Spectral-spatial classification based on integrated segmentation

A new spectral-spatial method for the classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Segmentation and one clustering method, K-means. In parallel, the input data set is classified by Support Vector Machines (SVM). Furthermore, the result of the segmentation and clustering steps are combined with the result of SVM through majority voting within each object. The final classification map is made by using majority voting between three produced classification maps. Experimental results indicate that the proposed method can significantly improve SVM and other studied methods in terms of accuracies.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[3]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[4]  Saldju Tadjudin,et al.  CLASSIFICATION OF HIGH DIMENSIONAL DATA WITH LIMITED TRAINING SAMPLES , 1998 .

[5]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Sebastiano B. Serpico,et al.  Partially supervised classification of remote sensing images using SVM-based probability density estimation , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[7]  Gabriele Moser,et al.  Partially Supervised classification of remote sensing images through SVM-based probability density estimation , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Lorenzo Bruzzone,et al.  Kernel methods for remote sensing data analysis , 2009 .

[9]  Nuno M. Fonseca Ferreira,et al.  Use of Darwinian Particle Swarm Optimization technique for the segmentation of Remote Sensing images , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[10]  Jon Atli Benediktsson,et al.  Extending the fractional order Darwinian particle swarm optimization to segmentation of hyperspectral images , 2012, Remote Sensing.

[11]  Jon Atli Benediktsson,et al.  An efficient method for segmentation of images based on fractional calculus and natural selection , 2012, Expert Syst. Appl..

[12]  Gabriele Moser,et al.  Combining Support Vector Machines and Markov Random Fields in an Integrated Framework for Contextual Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Jon Atli Benediktsson,et al.  Advances in Spectral-Spatial Classification of Hyperspectral Images , 2013, Proceedings of the IEEE.

[14]  Jon Atli Benediktsson,et al.  Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Jon Atli Benediktsson,et al.  Integration of Segmentation Techniques for Classification of Hyperspectral Images , 2014, IEEE Geoscience and Remote Sensing Letters.