Abstract
Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.
Highlights
Tree species classification has an important role in a wide range of applications, from forest management to biodiversity mapping
The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples
We introduce a weighted Support Vector Machine-based approach to tree species classification, at individual tree crown level, using light detection and ranging (LiDAR) data
Summary
Tree species classification has an important role in a wide range of applications, from forest management to biodiversity mapping. Along with the development of remote-sensing technology, the number of studies on tree species classification has increased over the last decades. Airborne hyperspectral data are considered to be the most accurate data sources for classification of tree species [7]. These data have many constraints in the acquisition phase (e.g., the time of the acquisition and the weather are influencing the data acquired), and they need a complex preprocessing when dealing with data over large areas composed by many stripes acquired in different days
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