• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.141-153
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• 2016

Reliable assessment of Land Use and Land Cover (LULC) changes greatly improves many practical issues in hydrography, socio-geographical research such as the observation of erosion and accretion, coastal monitoring, ecological effects evaluation. Remote sensing imageries can offer the outstanding capability to monitor nature and extent of land and associated changes over time. Nowadays accurate analysis using remote sensing imageries with high spatio-temporal resolution is required for environmental monitoring. This study develops a methodology of mapping and change detection in LULC by using classified Korea Multi-Purpose Satellite-2 (KOMPSAT-2) multispectral imageries at Jeonbuk and Jeonnam provinces including protected tidal flats located in the west coasts of Korean peninsula from 2008 to 2015. The LULC maps generated from unsupervised classification were analyzed and evaluated by post-classification change detection methods. The LULC assessment in Jeonbuk and Jeonnam areas had not showed significant changes over time although developed area was gradually increased only by 1.97% and 4.34% at both areas respectively. Overall, the results of this study quantify the land cover change patterns through pixel based analysis which demonstrate the potential of multispectral KOMPSAT-2 images to provide effective and economical LULC maps in the coastal zone over time. This LULC information would be of great interest to the environmental and policy mangers for the better coastal management and political decisions.

• Composites Research
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• v.35 no.6
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• pp.378-393
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• 2022

As an emerging power generation technology, triboelectric nanogenerators (TENGs) have received increasing attention due to their boundless promise in energy harvesting and self-powered sensing applications. The recent rise of soft robotics has sparked widespread enthusiasm for developing flexible and soft sensors and actuators. TENGs have been regarded as promising power sources for driving actuators and self-powered sensors, providing a unique approach for the development of soft robots with soft sensors and actuators. In this review, TENG-based soft robots with different morphologies and different functions are introduced. Among them, the design of biomimetic soft robots that imitate the structure, surface morphology, material properties, and sensing/generating mechanisms of nature has greatly benefited in improving the performance of TENGs. In addition, various bionic soft robots have been well improved compared to previous driving methods due to the simple structure, self-powering characteristics, and tunable output of TENGs. Furthermore, we provide a comprehensive review of various studies within specific areas of TENG-enabled soft robotics applications. We first explore various recently developed TENG-based soft robots and a comparative analysis of various device structures, surface morphologies, and nature-inspired materials, and the resulting improvements in TENG performance. Various ubiquitous sensing principles and generation mechanisms used in nature and their analogous artificial TENG designs are demonstrated. Finally, biomimetic applications of TENG enabled in tactile displays as well as in wearable devices, artificial electronic skin and other devices are discussed. System designs, challenges and prospects of TENGs-based sensing and actuation devices in the practical application of soft robotics are analyzed.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1003-1017
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• 2017

Multispectral image data of high spatial resolution is required to obtain correct information on the ground surface. The multispectral image data has lower resolution compared to panchromatic data. PAN-sharpening fusion technique produces the multispectral data with higher resolution of panchromatic image. Recently the object-based approach is more applied to the high spatial resolution data than the conventional pixel-based one. For the object-based image analysis, it is necessary to perform image segmentation that produces the objects of pixel group. Image segmentation can be effectively achieved by the process merging step-by-step two neighboring regions in RAG (Regional Adjacency Graph). In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. This degradation increases variation of pixel values in same area, and results in deteriorating the accuracy of image segmentation. An iterative approach that reduces the difference of pixel values in two neighboring pixels of same area is employed to alleviate variation of pixel values in same area. The size of segmented regions is associated with the quality of image segmentation and is decided by a stopping rue in the merging process. In this study, the image restoration and segmentation was quantitatively evaluated using simulation data and was also applied to the three PAN-sharpened multispectral images of high resolution: Dubaisat-2 data of 1m panchromatic resolution from LA, USA and KOMPSAT3 data of 0.7m panchromatic resolution from Daejeon and Chungcheongnam-do in the Korean peninsula. The experimental results imply that the proposed method can improve analytical accuracy in the application of remote sensing high resolution PAN-sharpened multispectral imagery.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.33-41
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• 2011

