基于U-ConvHDNet模型的戈壁砾幕层提取

doi:10.13474/j.cnki.11-2246.2025.0412

测绘通报 ›› 2025, Vol. 0 ›› Issue (4): 68-74.doi: 10.13474/j.cnki.11-2246.2025.0412

• 学术研究 • 上一篇

基于U-ConvHDNet模型的戈壁砾幕层提取

马于博^1,2, 张爱国³, 王浩宇^1,4, 刘帅琪^1,4, 靳镜宇^1,4, 沈占锋², 李均力^1,4

1. 中国科学院新疆生态与地理研究所干旱区生态安全与可持续发展全国重点实验室, 新疆乌鲁木齐 830011;
2. 中国科学院空天信息创新研究院, 北京 100101;
3. 生态环境部南京环境科学研究所, 江苏南京 210042;
4. 新疆遥感与地理信息系统应用重点实验室, 新疆乌鲁木齐 830011

收稿日期:2024-08-19 发布日期:2025-04-28
通讯作者: 张爱国。E-mail:zhangaiguo@nies.org
作者简介:马于博(2002—),男,博士生,主要研究方向为遥感信息解译。E-mail:yuboo_m@163.com
基金资助:
哈密市戈壁生态调查评价与保护修复(202306160842);新疆天山科技创新团队(2022TSYCTD0006);第三次新疆综合科学考察(2021xjkk1403)

Extraction of gobi desert gravel layer based on U-ConvHDNet

MA Yubo^1,2, ZHANG Aiguo³, WANG Haoyu^1,4, LIU Shuaiqi^1,4, JIN Jingyu^1,4, SHEN Zhanfeng², LI Junli^1,4

1. Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;
3. Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing 210042, China;
4. Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, China

Received:2024-08-19 Published:2025-04-28

摘要/Abstract

摘要： 砾幕层是戈壁生态系统的重要组成部分,大尺度的砾幕层遥感监测对戈壁生态系统保护具有重要意义。针对砾幕层结构松散、异质性强的特点,本文提出了一种基于U-ConvHDNet语义分割的砾幕层自动信息制图方法,利用2023年8月的哈密全区域的Sentinel-2影像提取戈壁砾幕层信息。结果表明,U-ConvHDNet模型的F1 分数为0.918,优于参与对比的7个主流语义分割模型,消融试验表明骨架网络的改进与上下采样模块的联合使用有效提升了精度。双重感受野滑窗策略优化了拼接线附近不稳定的现象,提取出哈密戈壁砾幕层总面积为1.026×10⁵ km²,其信息提取精度的F1分数为0.921。本文研究可为戈壁砾幕层的监测和戈壁生态系统治理提供技术支撑。

关键词: 遥感影像, 语义分割, 深度学习, 砾幕层

Abstract: The gravel layer is an essential component of the gobi desert ecosystem. Conducting large-scale remote sensing monitoring of the gravel layer is of great significance for protecting the gobi desert ecology. In response to the loose structure and strong heterogeneity of the gravel layer, this paper proposes an automatic information mapping method for the gravel layer based on the U-ConvHDNet semantic segmentation model. This method utilizes Sentinel-2 imagery from the entire Hami region captured in August 2023 to extract information on gobi gravel layer. The results indicate that the F1 score of the U-ConvHDNet model is 0.918, which is superior to that of the other seven semantic segmentation models. Ablation experiments demonstrate that the combined use of the improved backbone network, upsampling and downsampling modules effectively enhances the accuracy. The dual receptive field sliding window strategy optimizes the instability near stitching lines, enabling the extraction of the total area of the gobi gravel layer in Hami at 1.026×10⁵ km², with an information extraction precision of F1 score 0.921. This study provides technical support for monitoring of gobi gravel layer and the management of gobi ecosystems.

Key words: remote sensing imagery, semantic segmentation, deep learning, gravel layer

中图分类号:

马于博, 张爱国, 王浩宇, 刘帅琪, 靳镜宇, 沈占锋, 李均力. 基于U-ConvHDNet模型的戈壁砾幕层提取[J]. 测绘通报, 2025, 0(4): 68-74.

MA Yubo, ZHANG Aiguo, WANG Haoyu, LIU Shuaiqi, JIN Jingyu, SHEN Zhanfeng, LI Junli. Extraction of gobi desert gravel layer based on U-ConvHDNet[J]. Bulletin of Surveying and Mapping, 2025, 0(4): 68-74.

