• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1573-1587
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• 2021

A Drone image is an ultra-high-resolution image that is several or tens of times higher in spatial resolution than a satellite or aerial image. Therefore, drone image-based remote sensing is different from traditional remote sensing in terms of the level of object to be extracted from the image and the amount of data to be processed. In addition, the optimal scale and size of data used for model training is different depending on the characteristics of the applied deep learning model. However, moststudies do not consider the size of the object to be found in the image, the spatial resolution of the image that reflects the scale, and in many cases, the data specification used in the model is applied as it is before. In this study, the effect ofspatial resolution and image size of drone image on the accuracy and training time of the semantic segmentation deep learning model of six wintering vegetables was quantitatively analyzed through experiments. As a result of the experiment, it was found that the average accuracy of dividing six wintering vegetablesincreases asthe spatial resolution increases, but the increase rate and convergence section are different for each crop, and there is a big difference in accuracy and time depending on the size of the image at the same resolution. In particular, it wasfound that the optimal resolution and image size were different from each crop. The research results can be utilized as data for getting the efficiency of drone images acquisition and production of training data when developing a winter vegetable segmentation model using drone images.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.386-389
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• 2008

It is important that the training stage of a supervised classification is designed to provide the spectral information. On the design of the training stage of a classification typically calls for the use of a large sample of randomly selected pure pixels in order to characterize the classes. Such guidance is generally made without regard to the specific nature of the application in-hand, including the classifier to be used. An approach to the training of a support vector machine (SVM) classifier that is the opposite of that generally promoted for training set design is suggested. This approach uses a small sample of mixed spectral responses drawn from purposefully selected locations (geographical boundaries) in training. A sample of such data should, however, be easier and cheaper to acquire than that suggested by traditional approaches. In this research, we evaluated them against traditional approaches with high-resolution satellite data. The results proved that it can be used small mixed pixels to derive a classification with similar accuracy using a large number of pure pixels. The approach can also reduce substantial costs in training data acquisition because the sampling locations used are commonly easy to observe.

• Smart Media Journal
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• v.10 no.4
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• pp.9-14
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• 2021

In the case of the existing remote collaboration, the remote support service-oriented system is not suitable for the use of the field-oriented multi-person remote collaboration system. This paper is a remote collaboration system development for various industrial sites. We develop remote support and work management that faces the various needs of industrial sites, real-time video remote support between workers, and real-time voice work sharing between workers. In addition, The goal of the development aims to increase the usability by strengthening the security function through encryption in the video and to develop a more efficient system. Finally, the development contents are the remote management and the support software development, Android app development for worker, WebRTC-based remote collaboration system construction and development, and prototype development. These products are expected to increase demand and increase sales by installing and operating at industrial sites, and can promote manpower training, understanding trending technologies, and improving capabilities.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.628-638
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• 2023

Maritime Autonomous Surface ships(MASS) are gradually gaining importance. Until fully autonomous ships are developed, they will likely be controlled by remote operators who are based in a Remote Operations Center. However, there is currently no internationally or domestically established licensing for them. This issue can potentially pose a risk to navigation safety due to operations being handled by unqualified remote operators. We conducted a literature review and proposed criteria for the adoption of a licensing system for remote operators. We have futher offered alternatives to integrate this license into the existing officer licensing system, and analyzed them using Analytic Hierarchy Process(AHP). Subsequently,, theprimary need to enact legislation for remote operators is observed. The most preferred approach is to include the occupation of a remote operator in the Ship Officer Act, Article 4: Occupational Categories and Class of Licenses. Therefore, it would be logical for the organizational structure of the Remote Operation Center to mirror the traditional Bridge Resource Management. This study will contribute to the efficient training of remote operators and the safe navigation of autonomous ships with a focus on human resource management.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.449-460
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• 2020

Super-resolution is a technique used to reconstruct an image with low-resolution into that of high-resolution. Recently, deep-learning based super resolution has become the mainstream, and applications of these methods are widely used in the remote sensing field. In this paper, we propose a super-resolution method based on the deep back-projection network model to improve the satellite image resolution by the factor of four. In the process, we customized the loss function with the edge loss to result in a more detailed feature of the boundary of each object and to improve the stability of the model training using generative adversarial network based on Wasserstein distance loss. Also, we have applied the detail preserving image down-scaling method to enhance the naturalness of the training output. Finally, by including the modified-residual learning with a panchromatic feature in the final step of the training process. Our proposed method is able to reconstruct fine features and high frequency information. Comparing the results of our method with that of the others, we propose that the super-resolution method improves the sharpness and the clarity of WorldView-3 and KOMPSAT-2 images.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.75-80
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• 2023

In medically vulnerable areas in Korea, public health doctors play a significant role in providing not only general medical care but also emergency medical services to the local residents. However, it has been observed that public health doctors generally lack field experience, resulting in insufficient ability to handle emergency patients and to effectively use medical equipment. This study confirmed the effectiveness of education after conducting remote education using smart glasses on how to use medical equipment necessary for public health doctors. Specifically, real wear was used for smart glasses for medical equipment utilization education, and 10 public health officials in 10 islands in Shinan-gun were targeted. After the training, both the effect of using the equipment and the level of satisfaction were 3 or higher. Therefore, it was confirmed that remote education using smart glasses can be usefully used for public health doctors in medically vulnerable areas.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.429-431
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• 2003

The induced learning retardation method involves the temporary inhibition of the artificial neural network’s active units from participating in the error reduction process during training. This stimulates the less active units to contribute significantly to reduce the network error. However, some less active units are not sensitive to stimulation making them almost useless. The network can then be pruned by removing the less active units to make it smaller and more efficient. This study focuses on making the network more efficient and accurate by developing the induced learning retardation and pruning sequence training method. The developed procedure results to faster learning and more accurate artificial neural network for satellite image classification.

• Management & Information Systems Review
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• v.3
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• Journal of Fisheries and Marine Sciences Education
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• v.6 no.2
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• pp.130-142
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• 1994

To investigate the effective scheme to secure the specialized instructors in the governmental officials training facilities, the authors made a survey on the opinion concerning the dispatching service system of officials as instructors to the Fisheries Officials Training Institute on the fisheries specialized officials in the Fisheries Agency and its affiliated organization. The obtained results can be summarized as follows : 1. The exiting personnel management system has a lot of difficulties to secure the specialized instructors. Even though the difficulties may be solved by inviting part-time instructors to some extent, it is remote from the goal. The active utilization of dispatching service system of specialized officials to the Institute as instructors during the limited term will be effective rather than re-arrangement of personnel system or amendment of laws and ordinances to secure the specialized instructors. 2. The response of specialized officials who are objected in the present survey on the dispatching service system to the Institute appeared to be affirmative considerably, and then the dispatching service system may be expected of high efficiency in its realization.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.461-469
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• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.