• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.997-1008
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• 2023

Ship detection at sea can be performed in various ways. In particular, satellites can provide wide-area surveillance, and Synthetic Aperture Radar (SAR) imagery can be utilized day and night and in all weather conditions. To propose an efficient ship detection method from SAR images, this study aimed to apply the You Only Look Once Version 5 (YOLOv5) model to Sentinel-1 images and to analyze the difference between individual vs. integrated models and the accuracy characteristics by polarization. YOLOv5s, which has fewer and lighter parameters, and YOLOv5x, which has more parameters but higher accuracy, were used for the performance tests (1) by dividing each polarization into HH, HV, VH, and VV, and (2) by using images from all polarizations. All four experiments showed very similar and high accuracy of 0.977 ≤ AP@0.5 ≤ 0.998. This result suggests that the polarization integration model using lightweight YOLO models can be the most effective in terms of real-time system deployment. 19,582 images were used in this experiment. However, if other SAR images,such as Capella and ICEYE, are included in addition to Sentinel-1 images, a more flexible and accurate model for ship detection can be built.

• Korean Institute of Interior Design Journal
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• v.26 no.2
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• pp.22-32
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• 2017

This research is to analyze the spatial information search features which shown by Eye fixation and movement and conducted eye tracking experiment for targeting sports shop spatial images which it are same but looks different. This is able to find out the eye movement feature according to placement of goods from the eye movement and movement distance of spatial visitor, and the result can be defined as following. First, the whole original-reverse left / right images have a higher number of observations in the [IN] area than in the [OUT] area. This is because after eye taking high observations in LA area of [IN] have been jump-over [OUT], performed search activities in low eye fixation without high eye fixation. Second, there was a difference in the frequency of the observation data as the composition of the images changed. The original image has been often fixed the eyes in LA area, and the one that has been observed for a long time is reverse left / right image. Also, fixation point was shown higher at the reverse left / right image as jump-over from [OUT] area to [IN] area. If LA area seen as reverse left / right image, it is located in right-hand side. The case where the dominant area is on the right side has a characteristic that the eye fixation is longer. This can be understand that the arrangement of products for attract the customer's attention in the commercial space might be more effective when it is on the right side. Third, the moving distance(IN ${\rightarrow}$ OUT) of the sight pointed to external from LA area was long in the both original-reverse left / right images, but it is no relation with search direction([IN${\rightarrow}$OUT] [IN${\rightarrow}$OUT]) of the sight. In other words, the sight that entered in LA area can be seen as visual perception activity for re-searching after big jump-over, in the case go in to outward (OUT area) after searching for more than certain time. The fact that the moving distance of eye is relatively short in the [IN ${\rightarrow}$ OUT] process considered as that the gaze that stays outside the LA area naturally enters in to LA area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.633-639
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• 2018

High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

• Journal of Biosystems Engineering
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• v.39 no.1
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• pp.47-56
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• 2014

Purpose: In protected crop production facilities such as greenhouse and plant factory, farmers should be present and/or visit frequently to the production site for maintaining optimum environmental conditions and better production, which is time and labor consuming. Monitoring of environmental condition is highly important for optimum control of the conditions, and the condition is not uniform within the facility. Objectives of the paper were to investigate spatial and vertical variability in ambient environmental variables and to provide useful information for sensing and control of the environments. Methods: Experiments were conducted in a strawberry-growing greenhouse (greenhouse 1) and a cherry tomato-growing greenhouse (greenhouse 2). Selected ambient environmental variables for experiment in greenhouse 1 were air temperature and humidity, and in greenhouse 2, they were air temperature, humidity, PPFD (Photosynthetic Photon Flux Density), and $CO_2$ concentration. Results: Considerable spatial, vertical, and temporal variability of the ambient environments were observed. In greenhouse 1, overall temperature increased from 12:00 to 14:00 and increased after that, while RH increased continuously during the experiments. Differences between the maximum and minimum temperature and RH values were greater when one of the side windows were open than those when both of the windows were closed. The location and height of the maximum and minimum measurements were also different. In greenhouse 2, differences between the maximum and minimum air temperatures at noon and sunset were greater when both windows were open. The maximum PPFD were observed at a 3-m height, close to the lighting source, and $CO_2$ concentration in the crop growing regions. Conclusions: In this study, spatial, vertical, and temporal variability of ambient crop growing conditions in greenhouses was evaluated. And also the variability was affected by operation conditions such as window opening and heating. Results of the study would provide information for optimum monitoring and control of ambient greenhouse environments.

• The Journal of the Acoustical Society of Korea
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• v.21 no.5
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• pp.471-477
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• 2002

Recently, lip-reading has been studied actively as an auxiliary method of automatic speech recognition(ASR) in noisy environments. However, almost of research results were obtained based on the database constructed in indoor condition. So, we dont know how developed lip-reading algorithms are robust to dynamic variation of image. Currently we have developed a lip-reading system based on image-transform based algorithm. This system recognize 22 words and this word recognizer achieves word recognition of up to 53.54%. In this paper we present how stable the lip-reading system is in environmental variance and what the main variant factors are about dropping off in word-recognition performance. For studying lip-reading robustness we consider spatial valiance (translation, rotation, scaling) and illumination variance. Two kinds of test data are used. One Is the simulated lip image database and the other is real dynamic database captured in car environment. As a result of our experiment, we show that the spatial variance is one of degradations factors of lip reading performance. But the most important factor of degradation is not the spatial variance. The illumination variances make severe reduction of recognition rates as much as 70%. In conclusion, robust lip reading algorithms against illumination variances should be developed for using lip reading as a complementary method of ASR.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.297-304
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• 2015

