地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (1): 171-186.doi: 10.12082/dqxxkx.2021.200236

• 遥感科学与应用技术 • 上一篇    

协同多时相波谱特征的不透水面信息级联提取

帅艳民1,2,3(), 马现伟1,*(), 曲歌1, 邵聪颖1, 刘涛2,3, 刘守民4, 黄华兵5, 谷玲霄1, 拉提帕·吐尔汗江2,3, 梁继6, 李玲1   

  1. 1.辽宁工程技术大学测绘与地理科学学院,阜新123000
    2.中国科学院新疆生态与地理研究所 丝路绿色发展研究中心, 乌鲁木齐 830011
    3.中国科学院中亚生态与环境研究中心,乌鲁木齐 830011
    4.山东省菏泽单县科学技术局,菏泽 274300
    5.中山大学测绘科学与技术学院,珠海 519082
    6.湖南科技大学 地理空间信息技术国家地方联合工程实验室,湘潭 411201
  • 收稿日期:2020-05-03 修回日期:2020-07-03 出版日期:2021-01-25 发布日期:2021-03-25
  • 通讯作者: 马现伟
  • 作者简介:帅艳民(1973— ),女,山东菏泽人,博士,教授,主要从事定量遥感、地表覆被动态监测、卫星数据产品研发研究。E-mail: min_shuai@163.com
  • 基金资助:
    辽宁省“兴辽英才计划”创新领军人才-攀登学者项目(XLYC1802027);中国科学院百人计划项目(Y938091);中国科学院百人计划项目(Y674141001);湖南自然科学基金项目(2018JJ2116);国家自然科学基金项目(42071351);国家重点研发项目(2020YFA0608501);辽宁工程技术大学学科创新团队项目(LNTU20TD-23)

Cascade Extraction of Impervious Surface Information based on the Signature of Temporal Spectrum

SHUAI Yanmin1,2,3(), MA Xianwei1,*(), QU Ge1, SHAO Congying1, LIU Tao2,3, LIU Shoumin4, HUANG Huabing5, GU Lingxiao1, LATIPA Tuerhanjiang2,3, LIANG Ji6, LI Ling1   

  1. 1. College of Surveying and Mapping and Geographic Science, Liaoning Technical University, Fuxin 123000, China
    2. Research Center of Green Silk Road, Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences, Urumqi 830011, China
    3. Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
    4. Science and Technology Bureau of Heze Shan County, Heze 274300, China
    5. School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
    6. National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology,Hunan University of Science and Technology, Xiangtan 411201, China
  • Received:2020-05-03 Revised:2020-07-03 Online:2021-01-25 Published:2021-03-25
  • Contact: MA Xianwei
  • Supported by:
    Liaoning Revitalization Talents Program(XLYC1802027);One Hundred Talents Program of the Chinese Academy of Sciences(Y938091);One Hundred Talents Program of the Chinese Academy of Sciences(Y674141001);Hunan Natural Science Foundation Project(2018JJ2116);National Natural Science Foundation of China(42071351);National Key Research & Development Program of China(2020YFA0608501);Project supported discipline innovation team of Liaoning Technical University(LNTU20TD-23)

摘要:

不透水面作为反应城市表征变化和区域城镇化的重要技术指标,其位置、图斑大小、空间分布等信息在地表水热循环和能量平衡等领域被广泛需求。传统方法大都基于单一时相信息提取不透水面,而忽略多时相所蕴含的丰富信息。因此,本文提出多时相信息融合的不透水面级联提取方法,利用Landsat-8 OLI遥感影像分析归一化植被指数(Normalized Difference Vegetation Index, NDVI)、改进的归一化水体指数(Modified Normalized Difference Water Index, MNDWI)和归一化建筑指数(Normalized Difference Building Index, NDBI)年内时序变化特点和典型地物间多时相波谱曲线的协同特征,并归纳不透水面多时相变化规律;再根据先验知识所获取的有效地表信息,进行多时相分级提取不透水面信息。此外,基于实地考察数据和同期2 m GF-1遥感影像屏幕数字化生成30 m不透水面图斑,进行精度验证、分析和对比单时相、四季相及多时相3种时序情况下的提取精度。结果表明:单时相提取不透水面总精度最低,四季相提取精度优于单时相,而多时相提取精度最高(精度可达93.66%,Kappa系数为0.81)。本方法在偏远城镇不透水面的有效识别中显露潜在优势,可为不透水面提取方法融合时序波谱特征提供新思路。

关键词: 时序信息, 不透水面, 先验知识, 级联提取, 精度差异, Landsat OLI, 波谱特征, 遥感指数

Abstract:

Impervious surface acts as an important technical indicator of urban characterization and regional urbanization dynamics. Its location, patch size, and spatial distribution are widely used by the communities of surface hydrothermal cycle and energy balance. However, traditional methods usually adopt a single time-phase image to retrieval the impervious surface information without considering the rich information implied in multiple time-phase images. Therefore, this paper presented a method to extract the impervious surface by integrating features in time-series images within a year. We generated the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI) from time-series Landsat-8 OLI surface reflectance images to analyze the intra-annual features of multi-temporal spectrum curves between typical objects and summarize principles in describing changes of impervious surface from multiple time phases. We also combined the prior knowledge of effective impervious surface information to improve the extraction accuracy. Finally, we used field survey data and the 30-m impervious map generated from the 2-m GF-1 image by screen digitalization to validate the accuracy of results using three combinations of images as input (i.e., single time-phase image, four images of seasons, and multi-temporal images). The results show that the impervious surface extracted from the single-phase image had the lowest accuracy, while the extraction accuracy using multi-temporal images was the highest with an overall accuracy of 93.66% and a Kappa coefficient of 0.81. The extraction accuracy using the four images of seasons was in the middle. Furthermore, the presented method showed potential advantages of effectively identifying impervious surfaces in rural areas. Our method provides a new idea for the impervious surface extraction through integrating temporal spectral features of impervious surface.

Key words: time-series features, impervious surface, prior knowledge, cascade retrieval, accuracy comparison, Landsat OLI, spectrum characteristic, remote sensing index