• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.875-883
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• 2023

In this letter, we used PlanetScope imagery to conduct experiments on the color characteristics for oil type classification of marine oil spills through Red-Green-Blue (RGB) histogram analysis. The histograms of marine oil spills can be divided into three categories (dark black tones, light silver tones, and light rainbow tones) according to the distribution of pixel values in each band. Thick oil layers with dark black tones can be classified as heavy oil, while thin oil layers with light silver and rainbow tones can be classified as light oil. As more images are analyzed in the future, these oil spill detection and classification methods will become more generalized and reliable.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.843-849
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• 2023

This study attempted to determine the optimal algorithm after quantitatively analyzing noise, SNR, and CNR measurements by reconstructing four algorithms (Standard, Soft, Bone, and Detail) from head and neck CT images of patients who underwent implant surgery. As an analysis method, pixel values were calculated through the region of interest in the reconstructed image using the Image J program. For noise, SNR, and CNR, the region of interest was measured at the location of the pharynx, masseter muscle, and parotid gland in the image, and the mean and SD values were obtained. The values of SNR and CNR were calculated based on the given formula. As a result, the standard algorithm showed the lowest noise and the highest SNR. CNR was highest in the Soft algorithm, but showed no significant difference from the Standard algorithm. Therefore, it is believed that the Standard algorithm is the optimal algorithm for examining patients wearing intraoral implants in head and neck CT examinations. We hope that the data from this study will be used as basic data for image evaluation in head and neck CT examinations, and that the quality of images will be further improved through various algorithm changes. It is believed that this will be an opportunity to do so.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.985-991
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• 2023

A tissue-equivalent phantom is necessary for quality control of hyperthermia therapy. However, since there is no phantom for this purpose, phantoms made from agar are being used in various studies. The tissue-equivalent properties of the agar phantom were confirmed by comparison with the tissue-equivalent material bolus in this study. CT images of the agar phantom and bolus were acquired, and tissue equivalent characteristics were analyzed with image analysis and dose calculation using a computerized radiation therapy planning system. The average pixel value was 96.960±10.999 in bolus, 108.559±8.233 in 3% agar phantom, and 111.844±8.651 in 4% agar phantom. Using the SSD technique, 100 cGy was prescribed at a depth of 1.5 cm and 6 MV X -ray was set to irradiated to 10x10 cm2, and the absorbed dose according to depth was calculated from the central axis of the beam. The intraclass correlation coefficient of dose distribution of bolus, 3% agar phantom, and 4% agar phantom was 0.979 (p<.001, 95%CI .957-.991). The density (g/cm3) at the point where the absorbed dose was calculated was 0.990±0.020 at the bolus, 1.018±0.020 at the 3% agar phantom, and 1.035±0.024 at the 4% agar phantom. In this study, the internal density distribution and uniformity of the agar phantom were confirmed to be appropriate as a tissue equivalent material by analysis of CT images and a computerized radiation therapy planning system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.226-235
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• 2023

This study aimed to supplement the shortcomings of the Multiple-sensor-based Frost Observation System (MFOS). The developed frost observation system is an improvement of the existing system. Based on the leaf wetness sensor (LWS), it not only detects frost but also functions to predict surface temperature, which is a major factor in frost occurrence. With the existing observation system, 1) it is difficult to observe ice (frost) formation on the surface when capturing an image of the LWS with an RGB camera because the surface of the sensor reflects most visible light, 2) images captured using the RGB camera before and after sunrise are dark, and 3) the thermal infrared camera only shows the relative high and low temperature. To identify the ice (frost) generated on the surface of the LWS, a LWS that was painted black and three sheets of glass at the same height to be used as an auxiliary tool to check the occurrence of ice (frost) were installed. For RGB camera shooting before and after sunrise, synchronous LED lighting was installed so the power turns on/off according to the camera shooting time. The existing thermal infrared camera, which could only assess the relative temperature (high or low), was improved to extract the temperature value per pixel, and a comparison with the surface temperature sensor installed by the National Institute of Meteorological Sciences (NIMS) was performed to verify its accuracy. As a result of installing and operating the MFOS v2, which reflects these improvements, the accuracy and efficiency of automatic frost observation were demonstrated to be improved, and the usefulness of the data as input data for the frost prediction model was enhanced.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• The Journal of the Acoustical Society of Korea
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• v.27 no.2
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• pp.57-65
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• 2008

Setting parameters of Ultrasonic scanners influence the quality of ultrasonic images. In order to obtain optimized images sonographers need to understand the effects of the setting parameters on ultrasonic images. The present study considered typical four parameters including TGC (Time Gain Control), Gain, Frequency, DR (Dynamic Range). LCS (low contrast sensitivity) was chosen to quantitatively compare the quality of the images. In the present experiment LCS targets of a standard ultrasonic test phantom (539, ATS, USA) were imaged using a clinical ultrasonic scanner (SA-9000 PRIME, Medison, Korea). Altering the settings in the parameters of the ultrasonic scanner, 6 LCS target images (+15 dB, +6 dB, +3 dB, -3 dB, -6 dB, -15 dB) to each setting were obtained, and their LCS values were calculated. The results show that the mean pixel value (LCS) is the highest at the max setting in TGC, mid to max in gain and pen mode in frequency and 40-66 dB in DR. Among all images, the image being the highest in LCS was obtained at the setting of DR 40 dB. It is expected that the results will be of use in setting the parameters when ultrasonically examining masses often clinically found In either solid lesions (similar to +15, +6, +3 dB targets) or cystic lesions (similar to -15, -6, -3 dB targets).

