• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.4
• /
• pp.263-270
• /
• 2018

Land surface temperature is known to be an important factor in understanding the interactions of the ground-atmosphere. However, because of the large spatio-temporal variability, regular observation is rarely made. The existing land surface temperature is observed using satellite images, but due to the nature of satellite, it has the limit of long revisit period and low accuracy. In this study, in order to confirm the possibility of replacing land surface temperature observation using satellite imagery, images acquired by TIR (Thermal Infrared) sensor mounted on UAV (Unmanned Aerial Vehicle) are used. The acquired images were transformed from JPEG (Joint Photographic Experts Group) to TIFF (Tagged Image File Format) format and orthophoto was then generated. The DN (Digital Number) value of orthophoto was used to calculate the actual land surface temperature. In order to evaluate the accuracy of the calculated land surface temperature, the land surface temperature was compared with the land surface temperature directly observed with an infrared thermometer at the same time. When comparing the observed land surface temperatures in two ways, the accuracy of all the land covers was below the measure accuracy of the TIR sensor. Therefore, the possibility of replacing the satellite image, which is a conventional land surface temperature observation method, is confirmed by using the TIR sensor mounted on UAV.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1095-1106
• /
• 2019

Damage of forest fire should be investigated quickly and accurately for recovery, compensation and prevention of secondary disaster. Using remotely sensed data, burn severity is investigated based on the difference of reflectance or spectral indices before and after forest fire. Recently, the use of high resolution satellite and UAV imagery is increasing, but it is not easy to obtain an image before forest fire that cannot be predicted where and when. This study tried to analyze availability of high-resolution images and supervised classifiers on the burn severity classification. Two supervised classifiers were applied to the KOMPSAT-3A image and the UAV multispectral image acquired after the forest fire. The maximum likelihood (MLH) classifier use absolute value of spectral reflectance and the spectral angle mapper (SAM) classifier use pattern of spectra. As a result, in terms of spatial resolution, the classification accuracy of the UAV image was higher than that of the satellite image. However, both images shown very high classification accuracy, which means that they can be used for classification of burn severity. In terms of the classifier, the maximum likelihood method showed higher classification accuracy than the spectral angle mapper because some classes have similar spectral pattern although they have different absolute reflectance. Therefore, burn severity can be classified using the high resolution multispectral images after the fire, but an appropriate classifier should be selected to get high accuracy.

• Journal of Korean Society for Geospatial Information Science
• /
• v.22 no.3
• /
• pp.129-136
• /
• 2014

This study deals with red tide detection by using the remote sensing imagery from the Geostationary Ocean Color Imager (GOCI), the world's first geostationary orbit satellite, around the southern coast of Korea where the most severe red tide occurred recently. The red tide zone was determined by the available data selection from the GOCI imagery during the period of red tide occurrence and also the severe red tide zone was detected through the spatial analysis by temporal change out of the red tide zone. This study results showed that the coast in the vicinity of the Hansan and Yokji in Tongyeong-si was classified into the severe red tide zone, and that the red tide was likely to spread from the coast of Hansan and Yokji to the one of Sanyang-eub. In addition, the comparative analysis between the area of red tide occurrence, the prevention activities of Gyeongsangnam-do provincial government and the amount of the damage cost over time showed close correlation among them. It is still early to conclude that the study is showing the severe red tide zone and the spread path exactly due to various factors for red tide occurrence and activities. In order to improve the reliability of the results, the more data analysis is required.

• Korean Journal of Remote Sensing
• /
• v.21 no.2
• /
• pp.121-133
• /
• 2005

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of the useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program based on GLCM algorithm is newly implemented. As well, texture imaging modules for GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV Texture imaging parameters, it composed of six types of second order texture functions such as Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality in GLCM/GLDV, two direction modes such as Omni-mode and Circular mode newly implemented in this program are provided with basic eight-direction mode. Omni-mode is to compute all direction to avoid directionality complexity in the practical level, and circular direction is to compute texture parameters by circular direction surrounding a target pixel in a kernel. At the second phase of this study, some case studies with artificial image and actual satellite imagery are carried out to analyze texture images in different parameters and modes by correlation matrix analysis. It is concluded that selection of texture parameters and modes is the critical issues in an application based on texture image fusion.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.2
• /
• pp.51-58
• /
• 2018

