Remote-Sensing Image Classification Based on an Improved Probabilistic Neural NetworkReportar como inadecuado




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1

School of Information Science and Engineering, Southeast University, Nanjing 210009, China

2

Signal-Image-Parole Laboratory, Department of Computer Science, University of Science and Technology – Oran, Oran, Algeria





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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. View Full-Text

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





Autor: Yudong Zhang 1,* , Lenan Wu 1, Nabil Neggaz 2, Shuihua Wang 1 and Geng Wei 1

Fuente: http://mdpi.com/



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