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Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization
School of Information Science and Engineering, Southeast University, Nanjing 210096, China
* Author to whom correspondence should be addressed.
Received: 10 March 2011; in revised form: 24 April 2011 / Accepted: 26 April 2011 / Published: 2 May 2011
Abstract: This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.
Keywords: artificial neural network; synthetic aperture radar; principle component analysis; particle swarm optimization
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Cite This Article
MDPI and ACS Style
Zhang, Y.; Wu, L. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization. Sensors 2011, 11, 4721-4743.
Zhang Y, Wu L. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization. Sensors. 2011; 11(5):4721-4743.
Zhang, Yudong; Wu, Lenan. 2011. "Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization." Sensors 11, no. 5: 4721-4743.