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Remote sensing images classification is the key technology for monitoring forest changes. Texture features have been demonstrated to have better effectiveness than spectral features in the improvement of the classification accuracy. The accuracy of extracting texture information by window-based method depends on the choice of the window size. Moreover, the size should ideally match the spatial scale of the object or class under consideration. However, most of the existing texture feature extraction methods are all based on a single window and do not adequately consider the scale of different objects. Our first proposition is to use a composite window for extracting texture features, which is a small window surrounded by a larger window. Our second proposition is to reinforce the performance of the trained ensemble classifier by training it using only the most important features. Considering the advantages of random forest classifier, such as fast training speed and few parameters, these features feed this classifier. Measures of feature importance are estimated along with the growth of the base classifiers, here decision trees. We aim to classify each pixel of the forest images disturbed by hurricanes and fires in three classes, damaged, not damaged, or unknown, as this could be used to compute time-dependent aggregates. In this study, two research areas—Nezer Forest in France and Blue Mountain Forest in Australia—are utilized to validating the effectiveness of the proposed method. Numerical simulations show increased performance and improved monitoring ability of forest disturbance when using these two propositions. When compared with the reference methods, the best increase of the overall accuracy obtained by the proposed algorithm is 4.77% and 2.96% on the Nezer forest data and Blue Mountain forest data, respectively.

A Novel Feature Extension Method for the Forest Disaster Monitoring Using Multispectral Data