Landsat image glacier extraction based on context semantic segmentation network

doi:10.11947/j.AGCS.2020.20190313

Abstract

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

CLC Number:

P237

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.

References

[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.