared to all other true ortho-photo generation methods tested in this paper.
Open Source Remote Sensing of ORFEO Toolbox and Its Connection to Database of PostGIS with NIX File Importing
Lee, Ki-Won ; Kang, Sang-Goo ;
Korean Journal of Remote Sensing, volume 26, issue 3, 2010, Pages 361~371
In recent, interests regarding open source software for geo-spatial processing are increasing. Open source remote sensing (OSRS) is regarded as one of the progressing and advanced fields in remote sensing. Nevertheless, analyses or application cases regarding OSRS are not enough for general uses or references. In this study, three kinds of OSRS software in consideration of international popularity, types of functionalities, and development environments are taken into account: OSSIM, Opticks, and ORFEO Toolbox (OTB). First, functional comparison with respect to these is carried out on the level of the preliminary survey. According to this investigation, OTB is chosen as the most applicable OSRS software in this study. Running on OTB, NIX format importing module and database connecting module are implemented for widely general uses and further application. As for an example case, airborne image of NIX format is used to region growing segmentation algorithm in OTB, and then the results are stored and retrieved in PostGIS database to test implemented modules. Conclusively, local customization and algorithm development using OSRS software are necessary to build on-demand applications from the developers' viewpoint.