A method of detecting sea fogs using CALIOP data and its application to improve MODIS-based sea fog detection

2015 ◽  
Vol 153 ◽  
pp. 88-94 ◽  
Author(s):  
Dong Wu ◽  
Bo Lu ◽  
Tianche Zhang ◽  
Fengqi Yan
Keyword(s):  
Sea Fog ◽  
Sea Fogs ◽  
Fog Detection

scholarly journals Optical Thickness and Effective Radius Retrievals of Low Stratus and Fog from MTSAT Daytime Data as a Prerequisite for Yellow Sea Fog Detection

2015 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Li Yi ◽  
Boris Thies ◽  
Suping Zhang ◽  
Xiaomeng Shi ◽  
Jörg Bendix
Keyword(s):  
Optical Thickness ◽  
Yellow Sea ◽  
Effective Radius ◽  
Sea Fog ◽  
Fog Detection

Evaluation of Sea fog Detection Accuracy Based on Geostationary Satellite Image Using Machine Learning

2020 ◽  
Author(s):  
NaKyeong Kim ◽  
Suho Bak ◽  
Minji Jeong ◽  
Hongjoo Yoon
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Geostationary Satellite ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Sea Fog ◽  
Short Period ◽  
Wide Range ◽  
Fog Detection

<p><span>A sea fog is a fog caused by the cooling of the air near the ocean-atmosphere boundary layer when the warm sea surface air moves to a cold sea level. Sea fog affects a variety of aspects, including maritime and coastal transportation, military activities and fishing activities. In particular, it is important to detect sea fog as they can lead to ship accidents due to reduced visibility. Due to the wide range of sea fog events and the lack of constant occurrence, it is generally detected through satellite remote sensing. Because sea fog travels in a short period of time, it uses geostationary satellites with higher time resolution than polar satellites to detect fog. A method for detecting fog by using the difference between 11 μm channel and 3.7 μm channel was widely used when detecting fog by satellite remote sensing, but this is difficult to distinguish between lower clouds and fog. Traditional algorithms are difficult to find accurate thresholds for fog and cloud. However, machine learning algorithms can be used as a useful tool to determine this. In this study, based on geostationary satellite imaging data, a comparative analysis of sea fog detection accuracy was conducted through various methods of machine learning, such as Random Forest, Multi-Layer Perceptron, and Convolutional Neural Networks.</span></p>

scholarly journals Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations

2020 ◽  
Vol 12 (9) ◽  
pp. 1521
Author(s):  
Han-Sol Ryu ◽  
Sungwook Hong
Keyword(s):  
Near Infrared ◽  
Weather Conditions ◽  
Skill Score ◽  
Probability Of Detection ◽  
The Yellow Sea ◽  
Sea Fog ◽  
Cloudy Weather ◽  
Comparison Results ◽  
Normalized Difference Snow Index ◽  
Fog Detection

Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.

Automatic Sea Fog Detection and Estimation of Visibility Distance on CCTV

2018 ◽  
Vol 85 ◽  
pp. 881-885 ◽  
Author(s):  
Youngjin Choi ◽  
Hyeong-Gu Choe ◽  
Jae Young Choi ◽  
Kyeong Tae Kim ◽  
Jong-Beom Kim ◽  
...  
Keyword(s):  
Detection And Estimation ◽  
Sea Fog ◽  
Visibility Distance ◽  
Fog Detection

scholarly journals Sea fog detection based on unsupervised domain adaptation

2021 ◽  
Author(s):  
Mengqiu Xu ◽  
Ming Wu ◽  
Jun Guo ◽  
Chuang Zhang ◽  
Yubo Wang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Sea Fog ◽  
Fog Detection

scholarly journals A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection

2021 ◽  
Vol 13 (24) ◽  
pp. 5163
Author(s):  
Xiaofei Guo ◽  
Jianhua Wan ◽  
Shanwei Liu ◽  
Mingming Xu ◽  
Hui Sheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Probability Of Detection ◽  
Orthogonal Polarization ◽  
Feature Maps ◽  
Threshold Method ◽  
Ground Sample ◽  
Sea Fog ◽  
Skill Scores ◽  
Fog Detection

Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill scores (HSS) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the CSI of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products.

Automatic nighttime sea fog detection using GOES-16 imagery

2020 ◽  
Vol 238 ◽  
pp. 104712 ◽  
Author(s):  
Meisam Amani ◽  
Sahel Mahdavi ◽  
Terry Bullock ◽  
Steven Beale
Keyword(s):  
Sea Fog ◽  
Fog Detection

scholarly journals SEA FOG DETECTION BASED ON DYNAMIC THRESHOLD ALGORITHM AT DAWN AND DUSK TIME

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 159-163 ◽  
Author(s):  
J. H. Wan ◽  
L. Jiang ◽  
Y. F. Xiao ◽  
H. Sheng
Keyword(s):  
Remote Sensing ◽  
High Frequency ◽  
Spectral Characteristics ◽  
Dynamic Threshold ◽  
Polar Orbit ◽  
Sea Fog ◽  
Threshold Algorithm ◽  
Study Results ◽  
Fog Detection

Abstract. Dawn and dusk time is the high frequency period of sea fog occurrence, which is very important for all-day sea fog remote sensing detection. Most polar orbit satellites are limited by time resolution and transit time, and can not detect sea fog at dawn and dusk. Based on the Himawari-8 geostationary satellite data and the analysis of the spectral characteristics of sea fog at dawn and dusk, this paper determines the variation law of the reflectivity and brightness temperature of sea fog at dawn and dusk, chooses sensitive bands, sets the detection index of sea fog and its corresponding dynamic threshold, and realizes the detection of sea fog at dawn and dusk. The case study results indicate that our dynamic threshold algorithm can effectively detect the sea fog at dawn and dusk.

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