Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

Sensors

Yudong Zhang¹ 

[1] School of Information Science and Engineering, Southeast University, Nanjing 210096, China; E-Mail

关键词: artificial neural network;    synthetic aperture radar;    principle component analysis;    particle swarm optimization;   

DOI  :  10.3390/s110504721

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.