Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (6): 1165-1179.doi: 10.11947/j.AGCS.2024.20230469
• Smart Surveying and Mapping • Previous Articles Next Articles
Haiyan GU1,2(), Yi YANG1,2, Haitao LI1,2(
), Lijian SUN1,2, Shaopeng DING1,2, Shiqi LIU1,2
Received:
2023-10-11
Published:
2024-07-22
Contact:
Haitao LI
E-mail:guhy@casm.ac.cn;lhtao@casm.ac.cn
About author:
GU Haiyan (1982—), female, PhD, researcher, majors in intelligent interpretation and high-performance computing of remote sensing images. E-mail: guhy@casm.ac.cn
Supported by:
CLC Number:
Haiyan GU, Yi YANG, Haitao LI, Lijian SUN, Shaopeng DING, Shiqi LIU. Dynamic construction of high-resolution remote sensing image sample datasets and intelligent interpretation applications[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1165-1179.
Tab.3
Sample directory relationship"
序号 | 字段名 | 字段简写 | 字段含义 | 填写规则 | 填写方式 | 类型 | 示例 |
---|---|---|---|---|---|---|---|
1 | FID | FID | 序号ID | 自增ID | 自动 | Int | 27 |
2 | TreeID | TreeID | Tree编码 | 样本的Tree编码,采用3位序列编码,同层最大编码不超过1000个节点 | 自动 | String | 003029 |
3 | SamSet Name | YBMC | 样本集名称 | 对应并总结概括样本集中样本内容 | 人工 | String | 内蒙古耕地 |
4 | SampSet Nano | YBJX | 样本集名称简写 | 样本集名称的简写形式 | 人工 | String | 蒙耕地 |
5 | SampSetDate | JCJSJ | 创建时间 | 采集时间 | 自动 | Date | 2022-08-30 |
6 | Creator | JCJR | 创建人 | 操作人员姓名 | 人工 | String | 张三 |
7 | SampBak | JBak | 备注 | 样本集的辅助性说明 | 人工 | String | 该样本集主要针对草原生态监测有关的科研样本 |
8 | SampSet Link | YBJ | 样本集 | 关联样本信息表 | 自动 | String | YB3715_2022 |
Tab.4
Sample information relationship"
序号 | 字段名 | 字段简写 | 字段含义 | 填写规则 | 填写方式 | 类型 | 示例 |
---|---|---|---|---|---|---|---|
1 | FID | FID | 数据记录ID | 自增ID | 自动 | Int | 99 |
2 | SampleID | YBID | 样本ID | 样本唯一标识码:数据源_X_Y | 自动 | Int | GF1293_75 |
3 | Classification scheme | YBCS | 分类体系码 | — | 自动 | Int | 2 |
4 | Classification Code | CC | 分类码 | — | 自动 | Int | |
5 | Longitude | X | 经度 | 度(小数) | 自动 | Double | 118.653 498 7 |
6 | Latitude | Y | 纬度 | 度(小数) | 自动 | Double | 41.349 348 47 |
7 | Elevation | Z | 高程 | 整数(米) | 自动 | Double | 155 |
8 | DataSource | SJY | 数据源 | 数据源信息表中序号 | 自动 | Int | 14 |
9 | SampleWidth | YBKD | 样本宽度 | 样本宽度(像素) | 人工 | Int | 1024 |
10 | Sample Height | YBGD | 样本高度 | 样本高度(像素) | 人工 | Int | 1024 |
11 | SampleDate | CJSJ | 采集时间 | 采集时间 | 自动 | Date | 2021-08-30 |
12 | Operator | CJR | 采集人 | 人名 | 人工 | String | 张某 |
13 | Bak | Bak | 备注 | 辅助性说明 | 人工 | String | 钢架大棚房 |
14 | SampleImage | YBIM | 样本影像 | 大对象 | 自动 | Blob | |
15 | SampleLabel | YBLB | 样本标签 | 大对象 | 自动 | Blob |
[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] | 张继贤, 顾海燕, 杨懿, 等. 高分辨率遥感影像智能解译研究进展与趋势[J]. 遥感学报, 2021, 25(11):2198-2210. |
ZHANG Jixian, GU Haiyan, YANG Yi, et al. Research progress and trend of high-resolution remote sensing imagery intelligent interpretation[J]. National Remote Sensing Bulletin, 2021, 25(11):2198-2210. | |
[4] | 张继贤, 李海涛, 顾海燕, 等. 人机协同的自然资源要素智能提取方法[J]. 测绘学报, 2021, 50(8):1023-1032.DOI:10.11947/j.AGCS.2021.20210102. |
ZHANG Jixian, LI Haitao, GU Haiyan, et al. Study on man-machine collaborative intelligent extraction for natural resource features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1023-1032.DOI:10.11947/j.AGCS.2021.20210102. | |
[5] | 冯权泷, 陈泊安, 李国庆, 等. 遥感影像样本数据集研究综述[J]. 遥感学报, 2022, 26(4):589-605. |
FENG Quanlong, CHEN Boan, LI Guoqing, et al. A review for sample datasets of remote sensing imagery[J]. National Remote Sensing Bulletin, 2022, 26(4):589-605. | |
[6] | 龚健雅, 许越, 胡翔云, 等. 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. DOI:10.11947/j.AGCS.2021.20210085. |
GONG Jianya, XU Yue, HU Xiangyun, et al. 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.DOI:10.11947/j.AGCS.2021.20210085. | |
