Remote-Sensing Image Classification Based on an Improved Probabilistic Neural Network

Sensors

Yudong Zhang¹  Lenan Wu¹  Nabil Neggaz²  Shuihua Wang¹ 

[1] School of Information Science and Engineering, Southeast University, Nanjing 210009, China; E-Mails:;Signal-Image-Parole Laboratory, Department of Computer Science, University of Science and Technology – Oran, Oran, Algeria; E-Mail:

关键词: polarimetric SAR;    Probabilistic neural network;    gray-level co-occurrence matrix;    principle component analysis;    Brent’s Search;   

DOI  :  10.3390/s90907516

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland