An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image

Extraction of the flooded areas from synthetic aperture radar (SAR) and especially TerraSAR-X data is one of the most challenging tasks in the flood management and planning. SAR data due to its high spatial resolution and its capability of all weather conditions makes a proper choice for tropical countries. Texture is considered as an effective factor in distinguishing the classes especially in SAR imagery which records the backscatters that carry information of kind, direction, heterogeneity and relationship of the features. This paper put forward a computer program for texture analysis for high resolution radar data. Texture analysis program is introduced and discussed using the gray-level co-occurrence matrix (GLCM). To demonstrate the ability and correctness of this program, a test subset of TerraSAR-X imagery from Terengganu area, Malaysia was analyzed and pixel-based and object-based classification were attempted. The thematic maps derived by pixel-based method could not achieve acceptable visual interpretation and for that reason no accuracy assessment was performed on them. The overall accuracy achieved by object-based method was 83.63% with kappa coefficient of 0.8. Results on image texture classification showed that the proposed program is capable for texture analysis in TerraSAR-X image and the obtained textural analysis resulted in high classification accuracy. The proposed texture analysis program can be used in many applications such as land use/cover (LULC) mapping, hazard studies and many other applications. A computer program was developed for texture analysis as a tool in ArcMap for high resolution radar data.A test subset of TerraSAR-X imagery was analyzed and a simple pixel-based and object-based classification schemes.The thematic map derived by pixel-based method could not achieve acceptable visual interpretation, while the object-based method produced high accurate results.Image texture classification results showed that the proposed program is capable for texture analysis in TerraSAR-X image and obtained high classification accuracy.

[1]  Biswajeet Pradhan,et al.  Improvement of land-use classification using object-oriented and fuzzy logic approach , 2009 .

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

[3]  G. Hay,et al.  Object-Based Image Analysis , 2008 .

[4]  M. He,et al.  An algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data , 2012 .

[5]  Stefano Tebaldini,et al.  Sentinel-1 Radar Interferometry Applications , 2008 .

[6]  A. R. Mahmud,et al.  An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia , 2012, Environmental Earth Sciences.

[7]  Yunhan Dong,et al.  A review of SAR speckle filters: texture restoration and preservation , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[8]  B. Pradhan.  Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing , 2010 .

[9]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[10]  Myungcheol Lee,et al.  Graph theory for image analysis: an approach based on the shortest spanning tree , 1986 .

[11]  John B. Kyalo Kiema,et al.  Texture analysis and data fusion in the extraction of topographic objects from satellite imagery , 2002 .

[12]  Tae Jin Kang,et al.  Texture classification and segmentation using wavelet packet frame and Gaussian mixture model , 2007, Pattern Recognit..

[13]  Triloki Pant,et al.  Advanced fractal approach for unsupervised classification of SAR images , 2010 .

[14]  Yashon O. Ouma,et al.  Multiscale remote sensing data segmentation and post-segmentation change detection based on logical modeling: Theoretical exposition and experimental results for forestland cover change analysis , 2008, Comput. Geosci..

[15]  Zhuowei Hu,et al.  Texture feature analysis in oil spill monitoring by SAR image , 2012, 2012 20th International Conference on Geoinformatics.

[16]  Biswajeet Pradhan,et al.  Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image , 2011 .

[17]  Paolo Gamba,et al.  Discriminating urban environments using multiscale texture and multiple SAR images , 2006 .

[18]  Michael Eineder,et al.  Velocities of Major Outlet Glaciers of the Patagonia Icefield Observed by TerraSAR-X , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[19]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[20]  Joaquim J. Sousa,et al.  The viStaMPS tool for visualization and manipulation of time series interferometric results , 2013, Comput. Geosci..

[21]  George P. Petropoulos,et al.  Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery , 2012, Comput. Geosci..

[22]  Biswajeet Pradhan,et al.  A 100‐year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia , 2011 .

[23]  B. Pradhan.  Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia , 2010 .

[24]  Biswajeet Pradhan,et al.  Advanced differential interferometry synthetic aperture radar techniques for deformation monitoring: a review on sensors and recent research development , 2014 .

[25]  Biswajeet Pradhan,et al.  Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery , 2011 .

[26]  Barry Haack,et al.  Integrating multisensor data and RADAR texture measures for land cover mapping , 2000 .

[27]  Manfred F. Buchroithner,et al.  Application of spaceborne synthetic aperture radar data for extraction of soil moisture and its use in hydrological modelling at Gottleuba Catchment, Saxony, Germany , 2014 .

[28]  Biswajeet Pradhan,et al.  A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery , 2014 .

[29]  Nicholas Kouwen,et al.  Texture processing of synthetic aperture radar data using second-order spatial statistics , 1996 .

[30]  Kevin S. Magee,et al.  Segmentation, object-oriented applications for remote sensing land cover and land use classification , 2011 .

[31]  Mustafa Neamah Jebur,et al.  Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS , 2013 .

[32]  Ute Christina Herzfeld,et al.  A connectionist-geostatistical approach to automated image classification, applied to the analysis of crevasse patterns in surging ice , 2001 .

[33]  Saurabh Prasad,et al.  Application of omni-directional texture analysis to SAR images for levee landslide detection , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[34]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[35]  Manfred F. Buchroithner,et al.  Exploitation of TerraSAR-X Data for Land use/Land Cover Analysis Using Object-Oriented Classification Approach in the African Sahel Area, Sudan , 2013, Journal of the Indian Society of Remote Sensing.

[36]  Helmi Zulhaidi Mohd Shafri,et al.  Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery , 2014 .

[37]  Kurt J. Marfurt,et al.  Seismic texture analysis for reservoir prediction and characterization , 2010 .

[38]  Mario Chica-Olmo,et al.  Computing geostatistical image texture for remotely sensed data classification , 2000 .

[39]  Shuhe Zhao,et al.  A novel classification method based on texture analysis using high-resolution SAR and optical data , 2012, 2012 Second International Workshop on Earth Observation and Remote Sensing Applications.

[40]  B. Pradhan,et al.  Maximum Flood Prone Area Mapping using RADARSAT Images and GIS: Kelantan River Basin , 2009 .

[41]  Marc L. Serre,et al.  Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5 Concentrations across the Continental United States , 2012, Environmental health perspectives.

[42]  A. Vidal,et al.  Change detection of isolated housing using a new hybrid approach based on object classification with optical and TerraSAR-X data , 2011 .

[43]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[44]  Stefan Buckreuss,et al.  The TerraSAR-X Ground Segment , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Biswajeet Pradhan,et al.  Extraction of soil moisture from RADARSAT-1 and its role in the formation of the 6 December 2008 landslide at Bukit Antarabangsa, Kuala Lumpur , 2014, Arabian Journal of Geosciences.

[46]  Pierre Borderies,et al.  Radar and Optical Parallel Modelling of Forest Remote Sensing Data , 2012 .

[47]  Nozomu Iwasaki,et al.  Distribution of some species of harpacticoid copepods in east coast of Peninsular Malaysia , 2006 .

[48]  Manfred F. Buchroithner,et al.  Field-based landcover classification using TerraSAR-X texture analysis , 2011 .

[49]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .