• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.118-121
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• 2014

Image interpolation is a method of determining the value of new pixel coordinate in the process of image scaling. Recently, image contents are likely to be a large-capacity, interpolation algorithm is required to generate fast enhanced result image. In this paper, fast multiple mixed image interpolation for image resolution enhancement is proposed. The proposed method estimates expected 12 shortfalls from four sub-images of a input image, and generates the result image that is interpolated in the combination of the expected shortfalls with the input image. The experimental results demonstrate that PSNR increases maximum value of 1.9dB, SSIM increases maximum value of 0.052, and the subjective quality is superior to any other compared methods. Moreover, it is known by algorithm running time comparison that the proposed method has been at least three times faster than the compared conventional methods. The proposed method can be useful for application on image resolution enhancement.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.733-738
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• 2006

As the film transistor-liquid crystal display (TFTLCD) panels become larger and provide higher resolution, the propagation delay of row and column lines, the voltage modulation of Vcom, and the response time of liquid crystal affect the display images now more than in the past. It is more important to understand the electrical characteristic of TFT-LCD panels these days. This paper describes the electrical model of a 15-inch XGA ($1024{\times}768$) TFT-LCD panel. The parasitic resistance and capacitance of its panel are obtained by 3D simulation of a sub pixel. The accuracy of these data is verified by the measured values in an actual panel [1]. The developed panel simulation platform, the equivalent circuit of a 15-inch XGA panel, is simulated by HSPICE. The results of simulation are compared with those of experiment, according to changing the width of signal. Especially, the proposed simulation platform for modeling TFTLCD panels can be applied to large size LCD TVs. It can help panel and circuit designers to verify their ideas without making actual panels and circuits.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.11 no.2
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• pp.41-47
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• 2007

A fine segmentation algorithm is proposed for extracting objects in an image, which have both weak boundaries and highly non-convex shapes. The image has simple background colors or simple object colors. Two concepts, geometric attraction-driven flow (GADF) and edge-regions are combined to detect boundaries of objects in a sub-pixel resolution. The main strategy to segment the boundaries is to construct initial curves close to objects by using edge-regions and then to make a curve evolution in GADF. Since the initial curves are close to objects regardless of shapes, highly non-convex shapes are easily detected and dependence on initial curves in boundary-based segmentation algorithms is naturally removed. Weak boundaries are also detected because the orientation of GADF is obtained regardless of the strength of boundaries. For a fine segmentation, we additionally propose a local region competition algorithm to detect perceptible boundaries which are used for the extraction of objects without visual loss of detailed shapes. We have successfully accomplished the fine segmentation of objects from images taken in the studio and aphids from images of soybean leaves.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.55-62
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• 2019

The stereo geometry establishment based on the precise sensor modeling is prerequisite for accurate stereo data processing. Ground control points are generally required for the accurate sensor modeling though it is not possible over the area where the accessibility is limited or reference data is not available. For the areas, the relative orientation should be carried out to improve the geometric consistency between the stereo data though it does not improve the absolute positional accuracy. The relative orientation requires conjugate points that are well distributed over the entire image region. Therefore the automatic conjugate point extraction is required because the manual operation is labor-intensive. In this study, we applied the method consisting of the key point extraction, the search space minimization based on the epipolar line, and the rigorous outlier detection based on the RPCs (Rational Polynomial Coefficients) bias compensation modeling. We tested different parameters of window sizes for Kompsat-2 across track stereo data and analyzed the RPCs precision after the bias compensation for the cases whether the epipolar line information is used or not. The experimental results showed that matching outliers were inevitable for the different matching parameterization but they were successfully detected and removed with the rigorous method for sub-pixel level of stereo RPCs precision.

• Advances in aircraft and spacecraft science
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• v.10 no.1
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• pp.67-82
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• 2023

Linear array imaging sensors are widely used in remote sensing satellites. The final products of an imaging sensor can only be used when they are geometrically, radiometrically, and spectrally calibrated. Therefore, at the first stages of sensor design, a detailed calibration procedure must be carefully planned based on the accuracy requirements. In this paper, focusing on inherent optical distortion, a step-by-step procedure for laboratory geometric calibration of a typical push-broom satellite imaging sensor is simulated. The basis of this work is the simulation of a laboratory procedure in which a linear imager mounted on a rotary table captures images of a pin-hole pattern at different angles. By these images and their corresponding pinhole approximation, the correction function is extracted and applied to the raw images to give the corrected ones. The simulation results illustrate that using this approach, the nonlinear effects of distortion can be minimized and therefore the accuracy of the geometric position of this method on the image screen can be improved to better than the order of sub-pixel. On the other hand, the analyses can be used to proper laboratory facility selection based on the imaging sensor specifications and the accuracy.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Journal of The Korean Astronomical Society
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• v.41 no.6
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• pp.157-161
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• 2008

We have estimated the fractal dimension of the molecular clouds associated with the Hii region Sh 156 in the Outer Galaxy. We selected the $^{12}CO$ cube data from the FCRAO CO Survey of the Outer Galaxy. Using a developed code within IRAF, we identified slice-clouds (2-dimensional clouds in velocity-channel maps) with two threshold temperatures to estimate the fractal dimension. With the threshold temperatures of 1.8 K, and 3 K, we identified 317 slice-clouds and 217 slice-clouds, respectively. There seems to be a turn-over location in fractional dimension slope around NP (area; number of pixel) = 40. The fractal dimensions was estimated to be D = $1.5\;{\sim}\;1.53$ for $NP\;{\geq}\;40$, where $P\;{\propto}\;A^{D/2}$ (P is perimeter and A is area), which is slightly larger than other results. The sampling rate (spatial resolution) of observed data must be an important parameter when estimating fractal dimension. Fractal dimension is apparently invariant when varying the threshold temperatures applied to slice-clouds identification.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.553-563
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• 2006

This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.5-16
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• 2007

The monitoring and investigation of underwater landslide help to understand its mechanism, increase the usefuless of design and construction and reduce the losses. This paper presents three high resolution geophysical techniques electrical resisitance, ultrasonic wave reflection imaging, and shear wave tomography conducted to determine the lab-scaled submerged landslide. Electrical resistance profiles of a soil mass obtained by an electrical resistance probe provide detailed information to assess the spatial distribution of the soil mass with milimetric resolution. An ultrasonic wave image obtained by recording the reflections from interfaces of different impedance materials permits detecting layers and landslide with submilimetric resolution. The pixel based image of immersed landslides is created by the inversion of the boundary information achieved from the traveling time of shear waves. The experimental results show that the ultrasonic wave imaging and the electrical resistance can provide complementary information; and their association with S-wave tomography image can produce a 3-D view of the underwater landslide. This study suggests that geophysical techniques may be effective tools for the detection of the underwater landslides and spatial distribution offshore.