Recently, various satellite sensors have been developed and it is becoming more convenient to acquire multitemporal satellite images. Therefore, various researches are being actively carried out in the field of utilizing change detection techniques such as disaster and land monitoring using multitemporal satellite images. In particular, researches related to the development of unsupervised change detection techniques capable of extracting rapidly change regions have been conducted. However, there is a disadvantage that false detection occurs due to a spectral difference such as a seasonal change. In order to overcome the disadvantages, this study aimed to reduce the false alarm detection due to seasonal effects using the direction vector generated by applying the $S^2CVA$ (Sequential Spectral Change Vector Analysis) technique, which is one of the unsupervised change detection methods. $S^2CVA$ technique was applied to RapidEye images of the same and different seasons. We analyzed whether the change direction vector of $S^2CVA$ can remove false positives due to seasonal effects. For the quantitative evaluation, the ROC (Receiver Operating Characteristic) curve and the AUC (Area Under Curve) value were calculated for the change detection results and it was confirmed that the change detection performance was improved compared with the change detection method using only the change magnitude vector.

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

• Journal of Korean Society for Geospatial Information Science
• /
• v.12 no.2 s.29
• /
• pp.61-66
• /
• 2004

By the advent of the high-resolution Satellite imagery, there are increasing needs in image mosaicking technology which can be applied to various application fields such as GIS(Geographic Information system). To mosaic images, various methods such as image matching and histogram modification are needed. In this study, automated image mosaicking is performed using image matching method based on the multi-resolution wavelet analysis(MWA). Specifically, both area based and feature based matching method are embedded in the multi-resolution wavelet analysis to construct seam line.; seam points are extracted then polygon clipping method are applied to define overlapped area of two adjoining images. Before mosaicking, radiometric correction is proceeded by using histogram matching method. As a result, mosaicking area is automatically extracted by using polygon clipping method. Also, seamless image is acquired using multi-resolution wavelet analysis.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.4
• /
• pp.1-11
• /
• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.

• Korean Journal of Remote Sensing
• /
• v.23 no.2
• /
• pp.103-111
• /
• 2007

The spectral information based image analysis, visual interpretation and automatic classification have been widely carried out so far for remote sensing data processing. Yet recently, many researchers have tried to extract the spatial information which cannot be expressed directly in the image itself. Using the texture and wavelet scheme, we made a wavelet-based texture fusion image which includes the advantages of each scheme. Moreover, using these schemes, we carried out image classification for the urban spatial analysis and the geological structure analysis around the caldera area. These two case studies showed that image classification accuracy of texture image and wavelet-based texture fusion image is better than that of using only raw image. In case of the urban area using high resolution image, as both texture and wavelet based texture fusion image are added to the original image, the classification accuracy is the highest. Because detailed spatial information is applied to the urban area where detail pixel variation is very significant. In case of the geological structure analysis using middle and low resolution image, the images added by only texture image showed the highest classification accuracy. It is interpreted to be necessary to simplify the information such as elevation variation, thermal distribution, on the occasion of analyzing the relatively larger geological structure like a caldera. Therefore, in the image analysis using spatial information, each spatial information analysis method should be carefully selected by considering the characteristics of the satellite images and the purpose of study.

• Journal of the Korean Geosynthetics Society
• /
• v.19 no.3
• /
• pp.1-8
• /
• 2020

In this study, numerical analysis of debris flow was performed using the SPH (Smoothed Particle Hydrodynamics) technique to analyze the mechanism of debris flow, and the applicability of soil parameters was verified by comparison with previous studies. In addition, after performing aerial photographic survey using a drone, a topographic model was created based on this survey to check the applicability of the site to the valley part of Jagul Mountain basin. And after numerical analysis of debris flow was performed using NFLOW, and the result was compared and analyzed with the existing satellite image based method. As a result of this study, the numerical analysis method using drone image and NFLOW was found to have a higher applicability to predicting the impact of debris flow, because it can reflect the actual topography better than the existing method based on satellite imagery. Therefore, it is considered that this study can be used as basic data to establish the preventive measures for debris flow such as location selection of the eruption control dam.