Landsat影像冰川提取的上下文感知语义分割网络法

doi:10.11947/j.AGCS.2020.20190313

测绘学报 ›› 2020, Vol. 49 ›› Issue (12): 1575-1582.doi: 10.11947/j.AGCS.2020.20190313

Landsat影像冰川提取的上下文感知语义分割网络法

王忠武¹, 王志盼^2,3,4, 尤淑撑¹, 雷帆^3,4, 曹里^3,4, 杨凯钧^3,4

1. 自然资源部国土卫星遥感应用中心, 北京 100048;
2. 邵阳学院城乡建设学院, 湖南邵阳 422000;
3. 湖南省国土资源规划院, 湖南长沙 410007;
4. 国土资源评价与利用湖南省重点实验室, 湖南长沙 410007

收稿日期:2019-07-26 修回日期:2019-12-25 发布日期:2020-12-25
通讯作者: 王志盼 E-mail:wang749195@outlook.com
作者简介:王忠武(1983-),男,博士,副研究员,研究方向为自然资源遥感调查监测等。E-mail:23159140@qq.com
基金资助:
澜沧江-湄公河合作专项基金（澜沧江-湄公河流域水资源分布和土地利用动态遥感监测技术应用示范）

Landsat image glacier extraction based on context semantic segmentation network

WANG Zhongwu¹, WANG Zhipan^2,3,4, YOU Shucheng¹, LEI Fan^3,4, CAO Li^3,4, YANG Kaijun^3,4

1. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China;
2. School of Urban-Rural Development, Shaoyang University, Shaoyang 422000, China;
3. Hunan Provincial Land and Resources Planning Institute, Changsha 410007, China;
4. Hunan Key Laboratory of Land Resources Evaluation and Utilization, Changsha 410007, China

Received:2019-07-26 Revised:2019-12-25 Published:2020-12-25
Supported by:
Special Fund for Lancang-Mekong Cooperation (Application Research of Water Resources and Land Use Monitoring and Evaluation in the Lancang-Mekong River Region)

摘要/Abstract

摘要： 针对冰川提取存在云阴影、山体阴影、结冰湖泊等同物异谱、同谱异物导致难以有效区分的问题，设计了一种用于冰川提取的上下文感知深度学习语义分割网络。首先引入resnet50作为基准编码网络，以实现冰川特征提取的精度和效率平衡，其次针对现有语义分割网络存在上下文信息学习不足方面，设计了包括空洞卷积组块和最大池化组块的上下文信息提取层，以便更好地提取冰川的上下文信息。选择多景样本数据和验证数据的多源遥感影像进行试验，与现有基于特征指数的冰川提取方法、其他深度学习语义分割网络方法进行定性和定量对比，结果表明本文网络方法在结冰湖面等误提取，阴影的漏提取，以及提取结果完整性等方面，具有较好的效果，验证了本文方法的有效性与稳健性。

关键词: 深度学习, 语义分割, 冰川提取

Abstract: According to the glacier characteristics of remote sensing image, a context-aware deep learning semantic segmentation network for glacier extraction is proposed based on the glacier characteristics of remote sensing image. Firstly, resnet50 is introduced as the feature extraction network to achieve the accuracy and efficiency balance of glacier feature extraction. Secondly, the context-information learning of the existing semantic segmentation network is designed. The context information including the dilated-convolutional block and the max-pooled block is designed to better extract the context information of the glacier. Multiple remote sensing trained images and tested images are selected for experiment, which is qualitatively and quantitatively compared with the existing glacier feature index extraction method and other semantic segmentation network methods. The results show that the network method in the frozen lake surface, the leakage of the mountain shadow, cloud shadow and the integrity of the extraction results have a good effect, which verifies the effectiveness and robustness of the proposed method.

Key words: deep learning, semantic segmentation, glacier extraction

中图分类号:

P237

王忠武, 王志盼, 尤淑撑, 雷帆, 曹里, 杨凯钧. Landsat影像冰川提取的上下文感知语义分割网络法[J]. 测绘学报, 2020, 49(12): 1575-1582.

WANG Zhongwu, WANG Zhipan, YOU Shucheng, LEI Fan, CAO Li, YANG Kaijun. Landsat image glacier extraction based on context semantic segmentation network[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(12): 1575-1582.

