• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.777-786
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• 2018

GF (Guided Filtering) is a representative image processing technique to effectively remove noise while preserving edge information in the digital image. In this paper, we proposed a unsupervised change detection method for the KOMPSAT-3 satellite image using the GF and evaluated its performance. In order to utilize GF for the unsupervised change detection, cross-sharpened images were generated based on GF, and CVA (Change Vector Analysis) was applied to the generated cross-sharpened images to extract the changed area in the multitemporal satellite imagery. Experimental results using KOMPSAT-3 satellite images showed that the proposed method can be effectively used to detect changed regions compared with CVA results based on existing cross-sharpened images.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.6
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• pp.1-12
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• 2006

Remote sensing cannot provide a direct measurement of vegetation index (VI) but it can provide a reasonably good estimate of vegetation index, defined as the ratio of satellite bands. The monitoring of vegetation in nearby urban regions is made difficult by the low spatial resolution and temporal resolution image captures. In this study, enhancing spatial resolution method is adapted as to improve a low spatial resolution. Recent studies have successfully estimated normalized difference vegetation index (NDVI) using improved resolution method such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra satellite. Image enhancing spatial resolution is an important tool in remote sensing, as many Earth observation satellites provide both high-resolution and low-resolution multi-spectral images. Examples of enhancement of a MODIS multi-spectral image and a MODIS NDVI image of Cheongju using a Landsat TM high-resolution multi-spectral image are presented. The results are compared with that of the IHS technique is presented for enhancing spatial resolution of multi-spectral bands using a higher resolution data set. To provide a continuous monitoring capability for NDVI, in situ measurements of NDVI from paddy field was carried out in 2004 for comparison with remotely sensed MODIS data. We compare and discuss NDVI estimates from MODIS sensors and in-situ spectroradiometer data over Ochang plain region. These results indicate that the MODIS NDVI is underestimated by approximately 50%.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.573-586
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• 2011

The MTF(modulation transfer function) is one of parameters to evaluate the performance of imaging systems. Also, it can be used to restore information that is lost by a harsh space environment (radioactivity, extreme cold/heat condition and electromagnetic field etc.), atmospheric effects and falloff of system performance etc. This paper evaluated the MTF values of images taken by DubaiSat-1 satellite which was launched in 2009 by EIAST(Emirates Institute for Advanced Science and Technology) and Satrec Initiative. Generally, the MTF was assessed using various methods such as a point source method and a knife-edge method. This paper used the slanted-edge method. The slantededge method is the ISO 12233 standard for the MTF measurement of electronic still-picture cameras. The method is adapted to estimate the MTF values of line-scanning telescopes. After assessing the MTF, we performed the MTF compensation by generating a MTF convolution kernel based on the PSF(point spread function) with image denoising to enhance the image quality.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.37-46
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• 2017

Sinabung volcano in Indonesia was formed due to the subduction between the Eurasian and Indo-Australian plates along the Pacific Ring of Fire. After being dormant for about 400 years, Sinabung volcano erupted on the 29th of August, 2010 and most recently on the 1st of November, 2016. We measured the deformation of Sinabung volcano using Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar(ALOS/PALSAR) interferometric synthetic aperture radar(InSAR) images acquired from February 2007 to January 2011. Based on multi-temporal InSAR processing, we mapped the ground surface deformation before, during, and after the 2010 eruption with time-series InSAR technique. During the 3 years before the 2010 eruption, the volcano inflated at an average rate of ~1.7 cm/yr with a markedly higher rate of 6.6 cm/yr during the 6 months prior to the 2010 eruption. The inflation was constrained to the top of the volcano. From the 2010 eruption to January 2011,the volcano subsided by approximately 3 cm (~6 cm/yr). We interpreted that the inflation was due to magma accumulation in a shallow reservoir beneath Sinabung. The deflation was attributed to magma withdrawal from the shallow reservoir during the eruption as well as thermo-elastic compaction of erupted material. This result demonstrates once again the utility of InSAR for volcano monitoring.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.79-89
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• 2004

