Automatic Extraction of Green Tide Using Dual Polarization Chinese GF-3 SAR Images

Yu, Haifei; Wang, Changying; Sui, Yi; Li, Jin‐Hua; Chu, Jialan

doi:10.2112/si102-038.1

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…The speckle noise, generated by the defects of the coherent principle on which SAR imaging is based is inevitable, but it can be suppressed by using the methods, such as wave filtering and wavelet transform [14]. Although we have performed the Refined Lee Filter on SAR data in experiments, there is still some noise that cannot be eliminated.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…The surface of seawater is smooth, which mainly produces surface scattering, indicating that the backscattering ability of seawater is weak. On the contrary, the rough surface of green tide mainly produces volume scattering, which provides it with a strong backscattering ability [14]. The backscattering intensity of green tide is higher than that of seawater, resulting in the fact that green tide is brighter than seawater in SAR images, which makes it possible to extract the green tide from the seawater.…”

Section: A Feature Extractionmentioning

confidence: 99%

“…Cui et al [13] employed the images from ENVISAT-ASAR and HJ-1A/B to monitor the process of green tide outbreak in the Yellow Sea, and found that the average difference between the two kinds of remote sensing images is 15 %. Yu et al [14] presented a green tide automatic detection method based on adaptive thresholds for GF-3 SAR images, and pointed out that HV is more suitable for the extraction of green tide than HH due to the lower noise level of the former. Song et al [15] comprehensively used the markov random field (MRF) and the small univalue segment assimilating nucleus (SUSAN) to extract green tide from RADARSAT-2 images, thereby improving the extraction accuracy by 18% compared with the independent utilization of MRF.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Extraction of Green Tide From GF-3 SAR Images Based on Feature Selection and Deep Learning

Yu¹,

Wang²,

Li³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: A Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Extraction of Green Tide From GF-3 SAR Images Based on Feature Selection and Deep Learning

Yu¹,

Wang²,

Li³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Previous studies have shown that 2015 was the most serious year of the U. prolifera disaster in the study area. After that, in 2016, the coverage area of U. prolifera decreased, but the distribution scope and duration persisted [57][58][59]. Compared with 2016, the daily coverage area of U. prolifera in 2017 showed a significant downward trend.…”

Section: Spatio-temporal Distribution Of U Proliferamentioning

confidence: 97%

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

Zhang

Wei

et al. 2021

Remote Sensing

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An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.

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A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images

Xu,

Zhu,

Liu

et al. 2024

International Journal of Remote Sensing

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Automatic Extraction of Green Tide Using Dual Polarization Chinese GF-3 SAR Images

Cited by 5 publications

References 6 publications

Automatic Extraction of Green Tide From GF-3 SAR Images Based on Feature Selection and Deep Learning

Automatic Extraction of Green Tide From GF-3 SAR Images Based on Feature Selection and Deep Learning

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images

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