The change detection algorithms, based on remotely sensed satellite imagery, can be applied to various applications, such as the hazard/disaster analysis and the land monitoring. However, unchanged areas sometimes detected as the changed areas due to various errors in relief displacements and noise pixels, included in the original multi-temporal dataset at the application of unsupervised change detection algorithm. In this research, the object-based changed detection for the high-spatial resolution satellite images is applied by using the IR-MAD (Iteratively Reweighted- Multivariate Alteration Detection), which is one of those representative change detection algorithms. In additionally, we tried to increase the accuracy of change detection results with using the additional information, based on the cross-sharpening method. In the experiment, we used the KOMPSAT-2 satellite sensor, and resulted in the object-based IR-MAD algorithm, representing higher changed detection accuracy than that by the pixel-based IR-MAD. Also, the object-based IR-MAD, focused on cross-sharpened images, increased in accuracy of changed detection, compared to the original object-based IR-MAD. Through these experiments, we could conclude that the land monitoring and the change detection with the high-spatial-resolution satellite imagery can be accomplished efficiency by using the object-based IR-MAD algorithm.

• 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.

• Journal of the Korean Regional Science Association
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• v.37 no.4
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• pp.75-93
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• 2021

By defining a travel disparity based on the degree to which travelers diversify their destinations, this paper examines how socioeconomic and spatial factors affect the travel disparity among the elderly in Seoul. This paper uses the COVID-19 pandemic as a natural experiment which can bring about different behavioral responses among the older travelers. Using the smart card data, we compare the destination diversification patterns before and after the pandemic. In the early morning(4:30-9:00), the degree of destination diversification varies between the core and the periphery and this trend persists through the pandemic. In the late morning(9:00-12:00), a new trend of disparity appeared after the pandemic. Although those who hold higher socioeconomic status and live closer to the core have a larger range of choices for destinations, the difference of range did not lead to differences in diversification before the pandemic, due to the discretionary nature of the elderly's trip. In contrast, as the elderly were forced to search alternative destinations right after the outbreak of the pandemic, the range of choices became an important factor causing observable differences in destination diversification. The findings suggest that the travel disparity observed during the pandemic is due to the difference in the range of choices by socioeconomic and spatial factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1666-1689
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• 2021

The red line of Permanent Basic Farmland is the most important part in the "three-line" demarcation of China's national territorial development plan. The scientific and reasonable delineation of the red line is a major strategic measure being taken by China to improve its ability to safeguard the practical interests of farmers and guarantee national food security. The delineation of Permanent Basic Farmland zoning (DPBFZ) is essentially a multi-objective optimization problem. However, the traditional method of demarcation does not take into account the synergistic development goals of conservation of cultivated land utilization, ecological conservation, or urban expansion. Therefore, this research introduces the idea of artificial immune optimization and proposes a multi-objective model of DPBFZ red line delineation based on a clone selection algorithm. This research proposes an objective functional system consisting of these three sub-objectives: optimal quality of cropland, spatially concentrated distribution, and stability of cropland. It also takes into consideration constraints such as the red line of ecological protection, topography, and space for major development projects. The mathematical formal expressions for the objectives and constraints are given in the paper, and a multi-objective optimal decision model with multiple constraints for the DPBFZ problem is constructed based on the clone selection algorithm. An antibody coding scheme was designed according to the spatial pattern of DPBFZ zoning. In addition, the antibody-antigen affinity function, the clone mechanism, and mutation strategy were constructed and improved to solve the DPBFZ problem with a spatial optimization feature. Finally, Tongxu County in Henan province was selected as the study area, and a controlled experiment was set up according to different target preferences. The results show that the model proposed in this paper is operational in the work of delineating DPBFZ. It not only avoids the adverse effects of subjective factors in the delineation process but also provides multiple scenarios DPBFZ layouts for decision makers by adjusting the weighting of the objective function.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.815-822
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• 2023

Organic pollution is one of the most common forms of water contamination. Under the Water Quality Conservation Act, indicators for measuring organic substances include BOD, COD, and TOC. Analysis of BOD and COD is labor-intensive, and in the case of organic substances where biological decomposition is not feasible or toxic substances are present, the accuracy is often low. Therefore, the Ministry of Environment is shifting towards TOC-centric management. With advancements in sensor technology today, various parameters can be monitored using sensors. In this study, digital monitoring of river TOC using a spectrophotometer called Spectro::lyser V3 was conducted. Initially, experiments were carried out at the Andong River Experiment Center to assess the applicability of the measurement equipment. Subsequently, data collected at the confluence of the Nakdong River was analyzed for the spatial distribution of TOC using the Kriging technique. This research proposes the utilization of sensors for river TOC monitoring and spatial distribution analysis. Real-time monitoring of changes in river TOC concentration can serve as fundamental data for pollution monitoring and response. Sensor-based river monitoring offers advantages in terms of temporal resolution and real-time data acquisition. When various spatial information interpretation methods are applied, it is expected to contribute to diverse studies such as aquatic ecological health, river water source selection, and stratification analysis in the future.