BANANA-SEG, A FULLY CONVOLUTIONAL DEEP NEURAL NETWORK FOR BANANA TREE CROWN MAPPING IN AERIAL IMAGERY ACQUIRED FROM AN UNMANNED AERIAL VEHICLE (UAV)

Mapping of tree crowns is important in agriculture and ecology. Current manual systems of tree crown mapping are cumbersome and inefficient. In recent years, UAVs are emerging as a platform for remote sensing that complements traditional satellite based remote sensing systems. Remote sensing from a UAV also known as Low Altitude Remote Sensing (LARS) provides interesting options for agriculture since they allow study of crops at a sub decimeter ground separation distance (GSD). We propose Banana-Seg fully Convolutional deep neural network architecture for mapping of tree crowns in aerial imagery. Banana-Seg is a two dimensional Convolutional Neural Network (2D-CNN) architecture. The performance of tree crown mapping using Banana-Seg is evaluated by performance parameters derived from a confusion matrix or contingency matrix. Precision, recall, accuracy and F1- score performance are evaluated. The performance of Banana-Seg method is compared with a one dimensional CNN (1D-CNN) architecture. Further, visualization of performance is presented for several test images. The results indicate that the proposed Banana-Seg architecture outperforms the 1D-CNN method.