A5088281627- Remote Sensing in Agriculture
- Remote Sensing and Land Use
- Remote Sensing and LiDAR Applications
- Calibration and Measurement Techniques
- Infrared Target Detection Methodologies
- Atmospheric Ozone and Climate
- Smart Agriculture and AI
- 3D Surveying and Cultural Heritage
- Satellite Image Processing and Photogrammetry
- Land Use and Ecosystem Services
- Spectroscopy and Chemometric Analyses
- Agriculture, Soil, Plant Science
- Soil and Land Suitability Analysis
- Advanced Image Fusion Techniques
- Advanced Measurement and Detection Methods
- Urban Heat Island Mitigation
- Remote-Sensing Image Classification
- Geochemistry and Geologic Mapping
- Coastal and Marine Dynamics
- Ecology and Conservation Studies
- Geophysics and Gravity Measurements
- Diverse Topics in Contemporary Research
- GNSS positioning and interference
- Atmospheric aerosols and clouds
- AI in cancer detection
Rural Development Administration
2018-2025
Pukyong National University
2011-2020
National Institute of Agricultural Science and Technology
2018-2020
When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading poor classification accuracy. In particular, limited input the main obstacles obtain reliable results early mapping. This study investigates potential of a hybrid approach, i.e., CNN-random forest (CNN-RF), in...
In this manuscript, a new methodology based on deep learning model using Siamese network and attention module was proposed to classify crop cultivation areas, such as onion garlic, from multitemporal PlanetScope images in South Korea. To consider the seasonal characteristics of crops model, training data were constructed satellite images. It generated imagery January April, corresponding growth period Image patches by considering ratio minimize influence imbalanced process. FC-DenseNet with...
Unmanned aerial vehicle (UAV) imaging provides the ability to obtain high-resolution images at a lower cost than satellite imagery and photography. However, multiple UAV need be mosaicked of large areas, resulting multispectral image mosaics typically contain seam lines. To address this problem, we applied irradiance, vignette, bidirectional reflectance distribution function (BRDF) filters performed field work using DJI Mavic 3 Multispectral (M3M) camera collect data. We installed calibrated...
Understanding cropland utilization is essential for improving agricultural productivity and efficiently managing resources. Analyzing region-specific cropping systems enables the establishment of sustainable policies tailored to environmental conditions. However, conducting field surveys over extensive areas presents significant challenges. Satellite data monitoring provides continuous large-scale information cropland. The purpose this study develop a pattern product annual crops using...
As the use of unmanned aerial vehicle (UAV) images rapidly increases so does need for precise radiometric calibration. For UAV images, relative calibration is required in addition to traditional vicarious due small field view. calibration, some UAVs install irradiance sensors, but most do not. without them, an intelligent scheme must be applied. In this study, a method proposed improve quality reflectance map measurements. The method, termed by optimal path (RCOP), uses tie points acquired...
A radiometric characterization of the Korea Multi-Purpose Satellite (KOMPSAT) Series multispectral imagery was performed by Aerospace Research Institute (KARI) and Pukyong National University Remote Sensing Group (PKNU RSG). This paper presents a vicarious calibration KARI PKNU RSG in 2012 2014. Correlations between top-of-atmosphere (TOA) radiances spectral band responses KOMPSAT-3 sensors at Zuunmod, Mongolia Goheung, South Korea, were significant for bands. coefficients all bands...
In order to address issues related agricultural productivity and sustainability caused by climate change, the importance of observation monitoring is increasing.Remote sensing data at field, county, national levels are being collected using various remote platforms.In particular, drone imagery becoming an attractive platform with high resolution compared existing satellite aerial imagery, allowing for desired regions specific times, thus enabling immediate responses environmental...
A smart agricultural system is necessary for monitoring crop growth and stress conditions using near-ground-based remote sensing techniques. Crop can be estimated several standard parameters. However, obtaining timed sequential data observing leaf area index (LAI) challenging, normalized difference vegetation (NDVI) estimation in fields requires the installation of sensors on frames structure that are taller than crop. Canopy light transmittance (CLT) indicates degree decrease amount passing...
Suk Young Hong, Chan-Won Park, Young-Ah Jeon, Shin, Kyung-Do Lee, Jeong-Hui Yu, Ho-Yong Ahn, Jae-Hyun Ryu, Sangil Na, Yi-Hyun Kim, Lak-Yeong Choi,Dasom Hyun-Jin Jung. Korean J. Remote Sens. -0001;0:. https://doi.org/10.7780/kjrs.2024.40.5.2.7
Abstract. UAVs (Unmanned aerial Vehicles) can acquire images easily without large cost. For this reason, use of UAV is spreading to diverse fields such as orthoimages and DEM/DSM production. The spatial resolution usually expressed a GSD (Ground Sampling Distance). from has higher performance than other platforms satellites aircraft because it shoot at low altitude. However, blurring noise may occur on due the weather stability UAV. since cannot sufficiently meet resolving power actual image...
In recent years, Korea has sustained consistent access to remote sensed data by launching Multi-Purpose Satellite-3A (KOMPSAT-3A, K3A)-an updated version of the high-resolution KOMPSAT series. This KOMPSAT-3A required calibration and validation (Cal/Val) before after its launch enable proper functional characterization maintain veracity collected. The Aerospace Research Institute (KARI) executed initial prelaunch in laboratory we performed Cal/Val during Launch Early Operation Phase (LEOP)...
This paper presents a radiometric cross calibration of KOMPSAT-3 AEISS based on Landsat-8 OLI. Cross between the two sensors using simultaneous image pairs, acquired during an underfly event over Libya 4 pseudo invariant site (PICS) site. The spectral profile target comes from near-simultaneous EO-1 Hyperion data these sites for apply Spectral Band Adjustment Factor (SBAF). results indicate that Top Of Atmosphere (TOA) reflectance measurements agree with to within 5% after application SBAF....
1. Campos I., Neale C.M., López M.L., Balbontín C., and Calera A., 2014. Analyzing the effect of shadow on relationship between ground cover vegetation indices by using spectral mixture radiative transfer models. J. Appl. Remote Sens., 8(1), 83562-83562, https://doi.org/10.1117/1.JRS..... CrossRef Google Scholar
Water conditions in soil are measured with moisture sensors such as tensiometer and time-domain reflectometry.  However, installed may not fully represent the entire cultivation area due to factors topography, meteorological conditions, irrigation systems.The purpose this study is identify spatial variations of crop growth using drone images weather data. The drone, equipped multi-spectral, hyper-spectral, infrared cameras, captured images, precipitation information up 3 days later...
Remote sensing technology has significantly enhanced crop growth and disease monitoring.While satellites operate above the clouds, Unmanned Aerial Vehicle (UAV) mostly below making cloud cover a critical factor.This study aims to identify best surface reflectance correction method for UAV images under Sky Clear (SKC) Broken Clouds (BKN) conditions by comparing NoProcess, New Row Gradient (NewROW), Bidirectional Reflectance Distribution Function (BRDF) methods.Data was collected using RedEdge...