• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.459-466
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• 2023

In the last decade, artificial intelligence's dramatic advancement with the development of various deep learning techniques has significantly contributed to remote sensing fields and satellite image applications. Among many prominent areas, super-resolution research has seen substantial growth with the release of several benchmark datasets and the rise of generative adversarial network-based studies. However, most previously published remote sensing benchmark datasets represent spatial resolution within approximately 10 meters, imposing limitations when directly applying for super-resolution of small objects with cm unit spatial resolution. Furthermore, if the dataset lacks a global spatial distribution and is specialized in particular land covers, the consequent lack of feature diversity can directly impact the quantitative performance and prevent the formation of robust foundation models. To overcome these issues, this paper proposes a method to generate benchmark datasets by simulating the modulation transfer functions of the sensor. The proposed approach leverages the simulation method with a solid theoretical foundation, notably recognized in image fusion. Additionally, the generated benchmark dataset is applied to state-of-the-art super-resolution base models for quantitative and visual analysis and discusses the shortcomings of the existing datasets. Through these efforts, we anticipate that the proposed benchmark dataset will facilitate various super-resolution research shortly in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2361-2376
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• 2023

The electrocardiogram (ECG) signal is commonly used to screen and diagnose cardiovascular diseases. In recent years, deep neural networks have been regarded as an effective way for automatic ECG disease diagnosis. The convolutional neural network is widely used for ECG signal extraction because it can obtain different levels of information. However, most previous studies adopt single scale convolution filters to extract ECG signal features, ignoring the complementarity between ECG signal features of different scales. In the paper, we propose a dual-scale network with convolution filters of different sizes for 12-lead ECG classification. Our model can extract and fuse ECG signal features of different scales. In addition, different spatial and time periods of the feature map obtained from the 12-lead ECG may have different contributions to ECG classification. Therefore, we add a spatial-temporal attention to each scale sub-network to emphasize the representative local spatial and temporal features. Our approach is evaluated on PTB-XL dataset and achieves 0.9307, 0.8152, and 89.11 on macro-averaged ROC-AUC score, a maximum F1 score, and mean accuracy, respectively. The experiment results have proven that our approach outperforms the baselines.

• Journal of the Korean Institute of Rural Architecture
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• v.13 no.1
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• pp.29-36
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• 2011

This paper aims to clarify the residential quarter organization and housing spatial form of fishing village in Eocheong-do Island. The results are as follows. First, island residential quarter is formed along the coastline, the detached house and the shop house are mainly located in the front of the coastline. Second, the inclination road which connects the northernmost end part and the seashore road is maintained as a road system of a residential quarter from the Japanese colony term until now. Third, It is the feature that Eocheong-do island has the wide width of the front of a site compared with other island residential quarters. Therefore, the frontage wide of a building is also large. Fourth, the entrance of the building where the frontage is mainly located right in the middle, and each rooms are located in the surroundings of it. Finally, the typical community space of Eocheong-do island is Pyeongsang(平床). Pyeongsang is space which receives various functions in inhabitants' life cycle.

• Spatial Information Research
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• v.15 no.2
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• pp.181-187
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• 2007

To make digital terrain data, automatic correlation matching by stereo airborne/satellite images has been researched. The result of automatic correlation matching has a limit on extracting exact ground height because of angle of sensor, tree of height. Therefore, the amount of editing works depend on the distribution of spatial feature in images as well as image quality. This paper shows that the automatic correlation matching result was affected by density and height of tree.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.45-48
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• 2008

Since the mid-2000, so-called Web 2.0 paradigm was emerged and has been widely extended to actual Web markets with supporting Web technologies. Among Web 2.0 services, mashups with Open APIs is regarded as one of core technologies. Web mapping is also the most dominant mashup application in Open API-based applications, so that various mashups with Google Map API and other Open APIs are developing. In this paper, current technological status is explained with web GIS file formats of KML. While, a prototype is web-based GIS layer authoring system for general users, without any kinds of GIS tools and external spatial databases. This mashup application will be used web-based feature extraction for geo-spatial web for public users.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.525-527
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• 2003

Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• Korean Journal of Remote Sensing
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• v.24 no.3
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• pp.251-256
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• 2008

Morphing method, one of classic image processing algorithms, has been used in various application fields. The motivation of this work is to investigate its applicability in consideration to geo-spatial data including airborne or space-borne images. For this purpose, the Beier and Neely morphing algorithm is tentatively implemented in the form of a prototype with user interface. As the results, this feature-based morphing with paired image sets can be used for general users: image simulation using two or more images and construction of color-blending image between source image and destination image in different types. Some simple application cases were demonstrated. This scheme is the simple and useful approach for those who want to utilize both geo-spatial data sets and airborne/space-borne image sets.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.1-9
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• 2024

As Advanced Air Mobility (AAM) technologies evolve, ensuring accurate navigation and localization in complex urban airspaces has become crucial. Because the Global Navigation Satellite System (GNSS) is prone to vulnerabilities in urban flight environment, an alternative localization technique is required. This paper examines vision-based localization technologies to enhance GNSS-free navigation. In addition, we explore various geo-referencing studies that utilize pre-existing spatial databases to improve the accuracy of vision-based localization under GNSS-denied conditions. This paper discusses the various types of onboard vision camera sensors, vision-based localization, spatial information databases, feature extraction methods, and matching techniques that contribute to the development of a vision-based localization and geo-referencing system for AAM, ensuring safety and reliability in urban operations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.37-44
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• 2003

This study is focused on development of generalization processing for 1/25,000 digital mapping using l/5,000 digital map. The generalization processing for digital map included in spatial and attribute information consists of 3 steps. The first, elimination of layer which is not included in 1/25,000 feature code. The second, classification of 8 feature codes. The third, merging of spatial and attribute information. Therefore using generalization workflow developed in this project, automatic generalization system will develope optimal in the future and also contribute product to small scale digital map and thematic map.