Improving the impervious surface estimation with combined use of optical and SAR remote sensing images

Abstract Accurate mapping of urban impervious surfaces is important but challenging due to the diversity of urban land covers. This study presents an effort to synergistically combine optical and SAR data to improve the mapping of impervious surfaces. Three pairs of optical and SAR images, Landsat ETM + and ENVISAT ASAR, SPOT-5 and ENVISAR ASAR, and SPOT-5 and TerraSAR-X, were selected in three study areas to validate the effectiveness of the methods in this study. The potential of Random Forest (RF) was evaluated with parameter optimization for combining the optical and SAR images. Experiment results demonstrate some interesting findings. Firstly, the built-in out-of-bag (OOB) error is insufficient for accuracy assessment, and an assessment with additional reference data is required for combining optical and SAR images using RF. Secondly, the optimal number of variables (m) for splitting the decision tree nodes in RF should be some different from the principles reported previously, and an empirical relationship was given for determining the parameter m. Thirdly, the optimal number of decision trees (T) in RF is not sensitive to the resolutions and sensor types of optical and SAR images, and the optimal T in this study is 20. Fourthly, the combined use of optical and SAR images by using RF is effective to improve the land cover classification and impervious surface estimation, by reducing the confusions between bright impervious surface and bare soil and dark impervious surface and bare soil, as well as shaded area and water surface. Even though the easily-confused land classes tend to be different in different resolutions of images, the effectiveness of combining optical and SAR images is consistent. This improvement is more significant when combing lower resolution optical and SAR images. The conclusions of this study could serve as an important reference for further applications of optical and SAR images, and as a potential reference for the applications of RF to the fusion of other multi-source remote sensing data.

[1]  Liming Jiang,et al.  Quantifying Sub-pixel Urban Impervious Surface through Fusion of Optical and InSAR Imagery , 2009 .

[2]  Scott L. Powell,et al.  Effect of Alternative Splitting Rules on Image Processing Using Classification Tree Analysis , 2006 .

[3]  Jinghui Yang,et al.  Block-regression based fusion of optical and SAR imagery for feature enhancement , 2010 .

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

[5]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[6]  Fawwaz T. Ulaby,et al.  SAR speckle reduction using wavelet denoising and Markov random field modeling , 2002, IEEE Trans. Geosci. Remote. Sens..

[7]  J. Dubois,et al.  Evaluation Of The Grey-level Co-occurrence Matrix Method For Land-cover Classification Using Spot Imagery , 1990 .

[8]  Limin Yang,et al.  Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data , 2003 .

[9]  M. Pal,et al.  Random forests for land cover classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[10]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

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

[12]  E. Nezry,et al.  Adaptive speckle filters and scene heterogeneity , 1990 .

[13]  Anne Puissant,et al.  The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery , 2005 .

[14]  Hui Lin,et al.  A comparison study of impervious surfaces estimation using optical and SAR remote sensing images , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[15]  Yunpeng Wang,et al.  Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images , 2008, Environmental monitoring and assessment.

[16]  John Mingers,et al.  An Empirical Comparison of Selection Measures for Decision-Tree Induction , 1989, Machine Learning.

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

[18]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[19]  Min Hu,et al.  Regional Integrated Experiments on Air Quality over Pearl River Delta 2004 (PRIDE-PRD2004): Overview , 2008 .

[20]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[23]  R. Colwell.  Remote sensing of the environment , 1980, Nature.

[24]  Xinwu Li,et al.  Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine , 2011 .

[25]  M. Vastaranta,et al.  Predicting individual tree attributes from airborne laser point clouds based on the random forests technique , 2011 .

[26]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[27]  Björn Waske,et al.  Classifying Multilevel Imagery From SAR and Optical Sensors by Decision Fusion , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Samia Boukir,et al.  Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests , 2011 .

[29]  G. Calabresi.  The Use of ERS SAR for Flood Monitoring: AN Overall Assessment , 1996 .

[30]  Zong-Guo Xia,et al.  SAR applications in human settlement detection, population estimation and urban land use pattern analysis: a status report , 1997, IEEE Trans. Geosci. Remote. Sens..

[31]  Joydeep Ghosh,et al.  Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[32]  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.

[33]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[34]  André Stumpf,et al.  Object-oriented mapping of landslides using Random Forests , 2011 .

[35]  T. Esch,et al.  Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data , 2011 .

[36]  Henri Maître,et al.  A new statistical model for Markovian classification of urban areas in high-resolution SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Liming Jiang,et al.  Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong , 2009 .

[38]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[39]  Paolo Gamba,et al.  Spatial Indexes for the Extraction of Formal and Informal Human Settlements From High-Resolution SAR Images , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  Jong-Sen Lee,et al.  Digital image smoothing and the sigma filter , 1983, Comput. Vis. Graph. Image Process..

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

[42]  D. Lu,et al.  Urban surface biophysical descriptors and land surface temperature variations , 2006 .

[43]  Paolo Gamba,et al.  Texture-based characterization of urban environments on satellite SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..

[44]  Qihao Weng,et al.  Modeling Urban Growth Effects on Surface Runoff with the Integration of Remote Sensing and GIS , 2001, Environmental management.

[45]  U. Soergel.  Radar Remote Sensing of Urban Areas , 2010 .

[46]  M. Bauer,et al.  Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery , 2007 .

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

[48]  Samia Boukir,et al.  A two-pass random forests classification of airborne lidar and image data on urban scenes , 2010, 2010 IEEE International Conference on Image Processing.

[49]  Daniel L. Civco,et al.  TEMPORAL CHARACTERIZATION OF IMPERVIOUS SURFACES FOR THE STATE OF CONNECTICUT , 2002 .

[50]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.