Joint classification of panchromatic and multispectral images by multiresolution fusion through Markov random fields and graph cuts

The problem of the supervised classification of multiresolution images, composed of a higher-resolution panchromatic channel and of several coarser-resolution multispectral channels, is addressed in this paper by proposing a novel contextual method based on Markov random fields. The method iteratively exploits a linear mixture model for the relationships between data at different resolutions and a graph-cut approach to Markovian energy minimization to generate a contextual classification map at the highest resolution available in the input data set. The estimation of the parameters of the method is carried out by extending recently proposed techniques based on the expectation-maximization and Ho-Kashyap's algorithms. The method is experimentally validated with semisimulated and real data involving both IKONOS and Landsat-7 ETM+ images and the results are compared with those generated by a previous Bayesian multiresolution classification technique.

[1]  John A. Richards,et al.  Cluster-space representation for hyperspectral data classification , 2002, IEEE Trans. Geosci. Remote. Sens..

[2]  Klaus Steinnocher,et al.  Influence of image fusion approaches on classification accuracy: a case study , 2006 .

[3]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  A. Podaire,et al.  Extracting crop radiometric responses from simulated low and high spatial resolution satellite data using a linear mixing model , 1994 .

[5]  Dieter Oertel,et al.  Unmixing-based multisensor multiresolution image fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[6]  Siamak Khorram,et al.  Per-pixel Classification of High Spatial Resolution Satellite Imagery for Urban Land-cover Mapping , 2008 .

[7]  Stan Z. Li.  Markov Random Field Modeling in Image Analysis , 2009, Advances in Pattern Recognition.

[8]  E. Vivoni,et al.  Riparian vegetation mapping for hydraulic roughness estimation using very high resolution remote sensing data fusion. , 2010 .

[9]  Anil K. Jain,et al.  A Markov random field model for classification of multisource satellite imagery , 1996, IEEE Trans. Geosci. Remote. Sens..

[10]  Palma Blonda,et al.  Automatic Spectral-Rule-Based Preliminary Classification of Radiometrically Calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 Imagery—Part I: System Design and Implementation , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Lalit Kumar,et al.  Mapping Lantana camara: accuracy comparison of various fusion techniques. , 2010 .

[12]  Anne H. Schistad Solberg,et al.  A bayesian approach to classification of multiresolution remote sensing data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[13]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[14]  Sergio Teggi,et al.  TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm , 2003 .

[15]  Palma Blonda,et al.  Automatic Spectral Rule-Based Preliminary Classification of Radiometrically Calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 Imagery—Part II: Classification Accuracy Assessment , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[16]  J. C. Price,et al.  Combining multispectral data of differing spatial resolution , 1999, IEEE Trans. Geosci. Remote. Sens..

[17]  Luciano Alparone,et al.  MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery , 2006 .

[18]  D. Greig,et al.  Exact Maximum A Posteriori Estimation for Binary Images , 1989 .

[19]  Dongmei Chen,et al.  Strategies for Integrating Information from Multiple Spatial Resolutions into Land-Use/Land-Cover Classification Routines , 2003 .

[20]  Gabriele Moser,et al.  Classification of High-Resolution Images Based on MRF Fusion and Multiscale Segmentation , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[21]  K. Nikolakopoulos.  Comparison of Nine Fusion Techniques for Very High Resolution Data , 2008 .

[22]  Gabriele Moser,et al.  Weight Parameter Optimization by the Ho–Kashyap Algorithm in MRF Models for Supervised Image Classification , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Germain Forestier,et al.  Multiresolution Remote Sensing Image Clustering , 2009, IEEE Geoscience and Remote Sensing Letters.