Using a VGG-16 Network for Individual Tree Species Detection with an Object-Based Approach

Acquiring information about forest stands such as individual tree species is crucial for monitoring forests. To date, such information is assessed by human interpreters using airborne or an Unmanned Aerial Vehicle (UAV), which is time/cost consuming. The recent advancement in remote sensing image acquisition, such as WorldView-3, has increased the spatial resolution up to 30 cm and spectral resolution up to 16 bands. This advancement has significantly increased the potential for Individual Tree Species Detection (ITSD). In order to use the single source Worldview-3 images, our proposed method first segments the image to delineate trees, and then detects trees using a VGG-16 network. We developed a pipeline for feeding the deep CNN network using the information from all the 8 visible-near infrareds' bands and trained it. The result is compared with two state-of-the-art ensemble classifiers namely Random Forest (RF) and Gradient Boosting (GB). Results demonstrate that the VGG-16 outperforms all the other methods reaching an accuracy of about 92.13%.