• Journal of Digital Convergence
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• v.12 no.1
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• pp.319-324
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• 2014

In recent years, high-performance flying drones attract attention for many peoples. In particular, the drone equipped with multi-rotor is expanding its range of utilization in video imaging, aerial rescue, logistics, monitoring, measurement, military field, etc. However, the control function of its controller is very simple. In this study, using a G-sensor mounted on a mobile device, implements an enhanced controller to control flying drones through the intuitive gesture of user. The implemented controller improves the gesture recognition performance using a neural network algorithm.

• Journal of Aerospace System Engineering
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• v.11 no.1
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• pp.41-46
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• 2017

A new test bench for small multi-rotor type drones has been developed. Six degrees of freedom (DOF) motion is possible due to a ball bushing, wheels, and rotating plates. An FPGA (field programmable gate array) based controller, that supports realtime parallel processing, is used to measure attitude with an accelerometer and a gyro to adjust motor speed. Several tests were performed to check the operational properties of the test bench and the controller. The results show that this test bench is proper for verifying controllers and the control methods of small multi-rotor drones.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.589-599
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• 2018

The land cover map has been produced by using satellite and aerial images. However, these two images have the limitations in spatial resolution, and it is difficult to acquire images of a area at desired time because of the influence of clouds. In addition, it is costly and time-consuming that mapping land cover map of a small area used by satellite and aerial images. This study used multispectral camera-based drone to acquire multi-temporal images for orthoimages generation. The efficiency of produced land cover map was evaluated using time series analysis. The results indicated that the proposed method can generated RGB orthoimage and multispectral orthoimage with RMSE (Root Mean Square Error) of ${\pm}10mm$, ${\pm}11mm$, ${\pm}26mm$ and ${\pm}28mm$, ${\pm}27mm$, ${\pm}47mm$ on X, Y, H respectively. The accuracy of the pixel-based and object-based land cover map was analyzed and the results showed that the accuracy and Kappa coefficient of object-based classification were higher than that of pixel-based classification, which were 93.75%, 92.42% on July, 92.50%, 91.20% on October, 92.92%, 91.77% on February, respectively. Moreover, the proposed method can accurately capture the quantitative area change of the object. In summary, the suggest study demonstrated the possibility and efficiency of using multispectral camera-based drone in production of land cover map.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.967-978
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• 2023

This study proposed and validated a combined segmentation network (CSN) designed to effectively train on multiple drone image datasets and enhance the accuracy of semantic segmentation. CSN shares the entire encoding domain to accommodate the diversity of three drone datasets, while the decoding domains are trained independently. During training, the segmentation accuracy of CSN was lower compared to U-Net and the pyramid scene parsing network (PSPNet) on single datasets because it considers loss values for all dataset simultaneously. However, when applied to domestic autonomous drone images, CSN demonstrated the ability to classify pixels into appropriate classes without requiring additional training, outperforming PSPNet. This research suggests that CSN can serve as a valuable tool for effectively training on diverse drone image datasets and improving object recognition accuracy in new regions.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1219-1230
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• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1109-1124
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• 2022

This study proposes a marine debris monitoring methods using satellite and drone multispectral images. A multi-layer perceptron (MLP) model was applied to detect marine debris using Sentinel-2 satellite image. And for the detection of marine debris using drone multispectral images, performance evaluation and comparison of U-Net, DeepLabv3+ (ResNet50) and DeepLabv3+ (Inceptionv3) among deep learning models were performed (mIoU 0.68). As a result of marine debris detection using satellite image, the F1-Score was 0.97. Marine debris detection using drone multispectral images was performed on vegetative debris and plastics. As a result of detection, when DeepLabv3+ (Inceptionv3) was used, the most model accuracy, mean intersection over union (mIoU), was 0.68. Vegetative debris showed an F1-Score of 0.93 and IoU of 0.86, while plastics showed low performance with an F1-Score of 0.5 and IoU of 0.33. However, the F1-Score of the spectral index applied to generate plastic mask images was 0.81, which was higher than the plastics detection performance of DeepLabv3+ (Inceptionv3), and it was confirmed that plastics monitoring using the spectral index was possible. The marine debris monitoring technique proposed in this study can be used to establish a plan for marine debris collection and treatment as well as to provide quantitative data on marine debris generation.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.419-430
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• 2021

Remote sensing techniques using drone-based multispectral image were studied for fast and two-dimensional monitoring of algal blooms in the river. Drone is anticipated to be useful for algal bloom monitoring because of easy access to the field, high spatial resolution, and lowering atmospheric light scattering. In addition, application of multispectral sensors could make image processing and analysis procedures simple, fast, and standardized. Spectral indices derived from the active spectrum of photosynthetic pigments in terrestrial plants and phytoplankton were tested for estimating chlorophyll-a concentrations (Chl-a conc.) from drone-based multispectral image. Spectral indices containing the red-edge band showed high relationships with Chl-a conc. and especially, 3-band model (3BM) and normalized difference chlorophyll index (NDCI) were performed well (R²=0.86, RMSE=7.5). NDCI uses just two spectral bands, red and red-edge, and provides normalized values, so that data processing becomes simple and rapid. The 3BM which was tuned for accurate prediction of Chl-a conc. in productive water bodies adopts originally two spectral bands in the red-edge range, 720 and 760 nm, but here, the near-infrared band replaced the longer red-edge band because the multispectral sensor in this study had only one shorter red-edge band. This index is expected to predict more accurately Chl-a conc. using the sensor specialized with the red-edge range.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.841-850
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• 2019

The purpose of this study is to strengthen the capability of rapid mapping for disaster through improving the positioning accuracy of mapping and fusion of multi-sensing point cloud data derived from Unmanned Aerial Vehicles (UAVs) and disaster investigation vehicle. The positioning accuracy was evaluated for two procedures of drone mapping with Agisoft PhotoScan: 1) general geo-referencing by self-calibration, 2) proposed geo-referencing with optimized camera model by using fixed accurate Interior Orientation Parameters (IOPs) derived from indoor camera calibration test and bundle adjustment. The analysis result of positioning accuracy showed that positioning RMS error was improved 2~3 m to 0.11~0.28 m in horizontal and 2.85 m to 0.45 m in vertical accuracy, respectively. In addition, proposed data fusion approach of multi-sensing point cloud with the constraints of the height showed that the point matching error was greatly reduced under about 0.07 m. Accordingly, our proposed data fusion approach will enable us to generate effectively and timelinessly ortho-imagery and high-resolution three dimensional geographic data for national disaster management in the future.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.11
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• pp.949-958
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• 2016

This paper presents the new development result of the multi-band, the multi-mode SDR(Software Defined Radar) platform. The SDR hardware platform is implemented by using the reconfigurable multi-band RF transceiver and antenna modules of S, X, and K-bands, and a programmable signal processing module. The SDR software platform is implemented by using the multi-mode waveform generation of CW, Pulse, FMCW, and LFM Chirp as well as the adaptable algorithm library of signal processing and open API software modules. Through the integrated test of the SDR platform, the operational performance was verified in real-time. Also, through the field-application test, the ground target and air-vehicle drone target were successfully detected and their test results were presented.