参考文献

[1] RAUP B, KÄÄB A, KARGEL J S, et al. Remote sensing and GIS Technology in the Global Land Ice Measurements from Space (GLIMS) project[J]. Computers & Geosciences, 2007, 33(1):104-125.
[2] NIE Yong, LIU Qiao, LIU Shiyin. Glacial lake expansion in the central Himalayas by Landsat images, 1990-2010[J]. PLoS One, 2014, 9(3):e92654.
[3] ZEMP M, FREY H, GÄRTNER-ROER I, et al. Historically unprecedented global glacier decline in the early 21st century[J]. Journal of Glaciology, 2015, 61(228):745-762.
[4] SCHAUWECKER S, ROHRER M, ACUÑA D, et al. Climate trends and glacier retreat in the Cordillera Blanca, Peru, revisited[J]. Global and Planetary Change, 2014, 119:85-97.
[5] PAUL F, BOLCH T, KAAB A, et al. The glaciers climate change initiative:methods for creating glacier area, elevation change and velocity products[J]. Remote Sensing of Environment, 2015, 162:408-426.
[6] ZHANG Meng, WANG Xuhong, SHI Chenlie, et al. Automated glacier extraction index by optimization of Red/SWIR and NIR/SWIR ratio index for glacier mapping using Landsat imagery[J]. Water, 2019, 11(6):1223.
[7] PAUL F, WINSVOLD S H, KÄÄB A, et al. Glacier remote sensing using sentinel-2. Part Ⅱ:mapping glacier extents and surface Facies, and comparison to Landsat 8[J]. Remote Sensing, 2016, 8(7):575.
[8] 都伟冰, 李均力, 包安明, 等. 高山冰川多时相多角度遥感信息提取方法[J]. 测绘学报, 2015, 44(1):59-66. DOI:10.11947/j.AGCS.2015.20130514. DU Weibing, LI Junli, BAO Anming, et al. Information extraction method of alpine glaciers with multitemporal and multiangle remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(1):59-66. DOI:10.11947/j.AGCS.2015.20130514.
[9] RASTNER P, BOLCH T, NOTARNICOLA C, et al. A comparison of pixel-and object-based glacier classification with optical satellite images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(3):853-862.
[10] ZHANG Jingxiao, JIA Li, MENENTI M, et al. Glacier facies mapping using a machine-learning algorithm:the Parlung Zangbo Basin case study[J]. Remote Sensing, 2019, 11(4):452.
[11] 吴淼, 韩用顺, 张东水, 等. 表碛覆盖冰川信息提取方法——以波密县为例[J]. 山地学报, 2017, 35(2):238-245. DOI:10.16089/j.cnki.1008-2786.000217. WU Miao, HAN Yongshun, ZHANG Dongshui, et al. Information extraction method of debris-covered glaciers in Bomi Country[J]. Mountain Research, 2017, 35(2):238-245. DOI:10.16089/j.cnki.1008-2786.000217.
[12] JI Qian, YANG Taibao, HE Yi, et al. A simple method to extract glacier length based on digital elevation model and glacier boundaries for simple basin type glacier[J]. Journal of Mountain Science, 2017, 14(9):1776-1790.
[13] 贺浩, 王仕成, 杨东方, 等. 基于Encoder-Decoder网络的遥感影像道路提取方法[J]. 测绘学报, 2019, 48(3):330-338. DOI:10.11947/j.AGCS.2019.20180005. HE Hao, WANG Shicheng, YANG Dongfang, et al. A road extraction method for remote sensing image based on Encoder-Decoder network[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(3):330-338. DOI:10.11947/j.AGCS.2019.20180005.
[14] MOHAJERANI Y, WOOD M, VELICOGNA I, et al. Detection of glacier calving margins with convolutional neural networks:a case study[J]. Remote Sensing, 2019, 11(1):74.
[15] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional Networks for Biomedical Image Segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer. Cham:Springer, 2015:234-241.
[16] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA:IEEE, 2015:3431-3440.
[17] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV:IEEE, 2016:770-778.
[18] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV:IEEE, 2016:2818-2826.
[19] ZHAO Hengshuang, SHI Jianping, QI Xiaojuan, et al. Pyramid scene parsing network[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI:IEEE, 2017:2881-2890.
[20] CHEN L C, ZHU YUKUN, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the 15th European Conference on Computer Vision. Cham:Springer, 2018:833-851.
[21] LI Zhiwei, SHEN Huanfeng, CHENG Qing, et al. Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150:197-212.
[22] LIU Chenxi, CHEN L C, SCHROFF F, et al. Auto-deepLab:hierarchical neural architecture search for semantic image segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach:IEEE, 2019:82-92.

Landsat影像冰川提取的上下文感知语义分割网络法

Landsat image glacier extraction based on context semantic segmentation network

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