遥感影像智能解译样本库现状与研究

doi:10.11947/j.AGCS.2021.20210085

测绘学报 ›› 2021, Vol. 50 ›› Issue (8): 1013-1022.doi: 10.11947/j.AGCS.2021.20210085

遥感影像智能解译样本库现状与研究

龚健雅, 许越, 胡翔云, 姜良存, 张觅

武汉大学遥感信息工程学院, 湖北武汉 430079

收稿日期:2021-02-20 修回日期:2021-06-30 发布日期:2021-08-24
通讯作者: 许越 E-mail:yuexu41@whu.edu.cn
作者简介:龚健雅(1957-),男,博士,教授,中国科学院院士,长期从事地理信息理论和摄影测量与遥感基础研究。
基金资助:
国家重点研发计划（2016YFB0501403）；国家自然科学基金重大研究计划（92038301）

Status analysis and research of sample database for intelligent interpretation of remote sensing image

GONG Jianya, XU Yue, HU Xiangyun, JIANG Liangcun, ZHANG Mi

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Received:2021-02-20 Revised:2021-06-30 Published:2021-08-24
Supported by:
The National Key Research and Development Program of China (No. 2016YFB0501403);Major Program of the National Natural Science Foundation of China (No. 92038301)

摘要/Abstract

摘要： 我国遥感对地观测等项目顺利实施，获取了大量时效性强、覆盖范围广、信息量丰富的遥感数据。但遥感影像智能化自动处理技术发展仍相对滞后，无法满足区域/全球大范围地物信息快速提取的需求。近年来，人们利用深度学习技术显著提高了影像特征提取成效，但由于所使用的深度学习样本数量和类型有限，对于多源遥感影像的自动解译能力仍然不足。本文面向大范围多源遥感影像地物信息智能解译需求，在分析现有样本集现状及问题的基础上，研究提出遥感影像智能解译样本库设计方案，并在此基础上设计了基于互联网的样本协同采集与共享服务框架。本文将为多源遥感影像样本库建设提供参考，为大范围遥感影像智能解译提供支持。

关键词: 遥感智能解译, 样本库, 多源遥感影像, 数据模型, 深度学习

Abstract: The rapid development of earth observation projects in China has obtained a large volume of multi-source (multi-type sensors, multi-temporal, multi-scale) remote sensing data. But the capability of intelligent remote sensing image processing lags behind data acquisition. In recent years, people have significantly improved the effectiveness of image feature extraction with deep learning networks. But limited number and variety of sample data is not enough for processing the multi-source remote sensing images. This paper analyzed existing sample datasets and proposed a method for constructing a sample database for intelligent remote sensing image interpretation, including the data model, classification system, data organization, as well as the Internet-based platform for collaborative sample collection and sharing.

Key words: remote sensing intelligent interpretation, sample database, multi-source remote sensing image, database model, deep learning

中图分类号:

P237

龚健雅, 许越, 胡翔云, 姜良存, 张觅. 遥感影像智能解译样本库现状与研究[J]. 测绘学报, 2021, 50(8): 1013-1022.

GONG Jianya, XU Yue, HU Xiangyun, JIANG Liangcun, ZHANG Mi. Status analysis and research of sample database for intelligent interpretation of remote sensing image[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1013-1022.

