基于改进U-Net的珊瑚礁底栖物质信息提取方法

doi:10.19678/j.issn.1000-3428.0066091

计算机工程 ›› 2023, Vol. 49 ›› Issue (12): 231-242. doi: 10.19678/j.issn.1000-3428.0066091

基于改进U-Net的珊瑚礁底栖物质信息提取方法

傅杨淦¹^,², 朱岚巍¹^,²^,³^,⁴^,*, 吴虹蓉²^,⁵, 陈方¹^,²^,³^,⁴

1. 桂林电子科技大学计算机与信息安全学院, 广西桂林 541000
2. 海南空天信息研究院海南省地球观测重点实验室, 海南三亚 572000
3. 可持续发展大数据国际研究中心, 北京 100094
4. 中国科学院空天信息创新研究院数字地球重点实验室, 北京 100094
5. 长江大学地球科学学院, 武汉 430000

收稿日期:2022-10-24 出版日期:2023-12-15 发布日期:2023-02-08
通讯作者: 朱岚巍
作者简介:
傅杨淦（1997—），男，硕士，主研方向为深度学习、遥感图像处理
吴虹蓉，硕士
陈方，研究员、博士
基金资助:
海南省重点研发计划(ZDYF2020030); 广西创新驱动发展专项“中国-东盟地球大数据平台与应用示范”子课题(桂科AA20302022-3-2); 海南省重大科技计划项目(ZDKJ2019006)

Coral Reef Benthic Material Information Extraction Method Based on Improved U-Net

Yanggan FU¹^,², Lanwei ZHU¹^,²^,³^,⁴^,*, Hongrong WU²^,⁵, Fang CHEN¹^,²^,³^,⁴

1. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541000, Guangxi, China
2. Hainan Key Laboratory of Earth Observation, Hainan Aerospace Information Research Institute, Sanya 572000, Hainan, China
3. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
4. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
5. School of Earth Sciences, Yangtze University, Wuhan 430000, China

Received:2022-10-24 Online:2023-12-15 Published:2023-02-08
Contact: Lanwei ZHU

摘要/Abstract

摘要：

珊瑚礁底栖物质信息提取在珊瑚礁遥感监测领域具有重要意义。SVM、最大似然法等传统珊瑚礁底栖物质信息提取方法存在精度不高、不够自动化、时间成本较高等问题。目前深度学习方法在语义分割领域已有广泛应用，且取得了较好的效果，为此，利用深度学习技术设计一种基于改进U-Net的分割网络模型，以进行珊瑚礁底栖物质信息提取。为了保留分割细节，对编码器的每个层级设置一种多输入的方式。将ResNet34的残差部分结构作为网络的编码器，以提取更丰富的特征。结合分解卷积、注意力机制和通道混洗操作设计一种新的特征提取块，并将其代替编码器、底层和解码器中的普通卷积层。同时，通过注意力机制来改善U-Net模型的远跳连接，对权重进行调整，以提高分割精度。在三亚地区的GF-2多光谱遥感影像上进行实验，提取的地物类别分别为健康珊瑚礁、白化珊瑚礁、藻类混合物、沙、浪花、深海区和陆地，通过面向对象方法并结合Google Earth影像进行目视解译以修订建立数据集。实验结果表明，该模型的平均交并比和平均F1值分别达到67.17%和78.7%，与常用的分割模型相比，其在视觉效果和评价指标上更优，消融实验结果也验证了改进模块的有效性。

关键词: 珊瑚礁, 遥感影像, 图像分割, 远跳连接, 通道混洗, 注意力机制

Abstract:

The extraction of coral reef benthic material information is of great significance in coral reef remote sensing monitoring. Traditional information extraction methods for benthic organisms on coral reefs, such as Support Vector Machine(SVM) and the maximum likelihood method, have several drawbacks, including poor accuracy, low level of automation, and high time cost. At present, methods based on deep learning are being widely used in image segmentation, and satisfactory results have been obtained. Therefore, a segmentation network model based on improved U-Net and deep learning technology is designed to extract benthic material information from coral reefs. To improve segmentation details, a multi-input mode is set for each level of the encoder. The residual structure of ResNet34 is used as the encoder of the network to extract more abundant features. A new feature extraction block is designed by combining decomposition convolution, attention mechanism, and channel shuffle operation, to replace the common convolution layer in the encoder, bottom layer, and decoder; Concurrently, the attention mechanism is used to improve the far hop connection of the U-Net model, adjust the weight, and improve segmentation accuracy. Based on the GF-2 multispectral remote sensing image of Sanya, the extracted ground objects are classified as healthy coral reef, albino coral reef, algal mixture, sand, spray, deep sea area, and land. The dataset is established through visual interpretation and revision of an object-oriented method combined with Google Earth image. The experimental results show that mean Intersection over Union(mIoU)and average F1 score of the model in this study reached 67.17% and 78.7%, respectively. Compared with commonly used segmentation models, the proposed model performs better in visual effect and evaluation indicators. The ablation experimental results prove the effectiveness of the improved module.

Key words: coral reef, remote sensing image, image segmentation, skip connection, channel shuffle, attention mechanism

傅杨淦, 朱岚巍, 吴虹蓉, 陈方. 基于改进U-Net的珊瑚礁底栖物质信息提取方法[J]. 计算机工程, 2023, 49(12): 231-242.

Yanggan FU, Lanwei ZHU, Hongrong WU, Fang CHEN. Coral Reef Benthic Material Information Extraction Method Based on Improved U-Net[J]. Computer Engineering, 2023, 49(12): 231-242.

http://www.ecice06.com/CN/Y2023/V49/I12/231

图/表 18

图1 一级分割与三级分割对比结果

Fig.1 Comparison results of primary segmentation and tertiary segmentation

图2 2019年GF-2多光谱影像的三级分割结果

Fig.2 Tertiary segmentation results of GF-2 multispectral images in 2019

图3 数据集及对应的标签

Fig.3 Datasets and corresponding labels

图4 改进的U-Net网络模型

Fig.4 Improved U-Net network model

图5 Multiple-input结构

Fig.5 Multiple-input structure

图6 CBAM结构

Fig.6 CBAM structure

图7 FAS块结构

Fig.7 FAS block structure

图8 普通卷积和分解卷积

Fig.8 Ordinary convolution and decomposed convolution

图9 skip-attention结构

Fig.9 skip-attention structure

图10 网络训练过程中的指标变化曲线

Fig.10 Index change curves during network training process

图11 添加不同模块后的分割结果

Fig.11 Segmentation results after adding different modules

图12 使用不同损失函数的分割结果

Fig.12 Segmentation results using different loss functions

图13 不同模型的分割结果对比

Fig.13 Comparison of segmentation results between different models

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