A novel stumpage detection method for forest harvesting based on multi-sensor fusion

This paper describes a novel stumpage detection method for forest harvesting based on 2D laser scanner and infrared thermal imager. First, the stumpage information captured by the two sensors is fused via image fusion and laser matching. Then, rich and accurate stumpage features can be extracted from the fused information. Next, an SVM classifier model is constructed by sample training according to the feature data. Finally, in contrast to SVM with default parameters, three different optimization algorithms are proposed to optimize SVM parameters. Based on 400 stumpage samples, the test on the proposed algorithms is conducted. The results show that the SVM with GA has the best detection rate of 96.7 %. Finally, to verify the performance of the method in this paper, some comparative tests were carried out and the experimental results proved the feasibility and accuracy of the proposed method.