• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.1
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• pp.30-40
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• 2020

Monitoring the urban environmental changes caused by the urban regeneration project is necessary for evaluating the effect of the various types of urban regeneration projects that have been carried out in Seoul, South Korea. However, there is few available data and professional expert for evaluating the effect of these urban regeneration projects. This research evaluated the effect of the construction of rainwater village in Jangwi-dong area, constructed through the Seoul urban regeneration project, by utilizing the land surface temperatures derived from the multi-temporal Landsat-8 satellite images through the following steps. In the first step, the land surface temperature images were generated using the multispectral bands of the Landsat-8 satellite images. In the final step, the effect of constructing the rainwater villages was assessed by calculating the seasonal LST statistics for Jangwi-dong area, its neighbor area and entire Seoul area. The experimental results led the following conclusion: the construction of rainwater villages did not have the significant effect on the land surface temperature changes in Jangwi-dong area.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.975-988
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• 2020

Change detection is the process of identifying changes by observing the multi-temporal images at different times, and it is an important technique in remote sensing using satellite images. Among the change detection methods, the unsupervised change detection technique has the advantage of extracting rapidly the change area as a binary image. However, it is difficult to understand the changing pattern of land cover in binary images. This study used grid points generated from seamless digital map to evaluate the satellite image change detection results. The land cover change results were extracted using multi-temporal KOMPSAT-3A (K3A) data taken by Gimje Free Trade Zone and change detection algorithm used Spectral Angle Mapper (SAM). Change detection results were presented as binary images using the methods Otsu, Kittler, Kapur, and Tsai among the automated threshold selection algorithms. To consider the seasonal change of vegetation in the change detection process, we used the threshold of Differenced Normalized Difference Vegetation Index (dNDVI) through the probability density function. The experimental results showed the accuracy of the Otsu and Kapur was the highest at 58.16%, and the accuracy improved to 85.47% when the seasonal effects were removed through dNDVI. The algorithm generated based on this research is considered to be an effective method for accuracy assessment and identifying changes pattern when applied to unsupervised change detection.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1955-1969
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• 2022

Most of the public and private users who use national satellite information such as the KOMPSAT series mainly use Electro-Optical and Infrared (EO/IR) satellite images, and the utilization of Synthetic Aperture Radar (SAR) images is relatively insufficient. As KOMPSAT-5 currently in operation, KOMPSAT-6 and micro SAR satellite constellation systems are scheduled to be launched in the future, the demand for utilization of SAR satellite information is increasing in various fields. Accordingly, it is necessary to possess core technology for SAR utilization that can support the utilization of SAR satellite information for users. Due to the all-weather properties of SAR system, change detection technology is a key application technology. However, until now, the development of technology that automatic change detection and cueing using SAR images is insufficient. Through this study, the requirements of automatic change detection and cueing function using multi-temporal KOMPSAT-5 SAR satellite images were derived and a prototype program was developed. This prototype program aims to secure independent SAR utilization technology and promote the utilization of domestic SAR satellite information by practitioners in public sector organizations in Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.116-124
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• 2008

This study presents the application of multi-temporal and multi-scale panchromatic aerial photos for change detection in a small urban area. For aerial photos of the scale of 1:20,000 taken in 1987 and 1996 and the scale of 1:37,500 taken in 2000. Pre-processing that make the same conditions to all of the aerial photos was carried out through geometric correction, registration, contrasting, resamplimg, and mosaicking and then change detection were carried out respectively by image differencing and image ratio techniques. As a result, the change of urban features and landcover were able to be detected from panchromatic aerial photos that is single-band images and then the detected change results were compared between both techniques.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1215-1227
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• 2018

Sea ice motion is an important factor for assessing change of sea ice because the motion affects to not only regional distribution of sea ice but also new ice growth and thickness of ice. This study presents an application of multi-temporal high-resolution optical satellites images obtained from Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and Korea Multi-Purpose Satellite-3 (KOMPSAT-3) to measure sea ice motion using SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) feature tracking techniques. In order to use satellite images from two different sensors, spatial and radiometric resolution were adjusted during pre-processing steps, and then the feature tracking techniques were applied to the pre-processed images. The matched features extracted from the SIFT showed even distribution across whole image, however the matched features extracted from the SURF showed condensed distribution of features around boundary between ice and ocean, and this regionally biased distribution became more prominent in the matched features extracted from the ORB. The processing time of the feature tracking was decreased in order of SIFT, SURF and ORB techniques. Although number of the matched features from the ORB was decreased as 59.8% compared with the result from the SIFT, the processing time was decreased as 8.7% compared with the result from the SIFT, therefore the ORB technique is more suitable for fast measurement of sea ice motion.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1246-1254
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• 2004

