Impervious Surface Information Extraction Based on Hyperspectral Remote Sensing Imagery

The retrieval of impervious surface information is a hot topic in remote sensing. However, researches on impervious surface retrieval from hyperspectral remote sensing imagery are rare. This paper illustrates a case study of information extraction from urban impervious surfaces based on hyperspectral remote sensing imagery that is intended to improve the image spectral resolution of impermeable materials. Fuzhou, Guangzhou, and Hangzhou were selected as test areas and EO-1 Hyperion images were used as data sources. The impervious surface features were retrieved from remote sensing images using linear spectral mixture analysis. A stepwise discriminant analysis was performed to select feature bands for impervious surface retrieval. A standard deviation analysis, correlation analysis, and principal component analysis were then carried out for each of those up to 158 valid Hyperion spectral bands. Eleven feature bands were selected using the stepwise discriminant analysis and a new image called Hyperion’ was formed. The impervious surface was then retrieved from Hyperion’. The results indicate that the extraction accuracy and coverage accuracy are high in all three test areas. Tests of eleven feature band combinations selected in different areas show very good representations of the band combinations in impervious surface retrieval, and can thus be used as optimal band combinations for impervious surface retrieval.

[1]  D. Civco,et al.  A fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape , 2015 .

[2]  Yang Shao,et al.  Evaluation of Topographic Correction on Subpixel Impervious Cover Mapping With CBERS-2B Data , 2015, IEEE Geoscience and Remote Sensing Letters.

[3]  S. Linden,et al.  Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning , 2015 .

[4]  Hanqiu Xu,et al.  [Comparison of performances in retrieving impervious surface between hyperspectral (Hyperion) and multispectral (TM/ETM+) images]. , 2014, Guang pu xue yu guang pu fen xi = Guang pu.

[5]  Hanqiu Xu,et al.  Rule-based impervious surface mapping using high spatial resolution imagery , 2013 .

[6]  C. Deng,et al.  BCI: A biophysical composition index for remote sensing of urban environments , 2012 .

[7]  John B. Vogler,et al.  LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy , 2012 .

[8]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[9]  Dengsheng Lu,et al.  Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier. , 2011, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[10]  Dengsheng Lu,et al.  Impervious surface mapping with Quickbird imagery , 2011, International journal of remote sensing.

[11]  Xuefei Hu,et al.  Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method , 2011 .

[12]  Bunkei Matsushita,et al.  A pre-screened and normalized multiple endmember spectral mixture analysis for mapping impervious surface area in Lake Kasumigaura Basin, Japan , 2010 .

[13]  Hanqiu Xu,et al.  Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI) , 2010 .

[14]  Ya Ma,et al.  Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[15]  F. Canters,et al.  A comparison of two spectral mixture modelling approaches for impervious surface mapping in urban areas , 2009 .

[16]  D. Barrett,et al.  Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. , 2009 .

[17]  Qihao Weng,et al.  Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Qihao Weng,et al.  Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison , 2008 .

[19]  Ruiliang Pu,et al.  Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data , 2008 .

[20]  Scott L. Powell,et al.  Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972–2006 , 2007 .

[21]  S. Durbha,et al.  Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer , 2007 .

[22]  George Xian,et al.  An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data , 2006 .

[23]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[24]  Changshan Wu,et al.  Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery , 2004 .

[25]  M. Ashton,et al.  Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .

[26]  Jürgen Symanzik,et al.  Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta—a satellite perspective , 2003 .

[27]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[28]  Limin Yang,et al.  An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery , 2003 .

[29]  Chein-I Chang,et al.  Linear spectral random mixture analysis for hyperspectral imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[30]  Allan Aasbjerg Nielsen,et al.  Spectral Mixture Analysis: Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data , 2001, International Journal of Computer Vision.

[31]  Toby N. Carlson,et al.  The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective , 2000 .

[32]  J. R. Jensen,et al.  Effectiveness of Subpixel Analysis in Detecting and Quantifying Urban Imperviousness from Landsat Thematic Mapper Imagery , 1999 .

[33]  M. Ridd.  Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[34]  J. Boardman,et al.  Mapping target signatures via partial unmixing of AVIRIS data: in Summaries , 1995 .

[35]  Nicolas Dobigeon,et al.  Linear and nonlinear unmixing in hyperspectral imaging , 2016 .

[36]  Xu Hanqiu,et al.  Remote sensing-based retrieval of ground impervious surfaces , 2016, National Remote Sensing Bulletin.

[37]  Hu Jing-feng,et al.  Estimating Urban Impervious Surface Based on Thermal Infrared Remote Sensing Data and a Spectral Mixture Analysis Model , 2012 .

[38]  W. Fan,et al.  [Review of monitoring soil water content using hyperspectral remote sensing]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[39]  Zhao Xiaofeng,et al.  A Comparative Study of Four Spectral Mixture Analysis Methods for Land Surface Composition in a Hilly Coastal City , 2009 .

[40]  Xu Han-qiu,et al.  A Spectral Mixture Analysis and Mapping of Impervious Surfaces in Built-up Land of Fuzhou City , 2007 .

[41]  Mankato Armstrong Hall.  MAPPING IMPERVIOUS SURFACE AREA USING HIGH RESOLUTION IMAGERY : A COMPARISON OF OBJECT-BASED AND PER PIXEL CLASSIFICATION , 2006 .