Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis

This paper describes the potentialities of data integration of high spatial resolution multispectral (MS) and single-polarization X-band radar for object-based image analysis (OBIA) using already available algorithms and techniques. GeoEye-1 (GE1) MS images (0.5/2.0 m) and COSMO-SkyMed (CSK®) stripmap images (3.0 m) were collected over a complex test site in the Venetian Lagoon, made up of an intricate mixture of settlements, cultivations, channels, roads, and marshes. The validation confirmed that the integration of optical and radar data substantially increased the thematic accuracy [about 20%-30% for overall accuracy (OA) and about 25%-35% for k coefficient] of MS data, and unlike the outcomes of some new researches, also confirmed that, with appropriate preprocessing, traditional OBIA could also be applied to X-band radar data without the need of developing ad hoc algorithms.

[1]  Tao Tang,et al.  A Kernel Clustering Algorithm With Fuzzy Factor: Application to SAR Image Segmentation , 2014, IEEE Geoscience and Remote Sensing Letters.

[2]  William J. Emery,et al.  A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification , 2009 .

[3]  Anil Kumar,et al.  Analysis of Supervised Maximum Likelihood Classification for remote sensing image , 2014, International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014).

[4]  D. Ducrot,et al.  Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery. , 2009, Journal of environmental management.

[5]  A. Cracknell.  Review article Synergy in remote sensing-what's in a pixel? , 1998 .

[6]  Jing Li,et al.  Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas , 2009 .

[7]  Christian Bignami,et al.  Multisensor Satellite Monitoring of the 2011 Puyehue-Cordon Caulle Eruption , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Claudio Prati,et al.  A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Marco Gianinetto,et al.  Monitoring large oil slick dynamics with moderate resolution multispectral satellite data , 2014, Natural Hazards.

[10]  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.

[11]  Fabio Del Frate,et al.  An application of COSMO-Sky Med to coastal erosion studies , 2012 .

[12]  V. Karathanassi,et al.  A comparison study on fusion methods using evaluation indicators , 2007 .

[13]  R. Mathieu,et al.  Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery , 2007 .

[14]  N. Coops,et al.  Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification , 2007 .

[15]  CameronIain.  Image analysis, classification and change detection in remote sensing , 2013 .

[16]  Maoguo Gong,et al.  An efficient automatic SAR image segmentation framework in AIS using kernel clustering index and histogram statistics , 2014, Appl. Soft Comput..

[17]  Heather McNairn,et al.  Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories , 2009 .

[18]  Peter M. Atkinson,et al.  Fine Spatial Resolution Simulated Satellite Sensor Imagery for Land Cover Mapping in the United Kingdom , 1999 .

[19]  Jaap de Gruijter.  Spatial sampling schemes for remote sensing , 1999 .

[20]  L. Dini,et al.  Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques , 2014 .

[21]  Wei Su,et al.  Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery , 2008 .

[22]  L. Ianninia,et al.  Integration of multispectral and C-band SAR data for crop classification , 2013, Remote Sensing.

[23]  M. Canty.  Image Analysis, Classification, and Change Detection in Remote Sensing , 2006 .

[24]  Asmala Ahmad,et al.  Analysis of Maximum Likelihood Classificationon Multispectral Data , 2012 .

[25]  S. Bhaskaran,et al.  Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data , 2010 .

[26]  D. Amarsaikhan,et al.  Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification , 2010 .

[27]  Christopher Potter,et al.  Fusing optical and radar data to estimate sagebrush, herbaceous, and bare ground cover in Yellowstone , 2010 .

[28]  Wei Zhang,et al.  Multiple Classifier System for Remote Sensing Image Classification: A Review , 2012, Sensors.

[29]  Qingquan Li,et al.  A comparative analysis of image fusion methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Francesco Caltagirone,et al.  The COSMO-SkyMed Dual Use Earth Observation Program: Development, Qualification, and Results of the Commissioning of the Overall Constellation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Shuang Wang,et al.  Context-Based Hierarchical Unequal Merging for SAR Image Segmentation , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[32]  D. Smiraglia,et al.  Land cover data from Landsat single-date imagery: an approach integrating pixel-based and object-based classifiers , 2013 .

[33]  Nazzareno Pierdicca,et al.  The Contribution of SIASGE Radar Data Integrated With Optical Images to Support Thematic Mapping at Regional Scale , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Alexis J. Comber,et al.  Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data , 2014 .

[35]  Martin Machala,et al.  Forest Mapping Through Object-based Image Analysis of Multispectral and LiDAR Aerial Data , 2014 .

[36]  Fabio Rocca,et al.  Permanent scatterers in SAR interferometry , 1999, Remote Sensing.

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

[38]  Gabriele Moser,et al.  Supervised Classification of Multisensor and Multiresolution Remote Sensing Images With a Hierarchical Copula-Based Approach , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[39]  C. V. D. Sande,et al.  A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment , 2003 .

[40]  Marco Gianinetto,et al.  High-resolution SAR and high-resolution optical data integration for sub-urban land-cover classification , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[41]  J. A. Tullis,et al.  Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness , 2003 .

[42]  Fabio Del Frate,et al.  Use of Neural Networks for Automatic Classification From High-Resolution Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Claudio Prati,et al.  Shoreline detection: capability of COSMO-SkyMed and high-resolution multispectral images , 2013 .

[44]  Julien Radoux,et al.  Accounting for the area of polygon sampling units for the prediction of primary accuracy assessment indices , 2014 .

[45]  A. Held,et al.  High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing , 2003 .