• Journal of Korea Technology Innovation Society
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• v.9 no.1
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• pp.52-83
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• 2006

This study is to analyze the differences of consumer preferences between the terrestrial DMB and satellite DMB in various segment groups by using survey data. We categorized the consumers by the DMB preference patterns into four groups, i.e., non users group, terrestrial only users group, satellite only users group, dual users group. And then we analyzed the differences among these four segment groups in demographic characteristics, behavior patterns on telecommunication and broadcasting services, life-style, attitudes to DMB services.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.241-244
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• 2005

Many building detection methods mainly rely on line segments extracted from aerial or satellite imagery. Building detection methods based on line segments, however, are difficult to succeed in high resolution satellite imagery such as IKONOS imagery, for most buildings in IKONOS imagery have small size of roofs with low contrast between roof and background. In this paper, we propose an efficient method to extract line segments and group them at the same time. First, edge preserving filtering is applied to the imagery to remove the noise. Second, we segment the imagery by watershed method, which collects the pixels with similar intensities to obtain homogeneous region. The boundaries of homogeneous region are not completely coincident with roof boundaries due to low contrast in the vicinity of the roof boundaries. Finally, to resolve this problem, we set up snake model with segmented region boundaries as initial snake's positions. We used a greedy algorithm to fit a snake to roof boundary. Experimental results show our method can obtain more .correct roof boundary with small size and low contrast from IKONOS imagery. Snake algorithm, building roof detection, watershed segmentation, edge-preserving filtering

• Journal of IKEEE
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• v.27 no.4
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• pp.456-463
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• 2023

In this paper, we introduce the SSResUNet network model, which integrates the SPADE structure with the U-Net network model for accurate land cover classification using high-resolution satellite imagery without requiring user intervention. The proposed network possesses the advantage of preserving the spatial characteristics inherent in satellite imagery, rendering it a robust classification model even in intricate environments. Experimental results, obtained through training on KOMPSAT-3A satellite images, exhibit superior performance compared to conventional U-Net and U-Net++ models, showcasing an average Intersection over Union (IoU) of 76.10 and a Dice coefficient of 86.22.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.158-161
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• 2004

KOMPSAT-2, to be launched in 2005, will be a long awaited addition to the existing high-resolution satellite sensors. The use of download facilities in Europe will greatly increase its capacity without loosing any coverage over Korea. In this paper the concept for an Urban Application Center is presented. It is part of the proposed Regional Application Center which is dedicated to archiving and distributing KOMPSAT-2 images. The Urban Application Center will offer services derived from KOMPSAT-2. Its aim is to promote the use of KOMPSAT-2 data and increase the general awareness and acceptance of satellite data.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.95-100
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• 2002

Detecting simple shaped buildings from 1m-resolution satellite imagery is presented. The proposed algorithm is that first, image features such as edges are detected and then segmentation process is performed with the detected features. It can be result in line primitives. These primitives are linked and grouped by building hypotheses. Proposed building hypotheses restrict a building to simple rectangular shape. And sub-region homogeneity test is performed for finding rooftops of buildings. The proposed algorithm has been tested on IKONOS satellite image with 1m-resolution.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.202-210
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• 2019

The obstacles modeling is a fundamental and significant issue for path planning and automatic navigation of Unmanned Surface Vehicle (USV). In this study, we propose a novel obstacles modeling method based on high resolution satellite images. It involves two main steps: extraction of obstacle features and construction of convex hulls. To extract the obstacle features, a series of operations such as sea-land segmentation, obstacles details enhancement, and morphological transformations are applied. Furthermore, an efficient algorithm is proposed to mask the obstacles into convex hulls, which mainly includes the cluster analysis of obstacles area and the determination rules of edge points. Experimental results demonstrate that the models achieved by the proposed method and the manual have high similarity. As an application, the model is used to find the optimal path for USV. The study shows that the obstacles modeling method is feasible, and it can be applied to USV path planning.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1943-1949
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• 2021

In recent years, as the demand of high-resolution satellite images increases due to the miniaturization and constellation of satellites, various efforts to support users to utilize satellite images more conveniently are performed. Accordingly, the Korea Aerospace Research Institute produces and provides mosaic images on the Korean Peninsula every year to improve the convenience of users in the public sector and activate the use of satellite images. In order to increase the utilization of mosaic images on the Korean Peninsula, a study on satellite image segmentation and classification using mosaic images was attempted. However, since mosaic images provide only R, G, and B bands and processes such as image sharpening and color balancing are applied, there is a limitation that the spectral information of original images is distorted, so various indices were extracted and classified using R, G, and B bands to compensate for this. As a result of the study, the accuracy of image classification results using only mosaic images was about 72%, while the accuracy of image classification results using indices extracted from R, G, and B bands together was about 79%. Through this, it was confirmed that when performing image classification using mosaic images on the Korean Peninsula, the image classification results can be improved if the indices extracted from R, G, and B bands are used together. These research results are expected to be applied not only to mosaic images but also to images in which spectral information is limited or only R, G, and B bands are provided.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• Ocean and Polar Research
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• v.35 no.4
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• pp.395-405
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• 2013

The aim of this study is to suggest an optimal survey method for coastal habitat monitoring around Weno Island in Chuuk Atoll, Federated States of Micronesia (FSM). This study was carried out to compare and analyze differences between in situ survey (PHOTS) and high spatial satellite imagery (Worldview-2) with regard to the coastal habitat distribution patterns of Weno Island. The in situ field data showed the following coverage of habitat types: sand 42.4%, seagrass 26.1%, algae 14.9%, rubble 8.9%, hard coral 3.5%, soft coral 2.6%, dead coral 1.5%, others 0.1%. The satellite imagery showed the following coverage of habitat types: sand 26.5%, seagrass 23.3%, sand + seagrass 12.3%, coral 18.1%, rubble 19.0%, rock 0.8% (Accuracy 65.2%). According to the visual interpretation of the habitat map by in situ survey, seagrass, sand, coral and rubble distribution were misaligned compared with the satellite imagery. While, the satellite imagery appear to be a plausible results to identify habitat types, it could not classify habitat types under one pixel in images, which in turn overestimated coral and rubble coverage, underestimated algae and sand. The differences appear to arise primarily because of habitat classification scheme, sampling scale and remote sensing reflectance. The implication of these results is that satellite imagery analysis needs to incorporate in situ survey data to accurately identify habitat. We suggest that satellite imagery must correspond with in situ survey in habitat classification and sampling scale. Subsequently habitat sub-segmentation based on the in situ survey data should be applied to satellite imagery.