[7] | MNIH V. Machine learning for aerial image labeling[D]. Toronto: University of Toronto, 2013. |
[8] | LU Xiaoqiang, WANG Binqiang, ZHENG Xiangtao, et al. Exploring models and data for remote sensing image caption generation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4):2183-2195. |
[9] | MAGGIORI E, TARABALKA Y, CHARPIAT G, et al. Can semantic labeling methods generalize to any city? The Inria aerial image labeling benchmark[C]//Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth: IEEE, 2017. |
[10] | JI Shunping, WEI Shiqing, LU Meng. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery dataset[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1):574-586. |
[11] | YANG Yongke, XIAO Pengfeng, FENG Xuezhi, et al. Accuracy assessment of seven global land cover datasets over China[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 125:156-173. |
[12] | FRIEDLM A, SULLA-MENASHE D, TAN Bin, et al. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets[J]. Remote Sensing of Environment, 2010, 114(1):168-182. |
[13] | JOHNSON B, XIE Zhixiao. Classifying a high resolution image of an urban area using super-object information[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 83:40-49. |
[14] | VOLPI M, FERRARI V. Semantic segmentation of urban scenes by learning local class interactions[C]//Proceedings of 2015 IEEE Conferenceon Computer Vision and Pattern Recognition Workshops. Boston: IEEE, 2015. |
[15] | 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. |
[16] | TIAN Shiqi, ZHENG Zhuo, MA Ailong, et al. Hi-UCD: a large-scale dataset for urban semantic change detection in remote sensing imagery[EB/OL]. [2023-10-08]. https://arxiv.org/pdf/2011.03247. |
[17] | CHEN Hao, SHI Zhenwei. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection[J]. Remote Sensing, 2020, 12(10):1662. |
[18] | PETRIŞOR A. Assessment of the green infrastructure of bucharest using corine and urban atlas data[J]. Urbanism, Arhitectura, Constructii, 2015, 6(2):19-24. |
[19] | LONG Yang, XIA Guisong, LI Shengyang, 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. |
[20] | ZUO Yi, LI Lingling, LIU Xu, et al. Robust instance-based semi-supervised learning change detection for remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62:1-15. |
[21] | WANG Lukang, ZHANG Min, SHI Wenzhong. CS-WSCDNet: class activation mapping and segment anything model-based framework for weakly supervised change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61:1-12. |
[22] | 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):157-173. |
[23] | DUTTA A, ZISSERMAN A. The VIA annotation software for images, audio and video[C]//Proceedings of the 27th ACM International Conference on Multimedia. Nice: ACM Press, 2019: 2276-2279. |
[24] | CASTREJON L, KUNDU K, URTASUN R, et al. Annotating object instances with a polygon-RNN[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. |
[25] | ACUNA D, LING Huan, KAR A, et al. Efficient interactive annotation of segmentation datasets with polygon-RNN++[C]//Proceedings of 2018 IEEE/CVF Conferenceon Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 859-868. |
[26] | KIRILLOV A, MINTUN E, RAVI N, et al. Segment anything[EB/OL]. [2023-10-08]. https://arxiv.org/abs/2304.02643. |
[27] | ZOU Xueyan, YANG Jianwei, ZHANG Hao, et al. Segment everything everywhere all at once[EB/OL]. [2023-10-08]. https://arxiv.org/pdf/2304.06718. |
[28] | ZHANG Chaoning, HAN Dongshen, QIAO Yu, et al. Faster segment anything: towards lightweight SAM for mobile applications[EB/OL]. [2023-10-08]. https://arxiv.org/pdf/2306.14289. |
