Shrub Detection in High-Resolution Imagery: A Comparative Study of Two Deep Learning Approaches

A common task in high-resolution remotely-sensed aerial imagery is the detection of particular target plant species for various ecological and agricultural applications. Although traditionally object-based image analysis approaches have been the most popular method for this task, deep learning approaches such as image patch-based convolutional neural networks (CNNs) have been seen to outperform these older approaches. To a lesser extent, fully convolutional networks (FCNs) that allow for semantic segmentation of images, have also begun to be used in the broader literature.