参考文献

[1] 李德仁, 童庆禧, 李荣兴, 等. 高分辨率对地观测的若干前沿科学问题[J]. 中国科学:地球科学, 2012, 42(6):805-813. LI Deren, TONG Qingxi, LI Rongxing, et al. Current issues in high-resolution earth observation technology[J]. Scientia Sinica (Terrae), 2012, 42(6):805-813.
[2] 李德仁, 王密, 沈欣, 等. 从对地观测卫星到对地观测脑[J]. 武汉大学学报·信息科学版, 2017, 42(2):143-149. LI Deren, WANG Mi, SHEN Xin, et al. From earth observation satellite to earth observation brain[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2):143-149.
[3] 龚健雅. 人工智能时代测绘遥感技术的发展机遇与挑战[J]. 武汉大学学报(信息科学版), 2018, 43(12):1788-1796. GONG Jianya. Chances and challenges for development of surveying and remote sensing in the age of artificial intelligence[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):1788-1796.
[4] GREKOUSIS G, MOUNTRAKIS G, KAVOURAS M. An overview of 21 global and 43 regional land-cover mapping products[J]. International Journal of Remote Sensing, 2015, 36(21):5309-5335.
[5] JUN Chen, BAN Yifang, LI Songnian. Open access to Earth land-cover map[J]. Nature, 2014, 514(7523):434.
[6] RUSSAKOVSKY O, DENG Jia, SU Hao, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3):211-252.
[7] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers:surpassing human-level performance on ImageNet classification[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). December 7-13, 2015, Santiago, Chile. IEEE, 2015:1026-1034.
[8] RAWAT W, WANG Zenghui. Deep convolutional neural networks for image classification:a comprehensive review[J]. Neural Computation, 2017, 29(9):2352-2449.
[9] ZHANG Liangpei, ZHANG Lefei, DU Bo. Deep learning for remote sensing data:a technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2):22-40.
[10] TONG Xinyi, XIA Guisong, LU Qikai, et al. Land-cover classification with high-resolution remote sensing images using transferable deep models[J]. Remote Sensing of Environment, 2020, 237:111322.
[11] SCHEIRER W J, DE REZENDE ROCHA A, SAPKOTA A, et al. Toward open set recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(7):1757-1772.
[12] 胡翔云, 巩晓雅, 张觅. 变分法遥感影像人工地物自动检测[J]. 测绘学报, 2018, 47(6):780-789. HU Xiangyun, GONG Xiaoya, ZHANG Mi. A variational approach for automatic man-made object detection from remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6):780-789.
[13] CHEN Wuyang, JIANG Ziyu, WANG Zhangyang, et al. Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 15-20, 2019, Long Beach, CA, USA. IEEE, 2019:8916-8925.
[14] ANDERSON J R, HARDY E E, ROAC J T, et al. A land use and land cover classification system for use with remote sensing data[EB/OL]. 1976[LinkOut].
[15] 中华人民共和国自然资源部.基础性地理国情监测内容与指标:CH/T 9029-2019[S].北京:中国标准出版社,2019. Ministry of Natural Resources, People's Republic of China. Basic geographic national conditions monitoring content and indicators:CH/T 9029-2019[S].Beijing:Standards Press of China, 2019.
[16] 中国国家基础地理信息中心. 全球地理信息资源建设与维护更新10米地表覆盖数据生产技术规程:GM QQ 05-2018[S]. 北京:中国国家基础地理信息中心,2020. National Geomatics Center of China. Technical regulations of global geographic information resources construction and 10m surface covering data production maintenance:GM QQ 05-2018[S]. Beijing:National Geomatics Center of China, 2020.
[17] ISO, FAO UN.Geographic information-classification systems-part1:classification system structure:ISO/DIS19144-1[S]. Geneva:International Organization for Standardization, 2009.
[18] ISO, FAO UN.Geographic information-classification systems-part 2:land cover meta language (LCML):ISO/DIS19144-2[S]. Geneva:International Organization for Standardization, 2012.
[19] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. 中华人民共和国推荐性国家标准:地理信息分类系统第1部分:分类系统结构:GB/T 30322.1-2013[S]. 北京:中国标准出版社, 2014. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. National Standard (Recommended) of the People's Republic of China:geographic information-classification systems-part 1:classification system structure:GB/T 30322.1-2013[S]. Beijing:Standards Press of China, 2014.
[20] RUSSELL B C, TORRALBA A, MURPHY K P, et al. LabelMe:a database and web-based tool for image annotation[J]. International Journal of Computer Vision, 2008, 77(1/2/3):157-173.
[21] TORRALBA A, RUSSELL B C, YUEN J. LabelMe:online image annotation and applications[J]. Proceedings of the IEEE, 2010, 98(8):1467-1484.
[22] TZUTALIN. LabelImg[C/OL].[2020-10-15].https://github.com/tzutalin/labelImg.
[23] XU Y, HU X, WEI Y, et al. A machine learning dataset for large-scope high-resolution remote sensing image interpretation considering landscape spatial heterogeneity[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019, XLII-2/W13:731-736.
[24] CHEN Fei, CHEN Jun, WU Hao, et al. A landscape shape index-based sampling approach for land cover accuracy assessment[J]. Science China Earth Sciences, 2016, 59(12):2263-2274.
[25] 王志华, 孟樊, 杨晓梅, 等. 高空间分辨率遥感影像分割尺度参数自动选择研究[J]. 地球信息科学学报, 2016, 18(5):639-648. WANG Zhihua, MENG Fan, YANG Xiaomei, et al. Study on the automatic selection of segmentation scale parameters for high spatial resolution remote sensing images[J]. Journal of Geo-Information Science, 2016, 18(5):639-648.
[26] ZHU Xiao xiang, TUIA D, MOU Lichao, et al. Deep learning in remote sensing:a comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4):8-36.
[27] LONG Y, XIA G S, LI S, et al. On creating benchmark dataset for aerial image interpretation:reviews, guidances and million-AID[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:4205-4230.

遥感影像智能解译样本库现状与研究

Status analysis and research of sample database for intelligent interpretation of remote sensing image

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