参考文献

[1] YU K F,LEHMKUHL F,SCHLVTZ F,et al.Late quaternary environments in the gobi desert of Mongolia:vegetation,hydrological,and palaeoclimate evolution[J].Palaeogeography,Palaeoclimatology,Palaeoecology,2019,514:77-91.
[2] 王健铭,董芳宇,巴海·那斯拉,等.中国黑戈壁植物多样性分布格局及其影响因素[J].生态学报,2016,36(12):3488-3498.
[3] 王瑾杰,丁建丽,张子鹏.基于遥感生态指数的吐哈地区生态环境变化研究[J].干旱区地理,2022,45(5):1591-1603.
[4] 王金满,白中科,崔艳,等.干旱戈壁荒漠矿区破坏土地生态化复垦模式分析[J].资源与产业,2010,12(2):83-88.
[5] 邢浩,石海涛,娄雪聪.新疆哈密市南湖一带砾幕层自然修复能力及保护修复对策研究[J].新疆地质,2023,41(S1):72.
[6] 李增沛,张伟.戈壁荒漠砾幕层破坏的生态恢复措施[J].绿色科技,2016(2):11-12.
[7] 冯益明,吴波,周娜,等.基于遥感影像识别的戈壁分类体系研究[J].中国沙漠,2013,33(3):635-641.
[8] 张俊杰,白中科,杨博宇.新疆荒漠露天矿区生态受损及砾幕层重构方法研究[J].地学前缘,2021,28(4):142-152.
[9] YAMASHITA R,NISHIO M,DO R K G,et al.Convolutional neural networks:an overview and application in radiology[J].Insights into Imaging,2018,9(4):611-629.
[10] LIU Z,MAO H Z,WU C Y,et al.A ConvNet for the 2020s[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.New Orleans:IEEE,2022:11966-11976.
[11] XU G P,LIAO W T,ZHANG X,et al.Haar wavelet downsampling:a simple but effective downsampling module for semantic segmentation[J].Pattern Recognition,2023,143:109819.
[12] LIU W Z,LU H,FU H T,et al.Learning to upsample by learning to sample[C]//Proceedings of IEEE/CVF International Conference on Computer Vision.Paris:IEEE,2023:6004-6014.
[13] HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:770-778.
[14] LIU Z,LIN Y T,CAO Y,et al.Swin transformer:hierarchical vision transformer using shifted windows[C]//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision.Montreal:IEEE,2021:9992-10002.
[15] LI G,YUN I,KIM J H,et al.DABNet:depth-wise asymmetric bottleneck for real-time semantic segmentation[EB/OL].2019-07-26[2024-07-10].https://arxiv.org/abs/1907.11357.
[16] XIE E Z,WANG W H,YU Z D,et al.SegFormer:simple and efficient design for semantic segmentation with transformers[J].Advances in neural information processing systems,2021,34:12077-12090.
[17] CHEN L C,ZHU Y K,PAPANDREOU G,et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[M]//Lecture Notes in Computer Science.Cham:Springer International Publishing,2018:833-851.
[18] SHELHAMER E,LONG J,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of 2017 IEEE Transactions on Pattern Analysis and Machine Intelligence.[S.l.]:IEEE,2017:640-651.
[19] RONNEBERGER O,FISCHER P,BROX T.U-net:convolutional networks for biomedical image segmentation[M]//NAVAB N,HORNEGGER J,WELLS W M,et al.Lecture Notes in Computer Science.Cham:Springer International Publishing,2015:234-241.
[20] JIN Q G,MENG Z P,PHAM T D,et al.DUNet:a deformable network for retinal vessel segmentation[J].Knowledge-Based Systems,2019,178:149-162.
[21] ZHOU L C,ZHANG C,WU M.D-LinkNet:LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.Salt Lake City:IEEE,2018:192-1924.
[22] 张德海.东天山哈密盆地白垩纪以来构造-气候-地表过程研究[D].武汉:中国地质大学,2024.
[23] 王国灿,申添毅,陈超,等.覆盖区地质调查中的盆山构造地貌关系研究:以东天山-吐哈盆地为例[J].地球科学,2020,45(12):4313-4331.

基于U-ConvHDNet模型的戈壁砾幕层提取

Extraction of gobi desert gravel layer based on U-ConvHDNet

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