title: Remote-sensing image classification based on an improved probabilistic neural network.
abstract: This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the GLCM-based texture features. Then, a probabilistic neural network (PNN) was adopted for classification, and a novel algorithm proposed to enhance its performance. Principle component analysis (PCA) was chosen to reduce feature dimensions, random division to reduce the number of neurons, and Brent's search (BS) to find the optimal bias values. The results on San Francisco and Flevoland sites are compared to that using a 3-layer BPNN to demonstrate the validity of our algorithm in terms of confusion matrix and overall accuracy. In addition, the importance of each improvement of the algorithm was proven.
authors.affiliation: School of Information Science and Engineering, Southeast University, Nanjing 210009, China; E-Mails:  shuihuaw2007@gmail.com  (S.W.);  wei_geng@163.com  (G.W.).
authors.forename: Yudong
authors.initials: Y
authors.lastname: Zhang
authors.forename: Lenan
authors.initials: L
authors.lastname: Wu
authors.forename: Nabil
authors.initials: N
authors.lastname: Neggaz
authors.forename: Shuihua
authors.initials: S
authors.lastname: Wang
authors.forename: Geng
authors.initials: G
authors.lastname: Wei