Red tide detection using GF-1 WFV image based on deep learning method

Cui Bin’ge; Yang Guang; Fang Xi; Liu Rongjie

doi:10.12284/hyxb2023070

Volume 45 Issue 7

Jul. 2023

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Cui Bin’ge，Yang Guang，Fang Xi, et al. Red tide detection using GF-1 WFV image based on deep learning method[J]. Haiyang Xuebao，2023, 45(7)：147–157 doi: 10.12284/hyxb2023070

Citation:

Cui Bin’ge，Yang Guang，Fang Xi, et al. Red tide detection using GF-1 WFV image based on deep learning method[J]. Haiyang Xuebao，2023, 45(7)：147–157 doi: 10.12284/hyxb2023070

Citation:

Cui Bin’ge，Yang Guang，Fang Xi, et al. Red tide detection using GF-1 WFV image based on deep learning method[J]. Haiyang Xuebao，2023, 45(7)：147–157 doi: 10.12284/hyxb2023070

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Red tide detection using GF-1 WFV image based on deep learning method

doi: 10.12284/hyxb2023070

1.
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2.
Lab of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China

Received Date: 2022-01-30
Rev Recd Date: 2022-12-05

Available Online: 2022-12-26

Publish Date: 2023-07-01

Abstract

Abstract

Red tide is a major marine ecological disaster in China. Effectively monitoring the occurrence and spatial distribution of red tide is of great significance for their prevention and control. Traditional red tide monitoring is mainly conducted by watercolor satellites with low spatial resolution. However, there are monitoring blind areas for frequent small-scale red tides. GF-1 WFV remote sensing images, featuring high spatial resolution and a wide imaging range, can be used to monitor small-scale red tides. However, the traditional method for watercolor satellites cannot be used for GF-1 WFV satellite data as GF-1 WFV remote sensing images are characterized by low spectral resolution and few bands. And it is hard to extract the information about red tide as they differ in both shape and scale. Due to diverse shapes of the red tide distribution, this paper proposes a scale-adaptive red tide detection network (SARTNet) for GF-1 WFV sensing images. This network adopts a two-layer backbone structure to integrate the shape and detail features of red tide and introduces an attention mechanism to model the correlation between features of red tides at different scales, thereby improving its performance in detecting red tides that are complexly distributed. The experimental results show that the red tide detection performance of SARTNet is better than that of the existing methods, with an F1 score above 0.89; and it is less affected by environmental factors, with few missing and misstated pixels for red tide information at different scales.
- red tide detection,
- GF-1 WFV,
- deep semantic segmentation,
- attention mechanism,
- multi-scale

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References(35)

References

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