Classification of hyperspectral remote sensing image based on genetic algorithm and SVM

Hyperspectral remote sensing data has been widely used in Terrain Classification for its high resolution. The classification of urban vegetation, identified as an indispensable and essential part of urban development system, is now facing a major challenge as different complex land-cover classes having similar spectral signatures. For a better accuracy in classification of urban vegetation, a classifier model was designed in this paper based on genetic algorithm (GA) and support vector machine (SVM) to address the multiclass problem, and tests were made with the classification of PHI hyperspectral remote sensing images acquired in 2003 which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. SVM, based on statistical learning theory and structural risk minimization, is now widely used in classification in many fields such as two-class classification, and also the multi-class classification later due to its superior performance. On the other hand as parameters are very important factors affecting SVM's ability in classification, therefore, how to choose the optimal parameters turned out to be one of the most urgent problems. In this paper, GA was used to acquire the optimal parameters with following 3 steps. Firstly, useful training samples were selected according to the features of hyperspectral images, to build the classifier model by applying radial basis function (RBF) kernel function and decision Directed Acyclic Graph (DAG) strategy. Secondly, GA was introduced to optimize the parameters of SVM classification model based on the gridsearch and Bayesian algorithm. Lastly, the proposed GA-SVM model was tested for results' accuracy comparison with the maximum likelihood estimation and neural network model. Experimental results showed that GA-SVM model performed better classified accuracy, indicating the coupling of GA and SVM model could improve classification accuracy of hyperspectral remote sensing images, especially in vegetation classification.

[1]  Wang Qiang.  DSGF METHOD ON DETECTING AND REMOVING SPECTRAL NOISE OF HYPERSPECTRAL IMAGE , 2006 .

[2]  Xia Li.  Urban Vegetation Stress Level Monitoring Based on Hyperspectral Feature Selection and RBF Neural Network , 2008 .

[3]  Isabelle Guyon,et al.  Comparison of classifier methods: a case study in handwritten digit recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[4]  Pramod K. Varshney,et al.  HYPERSPECTRAL IMAGE CLASSIFICATION USING SUPPORT VECTOR MACHINES: A COMPARISON WITH DECISION TREE AND NEURAL NETWORK CLASSIFIERS , 2005 .

[5]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.

[6]  Yongchao Zhao,et al.  Hyperspectral remote sensing in China , 2001, International Symposium on Multispectral Image Processing and Pattern Recognition.

[7]  Haoran Zhang,et al.  Solving large-scale multiclass learning problems via an efficient support vector classifier , 2006 .

[8]  Roger L. King,et al.  Putting information into the service of decision making: the role of remote sensing analysis , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  John A. Richards,et al.  Analysis of remotely sensed data: the formative decades and the future , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[11]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[12]  Chih-Hung Wu,et al.  A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy , 2007, Expert Syst. Appl..

[13]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[14]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[15]  Paola Sebastiani,et al.  Robust Bayes classifiers , 2001, Artif. Intell..

[16]  J. Anthony Gualtieri,et al.  Support vector machines for hyperspectral remote sensing classification , 1999, Other Conferences.

[17]  Ching Y. Suen,et al.  Automatic model selection for the optimization of SVM kernels , 2005, Pattern Recognit..

[18]  Young-Chan Lee,et al.  Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters , 2005, Expert Syst. Appl..

[19]  R. Green,et al.  Hyperspectral Remote Sensing and Application , 1998 .

[20]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[21]  Juan Julián Merelo Guervós,et al.  Evolving RBF neural networks for time-series forecasting with EvRBF , 2004, Inf. Sci..

[22]  R. Courant,et al.  Methods of Mathematical Physics , 1962 .

[23]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[24]  Robert F. Cromp,et al.  Support Vector Machine Classifiers as Applied to AVIRIS Data , 1999 .