• Korean Journal of Remote Sensing
• /
• v.37 no.6_2
• /
• pp.1829-1836
• /
• 2021

Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_2
• /
• pp.1295-1305
• /
• 2021

Satellite-derived ocean color products are required to effectively monitor clear open ocean and coastal water regions for various research fields. For this purpose, accurate correction of atmospheric effect is essential. Currently, the Geostationary Ocean Color Imager (GOCI)-II ground segment uses the reanalysis of meteorological fields such as European Centre for Medium-Range Weather Forecasts (ECMWF) or National Centers for Environmental Prediction (NCEP) to correct gas absorption by water vapor and ozone. In this process, uncertainties may occur due to the low spatiotemporal resolution of the meteorological data. In this study, we develop water vapor absorption correction model for the GK-2 combined GOCI-II atmospheric correction using Advanced Meteorological Imager (AMI) total precipitable water (TPW) information through radiative transfer model simulations. Also, we investigate the impact of the developed model on GOCI products. Overall, the errors with and without water vapor absorption correction in the top-of-atmosphere (TOA) reflectance at 620 nm and 680 nm are only 1.3% and 0.27%, indicating that there is no significant effect by the water vapor absorption model. However, the GK-2A combined water vapor absorption model has the large impacts at the 709 nm channel, as revealing error of 6 to 15% depending on the solar zenith angle and the TPW. We also found more significant impacts of the GK-2 combined water vapor absorption model on Rayleigh-corrected reflectance at all GOCI-II spectral bands. The errors generated from the TOA reflectance is greatly amplified, showing a large error of 1.46~4.98, 7.53~19.53, 0.25~0.64, 14.74~40.5, 8.2~18.56, 5.7~11.9% for from 620 nm to 865 nm, repectively, depending on the SZA. This study emphasizes the water vapor correction model can affect the accuracy and stability of ocean color products, and implies that the accuracy of GOCI-II ocean color products can be improved through fusion with GK-2A/AMI.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1463-1478
• /
• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.