[29] | XIONG Yunyang, VARADARAJAN B, WU Lemeng, et al. EfficientSAM: leveraged masked image pretraining for efficient segment anything[EB/OL]. [2023-10-08]. https://arxiv.org/pdf/2312.00863. |
[30] | SONG Yanfei, PU Bangzheng, WANG Peng, et al. SAM-lightening: alightweight segment anything model with dilated flash attention to achieve 30 times acceleration[EB/OL]. [2023-10-08]. https://arxiv.org/pdf/2403.09195. |
[31] | 蒋正锋, 何韬, 施艳玲, 等. 融合卷积注意力机制与深度残差网络的遥感图像分类[J]. 激光杂志, 2022, 43(4):76-81. |
JIANG Zhengfeng, HE Tao, SHI Yanling, et al. Remote sensing image classification based on convolutional block attention module and deep residual network[J]. Laser Journal, 2022, 43(4):76-81. | |
[32] | 李道纪, 郭海涛, 卢俊, 等. 遥感影像地物分类多注意力融和U型网络法[J]. 测绘学报, 2020, 49(8):1051-1064.DOI:10.11947/j.AGCS.2020.20190407. |
LI Daoji, GUO Haitao, LU Jun, et al. A remote sensing image classification procedure based on multilevel attention fusion U-Net[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(8):1051-1064. DOI:10.11947/j.AGCS.2020.20190407. | |
[33] | ZHENG Xianwei, HUAN Linxi, XIA Guisong, et al. Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170:15-28. |
[34] | 张永生, 张振超, 童晓冲, 等. 地理空间智能研究进展和面临的若干挑战[J]. 测绘学报, 2021, 50(9):1137-1146.DOI:10.11947/j.AGCS.2021.20200420. |
ZHANG Yongsheng, ZHANG Zhenchao, TONG Xiaochong, et al. Progress and challenges of geospatial artificial intelligence[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9):1137-1146.DOI:10.11947/j.AGCS.2021.20200420. | |
[35] | 史文中, 张敏. 人工智能用于遥感目标可靠性识别:总体框架设计、现状分析及展望[J]. 测绘学报, 2021, 50(8):1049-1058.DOI:10.11947/j.AGCS.2021.20210095. |
SHI Wenzhong, ZHANG Min. Artificial intelligence for reliable object recognition from remotely sensed data: overall framework design, review and prospect[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1049-1058.DOI:10.11947/j.AGCS.2021.20210095. | |
[36] | 龚健雅, 张觅, 胡翔云, 等. 智能遥感深度学习框架与模型设计[J]. 测绘学报, 2022, 51(4):475-487.DOI:10.11947/j.AGCS.2022.20220027. |
GONG Jianya, ZHANG Mi, HU Xiangyun, et al. The design of deep learning framework and model for intelligent remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(4):475-487.DOI:10.11947/j.AGCS.2022.20220027. | |
[37] | TAO Cheng, YANG Zhang, JAMES H. Network spacetime AI: concepts, methods and applications[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(3):78-92. |
[38] | GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Journal of Geodesy and Geoinformation Science, 2018, 1(1):1-15. |
[39] | 陈军, 刘万增, 武昊, 等. 智能化测绘的基本问题与发展方向[J]. 测绘学报, 2021, 50(8):995-1005.DOI:10.11947/j.AGCS.2021.20210235. |
CHEN Jun, LIU Wanzeng, WU Hao, et al. Smart surveying and mapping: fundamental issues and research agenda[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):995-1005.DOI:10.11947/j.AGCS.2021.20210235. | |
[40] | 陈军, 艾廷华, 闫利, 等. 智能化测绘的混合计算范式与方法研究[J/OL]. 测绘学报: 1-19 [2024-05-18]. http://kns.cnki.net/kcms/detail/11.2089.P.20240415.1049.002.html. |
CHEN Jun, AI Tinghua, YAN Li, et al. Hybrid computational paradigm and methods for intelligentized surveying and mapping[J/OL]. Acta Geodaetica et Cartographica Sinica: 1-19 [2024-05-18]. http://kns.cnki.net/kcms/detail/11.2089.P.20240415.1049.002.html. | |
[41] | CHEN Jun, LI Zhilin, LI songnian, et al. From digitalized to intelligentized surveying and mapping: fundamental issues and research agenda[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2):148-160. DOI:10.11947/j.JGGS.2022.0213. |
[1] | Jinwei BU, Kegen YU, Qiulan WANG, Linghui LI, Xinyu LIU, Xiaoqing ZUO, Jun CHANG. Deep learning retrieval method for global ocean significant wave height by integrating spaceborne GNSS-R data and multivariable parameters [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(7): 1321-1335. |
