基于深度学习的GF-1卫星WFV影像赤潮探测方法

崔宾阁; 杨光; 方喜; 刘荣杰

doi:10.12284/hyxb2023070

基于深度学习的GF-1卫星WFV影像赤潮探测方法

doi: 10.12284/hyxb2023070

崔宾阁^1,,
杨光¹,
方喜¹,
刘荣杰^2, ,

1.
山东科技大学计算机科学与工程学院，山东青岛 266590
2.
自然资源部第一海洋研究所海洋物理与遥感研究室，山东青岛 266061

基金项目: 国家自然科学基金重大项目（61890964）；中韩海洋科学共同研究中心项目（PI-2022-1）。

详细信息

作者简介:
崔宾阁（1979-），男，教授，现从事高光谱遥感、海洋和海岸带遥感监测技术研究。E-mail：cuibinge@sdust.edu.cn

通讯作者:
刘荣杰（1981-），男，副研究员，主要从事自主卫星数据处理、水体光学遥感研究。E-mail: liurj@fio.org.cn

中图分类号: TP751; P714⁺.5
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出版历程
- 收稿日期: 2022-01-30
- 修回日期: 2022-12-05
- 网络出版日期: 2022-12-26
- 刊出日期: 2023-07-01

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

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

摘要

摘要: 赤潮是我国主要的海洋生态灾害，有效监测赤潮的发生和空间分布对于赤潮的防治具有重要意义。传统的赤潮监测以低空间分辨率的水色卫星为主，但是其对于频发的小规模赤潮存在监控盲区。GF-1卫星WFV影像具有空间分辨率高、成像幅宽大和重访周期短等优点，在小规模赤潮监测中表现出较大的潜力。然而，GF-1卫星WFV影像的光谱分辨率较低，波段少，传统面向水色卫星的赤潮探测方法无法应用于GF-1卫星WFV数据。而且赤潮具有形态多变、尺度不一的特点，难以精确提取。基于此，本文提出了一种面向GF-1卫星WFV影像的尺度自适应赤潮探测网络（SARTNet）。该网络采用双层主干结构以融合赤潮水体的形状特征与细节特征，并引入注意力机制挖掘不同尺度赤潮特征之间的相关性，提高网络对复杂分布赤潮的探测能力。实验结果表明，SARTNet赤潮探测精度优于现有方法，F1分数达到0.89以上，对不同尺度的赤潮漏提和误提较少，且受环境因素的影响较小。
- 赤潮探测 /
- GF-1 WFV /
- 深度语义分割 /
- 注意力机制 /
- 多尺度
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

HTML全文

图 1 研究区域

Fig. 1 Study area

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图 2 研究区I赤潮GF-1 WFV影像

Fig. 2 GF-1 WFV images of the location of study area I and red tides

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图 3 研究区II赤潮GF-1 WFV影像

Fig. 3 GF-1 WFV images of the location of study area II and red tides

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图 4 测试图像及其真值图

Fig. 4 Test images and its ground truth map

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图 5 SARTNet总体架构

H和W分别代表特征图的高度和宽度

Fig. 5 SARTNet overall architecture

H and W represent the height and width of feature map, respectively

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图 6 ASE模块结构

Fig. 6 ASE module structure

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图 7 模型损失曲线

Fig. 7 Model loss curve

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图 8 赤潮信息提取的定性结果

黄色矩形框表示明显的漏提或误提区域

Fig. 8 Qualitative results of red tide information extraction

The yellow rectangular box indicates obvious missed extraction or false extraction areas

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图 9 ASE模块输出特征图

Fig. 9 ASE module output feature map

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图 10 多层次主干输出的定性结果

Fig. 10 Qualitative results of multilayer backbone outputs

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图 11 赤潮水体和非赤潮水体的光谱曲线

Fig. 11 Spectral curves of red tide water and normal water

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图 12 输入不同数据的定性结果

Fig. 12 Qualitative results of experiments with different band settings

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图 13 赤潮探测模型适用性分析结果

Fig. 13 Applicability analysis results of red ride detection model

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表 1 GF-1 WFV影像参数

Tab. 1 GF-1 WFV image parameters

参数	多光谱宽幅相机
波长范围	0.45～0.52 μm
	0.52～0.59 μm
	0.63～0.69 μm
	0.77～0.89 μm
空间分辨率	16 m
幅宽	800 km
重访周期	2 d

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表 2 不同方法赤潮信息提取的定量结果

Tab. 2 Quantitative results of red tide information extraction by different methods

	方法	精确率/%	召回率/%	F1分数	参数量/M
研究区I	GF1_RI	67.64	80.77	0.73	−
	U-Net	84.32	91.32	0.87	31.02
	PSPNet	91.94	83.62	0.87	23.60
	DeepLabv3+	88.41	85.41	0.86	12.04
	SARTNet	88.81	90.87	0.89	5.01
研究区II	GF1_RI	79.48	60.40	0.68	−
	U-Net	78.80	80.47	0.79	31.02
	PSPNet	84.01	83.91	0.83	23.60
	DeepLabv3+	87.01	81.82	0.84	12.04
	SARTNet	91.09	87.29	0.89	5.01
注：加粗数据代表同列数据最大值。

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表 3 ASE模块消融实验结果

Tab. 3 ASE module ablation experimental results

ASE	精确率/%	召回率/%	F1分数
无	88.89	86.50	0.87
有	90.01	89.08	0.89

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表 4 不同层次主干对赤潮信息提取的定量结果

Tab. 4 Quantitative results of red tide information extraction at different levels

主干层次	精确率/%	召回率/%	F1分数
SARTNet-1	91.79	85.44	0.88
SARTNet-2	90.01	89.08	0.89
SARTNet-3	88.49	88.50	0.88

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表 5 输入不同数据的定量结果

Tab. 5 Quantitative results of experiments with different band settings

波段设置	精确率/%	召回率/%	F1分数
4波段	88.81	90.87	0.89
4波段+NDVI	87.08	60.45	0.71
4波段+GF1_RI	87.54	91.65	0.89
4波段+GF1_RI+NDVI	87.17	67.91	0.76

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表 6 模型适用性分析实验结果

Tab. 6 Experimental results of model applicability analysis

影像	精确率/%	召回率/%	F1分数
GF-1 WFV3影像	91.37	86.70	0.89
GF-1 WFV4影像	89.16	87.05	0.88

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