Effectiveness of boosting algorithms in forest fire classification

In this paper, it is aimed to investigate the capabilities of boosting classification approach for forest fire detection using SPOT-4 imagery. The study area, Bodrum in the province of Muǧla, is located at the south-western Mediterranean coast of Turkey where recent largest forest fires occurred in July 2007. Boosting method is one of the recent advanced classifiers proposed in the machine learning community, such as neural networks classifiers based on multilayer perceptron (MLP), radial basis function and learning vector quantization. The Adaboost (AB) and Logitboost (LB) algorithms which are the most common boosting methods were used for binary and multiclass classifications. The effectiveness of boosting algorithms was shown through comparison with Bayesian maximum likelihood (ML) classifier, neural network classifier based on multilayer perceptron (MLP) and regression tree (RT) classifiers. The pre and post SPOT images were corrected atmospherically and geometrically. Binary classification comprised burnt and non-burnt classes. In addition to the pixel based classification, textural measures including, gray level co-occurrence matrix such as entropy, homogeneity, second angular moment, etc. were also incorporated. Instead of the traditional boosting weak (base) classifiers such as decision tree builder or perceptron learning rule, neural network classifier based on multilayer perceptron were adapted as a weak classifier. The accuracy of the MLP was greater than that of ML, AB, LB and RT both using spectral data alone and textural data. The use of texture measures alone was found to increase classification accuracy of binary and multi-class classifications. The accuracy of the land cover classifications based on either binary or multi-class was maximised using a MLP approach. This was slightly greater than the accuracy achieved using AB and LB classifications. However, it was shown that AB and LB classifications hold great potential as an alternative to conventional techniques.