title: Crop classification by forward neural network with adaptive chaotic particle swarm optimization.
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.
authors.affiliation: School of Information Science and Engineering, Southeast University, Nanjing 210096, China. zhangyudongnuaa@gmail.com
authors.forename: Yudong
authors.initials: Y
authors.lastname: Zhang
authors.forename: Lenan
authors.initials: L
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D015374:Biosensing Techniques / Q000379:methods; D018556:Crops, Agricultural / Q000145:classification*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis