陈姣,黄远程,李朋飞

• 1:西安科技大学测绘科学与技术学院

摘要(Abstract)：

针对利用光谱混合分解提取不透水面特征通常受到端元类型和数量的限制,同时植被变化会影响估计精度的问题,该文提出了一种综合季相和植被变化信息的不透水面提取框架。基于混合像元部分分解算法——混合调谐匹配滤波(MTMF),设计了多季相组合MTMF(SCMTMF)特征和多季相叠加MTMF(SSMTMF)两种策略,构造了不透水面的多季相MTMF特征,将不透水特征与多季相植被指数结合利用支持向量机实现对不透水面的精确分类。结果表明,利用多季相特征得到的不透水面提取效果相较于单季相有较明显的改善,该文所提出的策略有利于提高不透水面的估计精度。

关键词(KeyWords)： 不透水面;Landsat OLI影像;多季相影像;支持向量机;混合光谱分解;MTMF

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41807063)

作者(Author): 陈姣,黄远程,李朋飞

DOI: 10.16251/j.cnki.1009-2307.2021.04.014

参考文献(References)：

• [1] 李德仁，罗晖，邵振峰.遥感技术在不透水层提取中的应用与展望[J].武汉大学学报(信息科学版),2016,41(5):569-577,703.(LI Deren,LUO Hui,SHAO Zhenfeng.Review of impervious surface mapping using remote sensing technology and its application[J].Geomatics and Information Science of Wuhan University,2016,41(5):569-577,703.)
• [2] DENG C,ZHU Z.Continuous subpixel monitoring of urban impervious surface using Landsat time series[J].Remote Sensing of Environment,2020,238:110929.
• [3] LIU K,FANG J,ZHAO D,et al.An assessment of urban surface energy fluxes using a sub-pixel remote sensing analysis:a case study in Suzhou,China[J].ISPRS International Journal of Geo-Information,2016,5(2):11.
• [4] CHEN F,WANG K,VAN DE VOORDE T,et al.Mapping urban land cover from high spatial resolution hyperspectral data:an approach based on simultaneously unmixing similar pixels with jointly sparse spectral mixture analysis[J].Remote Sensing of Environment,2017,196:324-342.
• [5] WU C,MURRAY A T.Estimating impervious surface distribution by spectral mixture analysis[J].Remote Sensing of Environment,2003,84(4):493-505.
• [6] PAN Y,ZHANG X,TIAN J,et al.Mapping asphalt pavement aging and condition using multiple endmember spectral mixture analysis in Beijing,China[J].Journal of Applied Remote Sensing,2017,11(1):016003.
• [7] SUN G,CHEN X,REN J,et al.Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping[J].International Journal of Applied Earth Observation and Geoinformation,2017,60:38-48.
• [8] MOHAPATRA R P,WU C.High resolution impervious surface estimation[J].Photogrammetric Engineering & Remote Sensing,2010,76(12):1329-1341.
• [9] WANG J,WU Z,WU C,et al.Improving impervious surface estimation:an integrated method of classification and regression trees(CART) and linear spectral mixture analysis(LSMA) based on error analysis[J].GIScience & Remote Sensing,2018,55(4):583-603.
• [10] CARLSON T N,ARTHUR S T.The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology:a satellite perspective[J].Global and Planetary Change,2000,25(1/2):49-65.
• [11] DENG C,WU C.BCI:a biophysical composition index for remote sensing of urban environments[J].Remote Sensing of Environment,2012,127:247-259.
• [12] SUN G,CHEN X,JIA X,et al.Combinational build-up index(CBI) for effective impervious surface mapping in urban areas[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,9(5):2081-2092.
• [13] TANG F,XU H.Comparison of performances in retrieving impervious surface between hyperspectral(hyperion) and multispectral(TM/ETM+) images[J].Spectroscopy and Spectral Analysis,2014,34(4):1075-1080.
• [14] POWELL R L,ROBERTS D A,DENNISON P E,et al.Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis:Manaus,Brazil[J].Remote Sensing of Environment,2007,106(2):253-267.
• [15] ZHANG L,WENG Q,SHAO Z.An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta,China,from 2000 to 2015[J].Remote Sensing of Environment,2017,201:99-114.
• [16] KUENZER C,BACHMANN M,MUELLER A,et al.Partial unmixing as a tool for single surface class detection and time series analysis[J].International Journal of Remote Sensing,2008,29(11):3233-3255.
• [17] BOARDMAN J W,KRUSE F A.Analysis of imaging spectrometer data using n-dimensional geometry and a mixture-tuned matched filtering approach[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(11):4138-4152.
• [18] HORNER J L,GIANINO P D.Phase-only matched filtering[J].Applied Optics,1984,23(6):812-816.
• [19] GREEN A A,BERMAN M,SWITZER P,et al.A transformation for ordering multispectral data in terms of image quality with implications for noise removal[J].IEEE Transactions on Geoscience and Remote Sensing,1988,26(1):65-74.
• [20] ZHANG H,ZHANG Y,LIN H.Seasonal effects of impervious surface estimation in subtropical monsoon regions[J].International Journal of Digital Earth,2014,7(9):746-760.
• [21] CORTES C,VAPNIK V.Support vector machine[J].Machine Learning,1995,20(3):273-297.
• [22] SHI L,LING F,GE Y,et al.Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model:a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016[J].Remote Sensing,2017,9(11):1148.
• [23] ZHANG H,LIN H,LI Y,et al.Mapping urban impervious surface with dual-polarimetric SAR data:an improved method[J].Landscape and Urban Planning,2016,151:55-63.
• [24] ZHOU T,ZHAO M,SUN C,et al.Exploring the impact of seasonality on urban land-cover mapping using multi-season Sentinel-1A and GF-1 WFV images in a subtropical monsoon-climate region[J].ISPRS International Journal of Geo-Information,2018,7(1):3.
• [25] COULSTON J W,JACOBS D M,KING C R,et al.The influence of multi-season imagery on models of canopy cover[J].Photogrammetric Engineering & Remote Sensing,2013(9):469-477.