Remote sensing technique is a probable means to estimate distribution of actual evapotranspiration in connection with regional characteristics of vegetation and landuse. The factors controlling evapotranspiration from ground surface are air temperature, humidity, wind, radiation, soil moisture and so on. Not only the vegetation influences directly the evapotranspiration, but also these factors strongly influences the vegetation growth at the area. Therefore, it can be expected that evapotranspiration is highly correlated to vegetation condition. The normalized difference vegetation index (NDVI) showed excellent ability to get the vegetation information. The NDVI is obtained using NOAA/AVHRR have been studied as a tool for vegetation monitoring. In this paper, a simple method to estimate actual avapotranspiration is proposed based on vegetation and meteorological data.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.169-172
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have been tried to enhance classification accuracy. Previous studies show that the classification technique based on wavelet transform is more effective than that of traditional techniques based on original pixel values, especially in complicated imagery. Various wavelets can be used in wavelet transform. Wavelets are used as basis functions in representing other functions, like sinusoidal function in Fourier analysis. In these days, some basis functions such as Haar, Daubechies, Coiflets and Symlets are mainly used in 2D image processing. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we computed the wavelet coefficients of satellite image using 10 different basis functions, and then classified test image. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis function. The energy parameter of signal is the sum of the squares of wavelet coefficients. The energy parameter is calculated by sub-bands after the wavelet decomposition and the energy parameter of each sub-band can be a favorable feature of texture. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.133-141
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• 2009

This study was conducted to support for determining an efficient sampling design for forest resources assessments in South Korea with respect to statistical efficiency. For this objective, different systematic sampling designs were simulated and compared based on an artificial forest population that had been built from field sample data and satellite data in Yang-Pyeong County, Korea. Using the k-NN technique, two thematic maps (growing stock and forest cover type per pixel unit) across the test area were generated; field data (n=191) and Landsat ETM+ were used as source data. Four sampling designs (systematic sampling, systematic sampling for post-stratification, systematic cluster sampling, and stratified systematic sampling) were employed as optimum sampling design candidates. In order to compute error variance, the Monte Carlo simulation was used (k=1,000). Then, sampling error and relative efficiency were compared. When the objective of an inventory was to obtain estimations for the entire population, systematic cluster sampling was superior to the other sampling designs. If its objective is to obtain estimations for each sub-population, post-stratification gave a better estimation. In order to successfully perform this procedure, it requires clear definitions of strata of interest per field observation unit for efficient stratification.

• Journal of the Korean Association of Geographic Information Studies
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• v.1 no.1
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• pp.8-17
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• 1998

This paper presents a method of construction and re-use of a GCP database for precision geometric correction of high resolution satellite images. Accurate geometric correction can be achieved by using accurate GCPs. The GCP information which is extracted from maps or other sources is saved in a database in conjunction with the corresponding image chips. The usage of the GCPs from the database gives reusability and efficiency in marking new GCPs. An image matching algorithm was developed to determine the corresponding positions between an image chip and a new image. The proposed technique can save time in the regular operation of satellite image preprocessing by propagating the pre-determined GCPs to the new image correction.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.4
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• pp.412-419
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• 2010

Concurrent Multi Path-Differential Optical Absorption Spectroscopy (CMP-DOAS) is a novel active optical system to measure simultaneously ambient trace gases (such as $NO_2$, $SO_2$, $O_3$, and HCHO) present on several light paths. The CMP-DOAS system consists of a 2D CCD camera, spectrometer, receiving telescopes, and artificial light sources. The system receives spectra, which have been transported through several paths. It also covers wavelength ranges of which trace gases of interest share at the same time. This study presents the instrumental setup of a CMP-DOAS in detail. A field campaign for a comparative measurement was carried out at an urban site in Gwangju for a month on January 2009. $NO_2$ mixing ratios measured by the CMP-DOAS system and in-situ $NO_2$ analyzers were in good agreement by 83%. It demonstrates the high capacities of the CMP-DOAS technique to cover atmospheric trace gases dispersed across wide light paths.