Red tide detection based on high spatial resolution broad band optical satellite data

2022 ◽  
Vol 184 ◽  
pp. 131-147
Author(s):  
Rongjie Liu ◽  
Yanfang Xiao ◽  
Yi Ma ◽  
Tingwei Cui ◽  
Jubai An
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Red Tide ◽  
Broad Band ◽  
Optical Satellite Data

Red Tide Detection Based on High Spatial Resolution Broad Band Satellite Data: A Case Study of GF-1

2019 ◽  
Vol 90 (sp1) ◽  
pp. 120
Author(s):  
Rong-Jie Liu ◽  
Jie Zhang ◽  
Bin-Ge Cui ◽  
Yi Ma ◽  
Ping-Jian Song ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Red Tide ◽  
Broad Band

scholarly journals Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network

2021 ◽  
Vol 14 (1) ◽  
pp. 88
Author(s):  
Xin Zhao ◽  
Rongjie Liu ◽  
Yi Ma ◽  
Yanfang Xiao ◽  
Jing Ding ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Coastal Zone ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Red Tide ◽  
Ocean Color ◽  
Detection Methods ◽  
Red Tides ◽  
Spectral Difference

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

scholarly journals High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4447
Author(s):  
Jisun Shin ◽  
Young-Heon Jo ◽  
Joo-Hyung Ryu ◽  
Boo-Keun Khim ◽  
Soo Mee Kim
Keyword(s):  
Deep Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Red Tide ◽  
Learning Approaches ◽  
Red Tides ◽  
Southern Coast ◽  
Color Sensors ◽  
Deep Learning Model ◽  
Bloom Water

Red tides caused by Margalefidinium polykrikoides occur continuously along the southern coast of Korea, where there are many aquaculture cages, and therefore, prompt monitoring of bloom water is required to prevent considerable damage. Satellite-based ocean-color sensors are widely used for detecting red tide blooms, but their low spatial resolution restricts coastal observations. Contrarily, terrestrial sensors with a high spatial resolution are good candidate sensors, despite the lack of spectral resolution and bands for red tide detection. In this study, we developed a U-Net deep learning model for detecting M. polykrikoides blooms along the southern coast of Korea from PlanetScope imagery with a high spatial resolution of 3 m. The U-Net model was trained with four different datasets that were constructed with randomly or non-randomly chosen patches consisting of different ratios of red tide and non-red tide pixels. The qualitative and quantitative assessments of the conventional red tide index (RTI) and four U-Net models suggest that the U-Net model, which was trained with a dataset of non-randomly chosen patches including non-red tide patches, outperformed RTI in terms of sensitivity, precision, and F-measure level, accounting for an increase of 19.84%, 44.84%, and 28.52%, respectively. The M. polykrikoides map derived from U-Net provides the most reasonable red tide patterns in all water areas. Combining high spatial resolution images and deep learning approaches represents a good solution for the monitoring of red tides over coastal regions.

scholarly journals The star formation histories of two Wolf-Rayet galaxies: NGC 1741 and He2-10. Two butterflies in a collection

1999 ◽  
Vol 193 ◽  
pp. 517-522
Author(s):  
Kelsey E. Johnson
Keyword(s):  
Star Formation ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Dwarf Galaxy ◽  
Broad Band ◽  
Star Clusters ◽  
Environmental Parameters ◽  
Super Star Clusters ◽  
Star Formation Histories ◽  
Compare And Contrast

In an effort to better understand how the properties of star formation in starburst galaxies depend on various environmental parameters, I present a comparison between two well-known WR galaxies: the interacting galaxy system NGC 1741 in the Hickson Compact Group 31, and the dwarf galaxy He2-10. The high spatial resolution of HST has allowed identification of a large number of starburst knots, or ‘super star clusters’ in these WR galaxies. Broad-band photometry and the latest stellar synthesis models are used to estimate the ages and masses of the super star clusters. The properties of the clusters are then used to compare and contrast the overall star-formation histories in the two WR galaxies.

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