Selection of pattern classifier in automatic detection for forest fire smoke feature.

The performance of pattern recognition methods for forest fire smoke feature extracted by pulsecoupled neural network (PCNN) was explored in this paper. PCNN smoke feature require high performance to classifiers due to the relevantly high dimension of extracted eigenvectors and the vague nature of smoke. These problems also bring uncertainty to the recognition. Comparative experiments between artificial neural networks (ANNs) and SVM were proposed. Experimental results show that SVM method outperforms other classifiers and the accuracy reaches 94.26% based on our smoke image database.