• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.724-726
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• 2003

Currently available image fusion methods are not efficient for fusing the Landsat 7 images. Significant color distortion is one of the major problems. In this paper, using the well-known wavelet based method for data fusion between high-resolution panchromatic and low-resolution multispectral satellite images, we performed Landsat 7 image fusion. Based on the experimental results obtained from this study, we analyzed some reasons for color distortion. A new approach using the biorthogonal wavelets based method for data fusion is presented. This new method has reached an optimum fusion result - with the same spectral resolution as the multispectral image and the same spatial resolution as the panchromatic image with minimum artifacts.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.39-54
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• 2012

The objective of this study is to propose efficient data fusion techniques feasible to the KOMPSAT-2 satellite images. The most widely used image fusion techniques, which are the high-pass filter (HPF), the intensity-hue-saturation-based (modified IHS), the pan-sharpened, and the wavelet-based methods, was applied to four KOMPSAT - 2 satellite images having different regional and seasonal characteristics. Each fusion result was compared and analyzed in spatial and spectral features, respectively. Quality evaluation of image fusion techniques was performed in both quantitative and visual analysis. The quantitative analysis methods used for this study were the relative global dimensional error (spatial and spectral ERGAS), the spectral angle mapper index (SAM), and the image quality index (Q4). The results of quantitative and visual analysis indicate that the pan-sharpened method among the fusion methods used for this study relatively has the suitable balance between spectral and spatial information. In the case of the modified IHS method, the spatial information is well preserved, while the spectral information is distorted. And also the HPF and wavelet methods do not preserve the spectral information but the spatial information.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.1-13
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• 2017

This study investigates the geometric and spatial image quality analysis of KOMPSAT-3A stereo pair. KOMPSAT-3A is, the latest satellite of KOMPSAT family, a Korean earth observation satellite operating in optical bands. A KOMPSAT-3A stereo pair was taken on 23 November, 2015 with 0.55 m ground sampling distance over Terrassa area of Spain. The convergence angle of KOMPSAT-3A stereo pair was estimated as $58.68^{\circ}$. The investigation was assessed through the evaluation of the geopositioning analysis, image quality estimation and the accuracy of automatic Digital Surface Model (DSM) generation and compared with those of KOMPSAT-3 stereo pair with the convergence angle of $44.80^{\circ}$ over the same area. First, geopositioning accuracy was tested with initial rational polynomial coefficients (RPCs) and after compensating the biases of the initial RPCs by manually collected ground control points. Then, regarding image quality, relative edge response was estimated for manually selected points visible from two stereo pairs. Both of the initial and bias-compensated positioning accuracy and the quality assessment result expressed that KOMPSAT-3A images showed higher performance than those of KOMPSAT-3 images. Finally, the accuracy of DSMs generated from KOMPSAT-3A and KOMPSAT-3 stereo pairs were examined with respect to the reference LiDAR-derived DSM. The various DSMs were generated over the whole coverage of individual stereo pairs with different grid spacing and over three types of terrain; flat, mountainous and urban area. Root mean square errors of DSM from KOMPSAT-3A pair were larger than those for KOMPSAT-3. This seems due to larger convergence angle of the KOMPSAT-3A stereo pair.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• Proceedings of the KSR Conference
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• 2003.05a
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• pp.321-325
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• 2003

The technology for the multi-dimensional terrain perspective view can be used as an important factors in planning and designing for the various construction projects. In this study, the stereo image perspective view has been generated for the multi-dimension analysis by combining useful digital map and remotely sensed satellite images. In the course of experimenting with the multi-dimensional topography generated by the combination of the front-projected image by the precise GCP and DEM from the contour line, the technology has been developed to offer the multi-dimensional access to the potential construction sites from the nearby main roads. This stereo image bird's eye view has made it possible to make multi-dimensional analysis on the terrain, which provides real time virtual access to the designated construction sites and will be a versatile application for development planning and construction projects.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.135-147
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• 2003

This study used the multistage hierarchical clustering image classification to analyze the satellite images for the land-cover types of an area in the Korean peninsula. The multistage algorithm consists of two stages. The first stage performs region-growing segmentation by employing a hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous, and finally the whole image space is segmented into sub-regions where adjacent regions have different physical properties. Without spatial constraints for merging, the second stage clusters the segments resulting from the previous stage. The image classification of hierarchical clustering, which merges step-by step two small groups into one large one based on the hierarchical structure of digital imagery, generates a hierarchical tree of the relation between the classified regions. The experimental results show that the hierarchical tree has the detailed information on the hierarchical structure of land-use and more detailed spectral information is required for the correct analysis of land-cover types.

• 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.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.306-309
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• 2004

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the Geographic Information System (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat TM (Thermatic Mapper) satellite images; and the vegetation index value from SPOT HRV (High Resolution Visible) satellite images. Landslide hazardous areas were analysed and mapped using the landslide-occurrence factors employing the probability-frequency ratio method. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, land 'cover had relatively positive effects, and lithology had relatively negative effects on the landslide susceptibility maps in the study area. In addition, the landslide susceptibility maps using the all factors showed the relatively good results.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.727-731
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• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.205-211
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• 2008

The river management in Korea had been focused on line based 2D spatial data for the developing river management application system. In this study, the polygon based 3D spatial data such as aerial photos and satellite images were selected and used through comparing their resolution levels for the river environment management. In addition, 1m detailed DEM (Digital Elevation Model) was constructed to implement the real topography information around river so that the damage area scale could be extracted for flood disaster. Also, the social environment thematic maps such as a cadastral map or land cover map could be used to verify the real damage area scale by overlay analysis on aerial photos or satellite images. The construction of these spatial data makes possible to present the real surface information and extract quantitative analysis to support the scientific decision making for establishing the river management policy. For the further study, the lidar surveying data will be considered as the very useful data by offering the real height information of riverbed as the depth of river so that flood simulation can give more reality.