[2] | Chao CHEN, Jintao LIANG, Gang YANG, Weiwei SUN, Shaojun GONG, Jianqiang WANG. Remote sensing parameters optimization for accurate land cover classification [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(7): 1401-1416. |
[3] | Liming JIANG, Yi SHAO, Zhiwei ZHOU, Peifeng MA, Teng WANG. A review of intelligent InSAR data processing: recent advancements, challenges and prospects [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1037-1056. |
[4] | Chi GUO, Yang LIU, Yarong LUO, Jingnan LIU, Quan ZHANG. Research progress in the application of image semantic information in visual SLAM [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1057-1076. |
[5] | Xiaogang NING, Hanchao ZHANG, Ruiqian ZHANG. Practical framework and methodology for high-performance intelligent invariant detection in remote sensing imagery [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1098-1112. |
[6] | Xunqiang GONG, Hongyu WANG, Tieding LU, Wei YOU. A general progressive decomposition long-term prediction network model for high-speed railway bridge pier settlement [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1113-1127. |
[7] | Daifeng PENG, Chenchen ZHAI, Dingwei ZHOU, Yongjun ZHANG, Haiyan GUAN, Yufu ZANG. High-resolution optical images change detection based on global information enhancement by pyramid semantic token [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1195-1211. |
[8] | Jicheng WANG, Anmei GUO, Li SHEN, Tian LAN, Zhu XU, Zhilin LI. Multi-level contrastive learning for weakly supervised extraction of urban solid wastes dump from high-resolution remote sensing images [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1212-1223. |
[9] | Shaopeng DING, Xiushan LU, Rufei LIU, Yi YANG, Haiyan GU, Haitao LI. Building change detection method combining object feature guidance and multiple attention mechanism [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1224-1235. |
[10] | Huimin LIU, Chenwei ZHANG, Kaiqi CHEN, Min DENG, Chong PENG. Deep learning-based spatio-temporal prediction and uncertainty assessment of urban PM2.5 distribution [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 750-760. |
[11] | XUE Zhixiang, YU Xuchu, LIU Jingzheng, YANG Guopeng, LIU Bing, YU Anzhu, ZHOU Jianan, JIN Shanghong. A self-supervised pre-training scheme for multi-source heterogeneous remote sensing image land cover classification [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(3): 512-525. |
[12] | SUN Chuanmeng, WEI Yu, LI Xinyu, MA Tiehua, WU Zhibo. Intelligent detection method of image water level inversion for water level without water scale in complex scenes [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(3): 558-568. |
[13] | LIAO Zhaohong, ZHANG Yichen, YANG Biao, LIN Mingchun, SUN Wenbo, GAO Zhi. Monocular height estimation method of remote sensing image based on Swin Transformer-CNN and its application in highway road construction sites [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(2): 344-352. |
[14] | LIN Yunhao, WANG Yanjun, LI Shaochun, CAI Hengfan. A coupled DeepLab and Transformer approach for fine classification of crop cultivation types in remote sensing [J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(2): 353-366. |
[15] | JIANG Baode, HANG Wei, XU Shaofen, WU Yong. Multi-scale building instance refinement extraction from remote sensing images by fusing with decentralized adaptive attention mechanism [J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(9): 1504-1514. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||