This paper presents a image coding method that is well adaptive to close-range video transmission because of its low titrate and simple coding procedure. At first, it reduces temporal redundancies by performing image DPCM between previous frame and current frame, and makes wavelet transformed image of this difference image. Then, the coefficients are quantized selectively by using the coefficient values of base level and mid-frequency level because inter-level redundancies are widely exists in multi-resolution images. Finally quantized coefficients are made iron the function that implies the target bitrate, the average coefficient energy, and the value of the level. The proposed method shows the effective Performance in the experiments using the continuous motion images and transition images.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.64-67
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• 2004

Change detection using aerial and satellite images is one of the important research topics in photogrammetry and image interpretation. It is of particular importance especially in the fields of military, political and administrative affairs. When there is a need to detect changes in multi-temporal images, the most efficient methods for change detection and thresholds of change/no change area need to be chosen. Also, the images obtained from the various methods need to be analyzed. To do so, we need a system that can support our decision making process. Therefore, in this paper, we propose the Decision Support System for Change Detection. This system is composed of Data Base, Model Base and Graphic User Interface(GUI). Data base is a compilation of previous change detection results, and Model Base comprise of numerous operations. The data can be input and have the results of change detection analyzed by using GUI. In this paper, we will explain the entire operation of the system and demonstrate the level of its effectiveness.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.179-197
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• 2020

Topographic normalization reduces the terrain effects on reflectance by adjusting the brightness values of the image pixels to be equal if the pixels cover the same land-cover. Topographic effects are induced by the imaging conditions and tend to be large in high mountainousregions. Therefore, image analysis on mountainous terrain such as estimation of wildfire damage assessment requires appropriate topographic normalization techniques to yield accurate image processing results. However, most of the previous studies focused on the evaluation of topographic normalization on satellite images with moderate-low spatial resolution. Thus, the alleviation of topographic effects on multi-temporal high-resolution images was not dealt enough. In this study, the evaluation of terrain normalization was performed for each band to select the optimal technical combinations for rapid and accurate wildfire damage assessment using PlanetScope images. PlanetScope has considerable potential in the disaster management field as it satisfies the rapid image acquisition by providing the 3 m resolution daily image with global coverage. For comparison of topographic normalization techniques, seven widely used methods were employed on both pre-fire and post-fire images. The analysis on bi-temporal images suggests the optimal combination of techniques which can be applied on images with different land-cover composition. Then, the vegetation index was calculated from the images after the topographic normalization with the proposed method. The wildfire damage detection results were obtained by thresholding the index and showed improvementsin detection accuracy for both object-based and pixel-based image analysis. In addition, the burn severity map was constructed to verify the effects oftopographic correction on a continuous distribution of brightness values.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.681-692
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• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1645-1653
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• 2014

In general, RGB, NIR, and thermal images are used for obtaining geospatial data. Such multiband images are collected via devices mounted on satellites or manned flights, but do not always meet users' expectations, due to issues associated with temporal resolution, costs, spatial resolution, and effects of clouds. We believe high-resolution, multiband images can be obtained at desired time points and intervals, by developing a payload suitable for a low-altitude, auto-piloted UAV. To achieve this, this study first established a low-cost, high-resolution multiband image collection system through developing a sensor and a payload, and collected geo-referencing data, as well as RGB, NIR and thermal images by using the system. We were able to obtain a 0.181m horizontal deviation and 0.203m vertical deviation, after analyzing the positional accuracy of points based on ortho mosaic images using the collected RGB images. Since this meets the required level of spatial accuracy that allows production of maps at a scale of 1:1,000~5,000 and also remote sensing over small areas, we successfully validated that the payload was highly utilizable.