• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1334-1336
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• 2003

On the basis of comprehensive analysis and summarization of the image fusion algorithms provided by PCI Geomatica software, deficiencies in image fusion processing functions of this software are put forwarded in this paper. This limitation could be improved by further developing PCI Geomatica on the user’ side. Five effective algorithms could be added into PCI Geomatica. In this paper, the detailed description of how to customize and further develop PCI Geomatica by using Microsoft Visual C++ 6.0, PCI SDK Kit and GDB technique is also given. Through this way, the remote sensing imagery fusion functions of PCI Geomatica software can be extended.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.907-916
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• 2020

Images taken from airplanes, satellites and drones have been used in various realms, and the kinds and specifications of images are enlarged gradually. Despite the importance of images on diverse applications, the quality information of the images is controlled by each agency or institute respectively without any principle, or even is neglected, because the application of standards to the final products of image is not easy in Korea. We aim to review necessities and strategies for applying international standards on image and to suggest potential issues and possibilities to make standards in action.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1200-1202
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• 2003

Satellite and aerial images are very useful means to monitor ecological and environmental situation. Nowadays more and more officials at Ministry of Environment in Korea need to access and use these image data through networks like internet or intranet. However it is very hard to manage and service these image data through internet or intranet, because of its size problem. In this paper very large image data service system for Ministry of Environment is constructed on web environment using image compression and web based image processing technology. Through this system, not only can officials in Ministry of Environment access and use all the image data but also can achieve several image processing effects on web environment. Moreover officials can retrieve attribute information from vector GIS data that are also integrated with the system.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.28-30
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• 2004

The West-East Nature Gas Pipeline is a great project in China. Advanced remote sensing technology combined with GIS and GPS is used to select the favorable plan from various possible routes through interpreting the information of topographic landform, regional geology, disaster geology, traffic conditions and nature environment from remote sensing images. There are a lot of changes in geographical and environmental factors along such pipelines due to the rapid development in China. Image maps produced from new satellite data can identify these changes and be used successfully not only on route-selection studies but also on in situ investigation, together with GPS. Results from detail analysis provide necessary information and parameters for plan, design and construction of the pipeline and they are also the basic data for the pipeline database. The set of techniques has been applied on planning and designing several pipelines successfully.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.820-823
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• 1996

In contrast to parts of relatively simple shapes, it is important to match their cross-sectional shapes during mating parts of complicated shapes. It requires the 2.pi. information along their matching boundary to figure out their relative geometrical shapes. In this paper, we propose a method measuring a misalignment at the interface during mating parts with the complicated shapes by using the omnidirectional image sensing system(OLSSA). Also we carried out experiments in order to prove the method, and the results show the feasibility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2748-2766
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• 2016

Compared with traditional patch-based sparse representation, recent studies have concluded that group-based sparse representation (GSR) can simultaneously enforce the intrinsic local sparsity and nonlocal self-similarity of images within a unified framework. This article investigates an accelerated split Bregman method (SBM) that is based on GSR which exploits image compressive sensing (CS). The computational efficiency of accelerated SBM for the measurement matrix of a partial Fourier matrix can be further improved by the introduction of a fast Fourier transform (FFT) to derive the enhanced algorithm. In addition, we provide convergence analysis for the proposed method. Experimental results demonstrate that accelerated SBM is potentially faster than some existing image CS reconstruction methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.183-185
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• 2003

Spatially referenced mobile mapping (MM) images contain rich information of man-made objects , e.g. road centerlines, buildings, light poles, traffic signs ,billboards and line trees etc. Therefore, the applications in transportation, urban 3D reconstruction, utility management are implemented increasingly. It’s a fundamental issue lies in MM image process that how to orient this image in the object space including interior orientation of camera and the exterior orientation of image. In this paper, the algorithm of automatic acquirement of DC (Digital Camera) parameters based on MM images is illustrated. And then, the mapping between image space and object space for MM images is described.