Dr. Zhigang Chen Profile

Dr. Zhigang Chen

at East China Normal Univ

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Publications (2)

SPIE Journal Paper | 27 June 2012

Classification of urban vegetation patterns from hyperspectral imagery: hybrid algorithm based on genetic algorithm tuned fuzzy support vector machine

Mandi Zhou, Jiong Shu, Zhigang Chen, Minhe Ji

OE, Vol. 51, Issue 11, 111709, (June 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.11.111709

KEYWORDS: Image classification, Vegetation, Fuzzy logic, Hyperspectral imaging, Genetic algorithms, Remote sensing, Data modeling, Optical engineering, Artificial neural networks, Error analysis

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Proceedings Article | 12 August 2010 Paper

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

Mandi Zhou, Jiong Shu, Zhigang Chen

Proceedings Volume 7809, 78090A (2010) https://doi.org/10.1117/12.860153

KEYWORDS: Image classification, Hyperspectral imaging, Remote sensing, Vegetation, Genetic algorithms, Statistical analysis, Classification systems, Statistical modeling, Data modeling, Roads

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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.

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