Integration of Segmentation Techniques for Classification of Hyperspectral Images

A new spectral-spatial method for 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. The output of these two methods is classified by support vector machines. Experimental results indicate that the integration of the two segmentation methods can overcome the drawbacks of each other and increase the overall accuracy in classification.

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

[2]  Aly A. Farag,et al.  A unified framework for MAP estimation in remote sensing image segmentation , 2005, IEEE Transactions on Geoscience and Remote Sensing.

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

[4]  Micael S. Couceiro,et al.  Analysis and Parameter Adjustment of the RDPSO Towards an Understanding of Robotic Network Dynamic P , 2012 .

[5]  Nuno M. Fonseca Ferreira,et al.  Modeling and control of biologically inspired flying robots , 2012, Robotica.

[6]  Shiyuan Yang,et al.  Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm , 2007, Inf. Process. Lett..

[7]  Julie F. Pallant,et al.  SPSS Survival Manual , 2020 .

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

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

[10]  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.

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

[12]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Raghuveer M. Rao,et al.  Darwinian Particle Swarm Optimization , 2005, IICAI.

[14]  Jon Atli Benediktsson,et al.  A new approach for the morphological segmentation of high-resolution satellite imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[15]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[16]  Jon Atli Benediktsson,et al.  Segmentation and classification of hyperspectral images using watershed transformation , 2010, Pattern Recognit..

[17]  Wolfgang Reinhardt,et al.  Image segmentation for the purpose of object-based classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[18]  J. Tilton,et al.  Analysis of hierarchically related image segmentations , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.