• Information Systems Review
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• v.25 no.1
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• pp.129-143
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• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• Korean Journal of Radiology
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• v.24 no.10
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• pp.1006-1016
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• 2023

Objective: Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT). Materials and Methods: In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT. The image quality of PCD-CT was assessed qualitatively by three independent radiologists for overall image quality, edge sharpness, image noise, lesion conspicuity, and diagnostic confidence using a 5-point Likert scale (5 = excellent), and quantitatively for signal homogeneity using the coefficient of variation (CV) of Hounsfield Units (HU) values and modulation transfer function (MTF) via the full width at half maximum (FWHM) in the frequency space. The results were compared with those of the current clinical standard EID-CT protocols as controls. Additionally, the radiation dose (CTDIvol) was determined. Results: We enrolled 35 patients with multiple myeloma (mean age 69.8 ± 9.1 years; 18 [51%] males). Qualitative image analysis revealed superior scores (median [interquartile range]) for PCD-CT regarding overall image quality (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), edge sharpness (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), image noise (4.0 [4.0-4.0] vs. 3.0 [3.0-4.0]), lesion conspicuity (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), and diagnostic confidence (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]) compared with EID-CT (P ≤ 0.004). In quantitative image analyses, PCD-CT compared with EID-CT revealed a substantially lower FWHM (2.89 vs. 25.68 cy/pixel) and a significantly more homogeneous signal (mean CV ± standard deviation [SD], 0.99 ± 0.65 vs. 1.66 ± 0.5; P < 0.001) at a significantly lower radiation dose (mean CTDIvol ± SD, 3.33 ± 0.82 vs. 7.19 ± 3.57 mGy; P < 0.001). Conclusion: Whole-body PCD-CT provides significantly higher subjective and objective image quality at significantly reduced radiation doses than the current clinical standard EID-CT protocols, along with readily available multi-spectral data, facilitating the potential for further advanced post-processing.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.4
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• pp.365-384
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• 2024

The development of scanning technology is accelerating for safer and more efficient automated inspection than human-based inspection. Research on automatically detecting facility damage from images collected using computer vision technology is also increasing. The pixel size, quality, and quantity of an image can affect the performance of deep learning or image processing for automatic damage detection. This study is a basic to acquire high-quality raw image data and camera performance of a mobile tunnel scanning system for automatic detection of damage based on deep learning, and proposes a method to quantitatively evaluate image quality. A test chart was attached to a panel device capable of simulating a moving speed of 40 km/h, and an indoor test was performed using the international standard ISO 12233 method. Existing image quality evaluation methods were applied to evaluate the quality of images obtained in indoor experiments. It was determined that the shutter speed of the camera is closely related to the motion blur that occurs in the image. Modulation transfer function (MTF), one of the image quality evaluation method, can objectively evaluate image quality and was judged to be consistent with visual observation.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.80-90
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• 2014

The objective of this research is to estimate the stand volume of Pinus koraiensis, by using the investigated volume and the information of remote sensing(RS), in the research forest of Kangwon National University. The average volume of the research forest per hectare was $307.7m^3/ha$ and standard deviation was $168.4m^3/ha$. Before and after carrying out 3 by 3 majority filtering on TM image, eleven indices were extracted each time. Independent variables needed for linear regression equation were selected using mean pixel values by indices. The number of indices were eleven: six Bands(except for thermal Band), NDVI, Band Ratio(BR1:Band4/Band3, BR2:Band5/Band4, BR3:Band7/Band4), Tasseled Cap-Greeness. As a result, NDVI and TC G were chosen as the most suitable indices for regression before and after filtering, and R-squared was high: 0.736 before filtering, 0.753 after filtering. As a result of error verification for an exact comparison, RMSE before and after filtering was about $69.1m^3/ha$, $67.5m^3/ha$, respectively, and bias was $-12.8m^3/ha$, $9.7m^3/ha$, respectively. Therefore, the regression conducted with filtering was selected as an appropriate model because of low RMSE and bias. The estimated stand volume applying the regression was $160,758m^3$, and the average volume was $314m^3/ha$. This estimation was 1.2 times higher than the actual stand volume of Pinus koraiensis.