In the information communication technology, it is world-widely apparent that trend movement from internet web to smartphone app by users demand and developers environment. So it needs kinds of appropriate technological responses from geo-spatial domain regarding this trend. However, most cases in the smartphone app are the map service and location recognition service, and uses of geo-spatial contents are somewhat on the limited level or on the prototype developing stage. In this study, app for extraction of corner point features using geo-spatial imagery and their linkage to database system are developed. Corner extraction is based on Harris algorithm, and all processing modules in database server, application server, and client interface composing app are designed and implemented based on open source. Extracted corner points are applied LOD(Level of Details) process to optimize on display panel. Additional useful function is provided that geo-spatial imagery can be superimposed with the digital map in the same area. It is expected that this app can be utilized to automatic establishment of POI (Point of Interests) or point-based land change detection purposes.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.551-566
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• 2016

In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. The degradation results in noise and blurring which badly affect identification and extraction of useful information in image data. Especially, the degradation gives bad influence in the analysis of images collected over the scene with complicate surface structure such as urban area. This study proposes a multi-stage image restoration to improve the accuracy of detailed analysis for the images collected over the complicate scene. The proposed method assumes a Gaussian additive noise, Markov random field of spatial continuity, and blurring proportional to the distance between the pixels. Point-Jacobian Iteration Maximum A Posteriori (PJI-MAP) estimation is employed to restore a degraded image. The multi-stage process includes the image segmentation performing region merging after pixel-linking. A dissimilarity coefficient combining homogeneity and contrast is proposed for image segmentation. In this study, the proposed method was quantitatively evaluated using simulation data and was also applied to the two panchromatic images of super-high resolution: Dubaisat-2 data of 1m resolution from LA, USA and KOMPSAT3 data of 0.7 m resolution from Daejeon in the Korean peninsula. The experimental results imply that it can improve analytical accuracy in the application of remote sensing high resolution panchromatic imagery.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.435-444
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• 2009

In the case of oil spill accident at sea, information concerning the movement of spilled oil is important in making response strategies. Aircrafts and the satellites have been utilized for monitoring of spilled oil. In these days, numerical models are using to predict the movement of the spilled oil. In the future a coupling method of modeling and remote sensing data should be needed to predict more correctly the spilled oil. The purpose of this paper is to present an application of satellite image data to an oil spill prediction model as an initial condition. Environmental Fluid Dynamics Computer Code (EFDC) was used to predict the movement of the oil spilled from Hebei Spirit incident occurred in Taean coastal area on December 7,2007. In order to make the model initial condition and to compare the model results, two satellite images, KOMPSAT-2 MSC and ENVISAT ASAR obtained on December 8 and 11, were used during the period of the oil spill incident. The model results showed an improvement for the prediction of the spilled oil by using the initial condition deduced from satellite image data than the initial condition specified at the oil spill incident site in the respects of the distributed spilled area.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.504-514
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• 2009

Recently, fire monitoring application systems have been an active research area due to the safety of industries, historical monuments and so on. The fire monitoring application systems can reduce the damage of properties by providing earlier warning for possible fire situation. However, the existing systems have a drawback that they detect fire with delay due to their uniform epoch in fire detection algorithm. Moreover, they do not provide user-friendly graphical user interfaces in their fire monitoring systems. To resolve the problems, First, we propose a new fire detection algorithm (Early Fire Detection Fire Algorithm) which uses the distribution of sensing data for early fire detection. Our fire detection algorithm is better in terms of fire detection time than the existing work because it can set a start time of fire detection epoch dynamically based on data distribution. Second, we develop a fire monitoring application system which provides users with both a user-friendly graphical user interface and a fire alarm message when fire occurs. Finally, we show from our experiment that our developed system is effectively used for early fire warning in a variety of fire situations.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1061-1073
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• 2017

In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1901-1910
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• 2022

Arriving in the new space age, securing technology for fusion application of KOMPSAT-3·3A and global satellite images is becoming more important. In general, multi-sensor satellite images have relative geometric errors due to various external factors at the time of acquisition, degrading the quality of the satellite image outputs. Therefore, we propose a fine-image registration methodology to minimize the relative geometric error between KOMPSAT-3·3A and global satellite images. After selecting the overlapping area between the KOMPSAT-3·3A and foreign satellite images, the spatial resolution between the two images is unified. Subsequently, tie-points are extracted using a hybrid matching method in which feature- and area-based matching methods are combined. Then, fine-image registration is performed through iterative registration based on pyramid images. To evaluate the performance and accuracy of the proposed method, we used KOMPSAT-3·3A, Sentinel-2A, and PlanetScope satellite images acquired over Daejeon city, South Korea. As a result, the average RMSE of the accuracy of the proposed method was derived as 1.2 and 3.59 pixels in Sentinel-2A and PlanetScope images, respectively. Consequently, it is considered that fine-image registration between multi-sensor satellite images can be effectively performed using the proposed method.