Silvi-Net – A dual-CNN approach for combined classification of tree species and standing dead trees from remote sensing data

作者：S. Briechle, Peter Krzystek, G. Vosselman · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2021 · DOI：10.1016/j.jag.2020.102292 · 被引用次数：61 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Forest ecology and management

Forest managers and nature conservationists rely on precise mapping of single trees from remote sensing data for efficient estimation of forest attributes. In recent years, additional quantification of dead wood in particular has garnered interest. However, tree-level approaches utilizing segmented single trees are still limited in accuracy and their application is therefore mostly restricted to research studies. Furthermore, the combined classification of presegmented single trees with respect to tree species and health status is important for practical use but has been insufficiently investigated so far. Therefore, we introduce Silvi-Net, an approach based on convolutional neural networks (CNNs) fusing airborne lidar data and multispectral (MS) images for 3D object classification. First, we segment single 3D trees from the lidar point cloud, render multiple silhouette-like side-view images, and enrich them with calibrated laser echo characteristics. Second, projected outlines of the segmented trees are used to crop and mask the MS orthomosaic and to generate MS image patches for each tree. Third, we independently train two ResNet-18 networks to learn meaningful features from both datasets. This optimization process is based on pretrained CNN weights and recursive retraining of model parameters. Finally, the extracted features are fused for a final classification step based on a standard multi-layer perceptron and majority voting. We analyzed the network’s performance on dat...