Forest Species Recognition Based on Ensembles of Classifiers

Recognition of forest species is a very challenging task thanks to the great intra-class variability. To cope with such a variability, we propose a multiple classifier system based on a two-level classification strategy and microscopic images. By using a divide-and-conquer approach, an image is first divided into several sub-images which are classified independently by each classifier. In a first fusion level, partial decisions for the sub-images are combined to generate a new partial decision for the original image. Then, the second fusion level combines all these new partial decisions to produce the final classification of the original image. To generate the pool of diverse classifiers, we used classical texture-based features as well as keypoint-based features. A series of experiments shows that the proposed strategy achieves compelling results. Compared to the best single classifier, a Support Vector Machine (SVM) trained with a keypoint based feature set, the divide-and-conquer strategy improves the recognition rate in about 4 and 6 percentage points in the first and second fusion levels, respectively. The best recognition rate achieved by this proposed method is 98.47%.