• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.305-313
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• 2019

Recently, spatial information is being constructed actively based on the images obtained by drones. Because occlusion areas occur due to buildings as well as many obstacles, such as trees, pedestrians, and banners in the urban areas, an efficient way to resolve the problem is necessary. Instead of the traditional way, which replaces the occlusion area with other images obtained at different positions, various models based on deep learning were examined and compared. A comparison of a type of feature descriptor, HOG, to the machine learning-based SVM, deep learning-based DNN, CNN, and RNN showed that the CNN is used broadly to detect and classify objects. Until now, many studies have focused on the development and application of models so that it is impossible to select an optimal model. On the other hand, the upgrade of a deep learning-based detection and classification technique is expected because many researchers have attempted to upgrade the accuracy of the model as well as reduce the computation time. In that case, the procedures for generating spatial information will be changed to detect the occlusion area and replace it with simulated images automatically, and the efficiency of time, cost, and workforce will also be improved.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.397-400
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• 2017

Recently, a lot of images containing various global movements have been generated by the activation of the photographic equipment such as the drone and the action cam. In this case, when the motion such as rotation, scaling is generated, it is difficult to expect a high coding efficiency in the conventional inter-picture prediction method using the 2D motion vector. In this paper, we propose a video coding method that reflects global motion through homography reference pictures. As a proposed method, there are 1) a method of generating a new reference picture by grasping a global motion relation between a current picture and a reference picture by homography, and 2) a method of utilizing a homography reference picture for inter-picture prediction. The experiment was applied to the HEVC reference software HM 14.0, and the experimental result showed an increase in encoding efficiency of 6.6% based on RA. Especially, the results using the videos with rotational motion have a maximum coding efficiency of 32.6%, which is expected to show high efficiency in video, which is often represented by complex global motion such as drones.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.185-198
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• 2021

Researches using UAVs are being actively conducted in the field of quickly constructing 3D spatial information in small areas. In this study, without using ground control points, a stereo camera was mounted on a UAV to collect images and quickly construct three-dimensional positions through image matching, bundle adjustment, and the determination of a scale factor. Through the experiment, when bundle adjustment was performed using stereo constraints, the root mean square error was 1.475m, and when absolute orientation was performed in consideration of a scale, it was found to be 0.029m. Through this, it was found that when using the data processing method of a UAV equipped with a stereo camera proposed in this study, high-accuracy 3D spatial information can be constructed without using ground control points.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.5
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• pp.57-67
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• 2019

Water-Birds are the birds that occupy the highest proportion in Korea, inland wetlands and reservoirs provide them with a good environment as habitat, but their habitats have been losing because of thoughtless development. Therefore, artificial plant islands in reservoirs are important for improving habitat environment and providing food resources. However, there are no research and standards on the built and management of artificial plant islands. So this study is to find out the density of bird using artificial plant island as habitat through monitoring using UAV focus on the Cheonho-reservoirs located in Seobuk-gu, Cheonan-si(Middle Chungcheong Province). Further, the correlation analysis with environmental factors was conducted to determine the effect of artificial plant islands as habitats for water-birds. The supervised classification of the three-time images taken by the drone identified 244 white-billed ducks and 46 mandarin ducks. The utilization rate was different for each photographed date, and more individuals were identified in wet artificial plant islands than dry ones. As a result of analyzing the utilization follow environmental factors, the distance from the trail showed a significant correlation, and the other factors did not have a statistically significant effect. This study is the first case of the UAV monitoring method of the water-birds using artificial plant islands in the reservoir, and can be used as the basic data for the built and management.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.236-244
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• 2020

In this research a suspended solid transport experiment was conducted in the river experiment center to find the characteristics and dispersion of the material. Modeling by the particle dispersion model was also executed to reproduce the suspended solid transport. The suspended solid was consisted of a mixture of silica and water using a mixing equipment, which was then introduced into a real-scale flume and measured with the laser-diffraction based particle size analyzer(LISST) to find the concentration of the material. The comparison between the measured suspended solid concentration using drone images and particle size analyzers, with the model showed a good match overall, which proved the applicability of the model. Along with finding the model applicability, the research showed the potential for suspended solid estimation in high flow situations with high rainfall.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.