International Journal of Remote Sensing

Accurately estimating impervious surfaces is of considerable importance towards understanding human impacts on the environment. In this article, a sequential multiprocess model is presented to estimate the distribution of impervious surfaces using Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery. Unlike typical single-thread classification processes, the multiprocess model consists of a series of classification steps. By separating the classification task into several subtasks of variable difficulty, multiple classifiers are integrated while adapting to different levels of complexity. The results show statistically significant improvements for the proposed model. An interesting finding is the uneven distribution of simple classifiers in either highly rural or suburban/urban areas, suggesting that the classification difficulty is concentrated in rural areas. The proposed methodology also supports spatially explicit accuracy metrics, facilitating incorporation of the results obtained in interdisciplinary modelling efforts and providing a guide for further algorithmic refinement.

[1]  G. Mountrakis,et al.  Developing collaborative classifiers using an expert-based model. , 2009 .

[2]  D. Civco,et al.  IMPERVIOUS SURFACE MAPPING FOR THE STATE OF CONNECTICUT 1 , 1997 .

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

[4]  Giorgos Mountrakis.  Next Generation Classifiers : Focusing on Integration Frameworks , 2008 .

[5]  T. Minor,et al.  Detecting and discriminating impervious cover with high-resolution IKONOS data using principal component analysis and morphological operators , 2003 .

[6]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[7]  Nathaniel D. Herold,et al.  MAPPING IMPERVIOUS SURFACES AND FOREST CANOPY USING CLASSIFICATION AND REGRESSION TREE (CART) ANALYSIS , 2003 .

[8]  Sangbum Lee,et al.  Subpixel analysis of Landsat ETM/sup +/ using self-organizing map (SOM) neural networks for urban land cover characterization , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Guobin Zhu,et al.  Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel , 2002 .

[10]  Barry Haack,et al.  Radar and Optical Data Comparison/Integration for Urban Delineation: A Case Study , 2002 .

[11]  D. Roberts,et al.  Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil , 2007 .

[12]  Mike Cranea,et al.  ESTIMATION OF SUB-PIXEL IMPERVIOUS SURFACES USING LANDSAT AND ASTER IMAGERY FOR ASSESSING URBAN GROWTH , 2005 .

[13]  Giorgos Mountrakis,et al.  Integrating intermediate inputs from partially classified images within a hybrid classification framework: An impervious surface estimation example , 2010 .

[14]  Martin Herold,et al.  Mapping imperviousness using NDVI and linear spectral unmixing of ASTER data in the Cologne-Bonn region (Germany) , 2004, SPIE Remote Sensing.

[15]  D. Lu,et al.  Extraction of urban impervious surfaces from an IKONOS image , 2009 .

[16]  Alan T. Murray,et al.  Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques , 2002 .

[17]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

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

[19]  D. Civco,et al.  SUBPIXEL IMPERVIOUS SURFACE MAPPING , 2001 .

[20]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[21]  D. Roberts,et al.  Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments , 2009 .

[22]  Curt H. Davis,et al.  A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[23]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[24]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[25]  C. Arnold,et al.  IMPERVIOUS SURFACE COVERAGE: THE EMERGENCE OF A KEY ENVIRONMENTAL INDICATOR , 1996 .

[26]  Marvin E. Bauer,et al.  ESTIMATION, MAPPING AND CHANGE ANALYIS OF IMPERVIOUS SURFACE AREA BY LANDSAT REMOTE SENSING , 2005 .

[27]  Giorgos Mountrakis,et al.  Enhancing and replacing spectral information with intermediate structural inputs: A case study on impervious surface detection , 2011 .

[28]  Oleksandr Makeyev,et al.  Neural network with ensembles , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[29]  O. Dikshit,et al.  Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh , 2001 .

[30]  Paul F. Hopkins,et al.  Urban cover mapping using digital, high-spatial resolution aerial imagery , 2001, Urban Ecosystems.

[31]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[32]  Shobha V. Iyer,et al.  Urban landuse monitoring using neural network classification , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[33]  Giorgos Mountrakis,et al.  Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification , 2011 .

[34]  R. Forman,et al.  ROADS AND THEIR MAJOR ECOLOGICAL EFFECTS , 1998 .

[35]  Andrew J. Smith,et al.  Subpixel Estimates of Impervious Surface Cover Using Landsat TM Imagery , 2002 .

[36]  T. Schueler.  The importance of imperviousness , 1995 .

[37]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[38]  Sucharita Gopal,et al.  Uncertainty and Confidence in Land Cover Classification Using a Hybrid Classifier Approach , 2004 .

[39]  Xuefei Hu,et al.  Estimating impervious surfaces using linear spectral mixture analysis with multitemporal ASTER images , 2009 .

[40]  Yoshio Yamaguchi,et al.  Polarimetric Features of Oyster Farm Observed by AIRSAR and JERS-1 , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Xiaojun Yang,et al.  Estimating landscape imperviousness index from satellite imagery , 2006, IEEE Geosci. Remote. Sens. Lett..

[42]  Randel L. Dymond,et al.  Evaluation of Impervious Surface Estimates in a Rapidly Urbanizing Watershed , 2004, Photogrammetric Engineering & Remote Sensing.

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

[44]  R. Klein.  URBANIZATION AND STREAM QUALITY IMPAIRMENT , 1979 .

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

[46]  Brian M. Steele,et al.  Combining Multiple Classifiers: An Application Using Spatial and Remotely Sensed Information for Land Cover Type Mapping , 2000 .

[47]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .