BANANA-SEG, A FULLY CONVOLUTIONAL DEEP NEURAL NETWORK FOR BANANA TREE CROWN MAPPING IN AERIAL IMAGERY ACQUIRED FROM AN UNMANNED AERIAL VEHICLE (UAV)
Journal: JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (JCET) (Vol.9, No. 6)Publication Date: 2018-12-29
Authors : RAMESH KESTUR; MEENAVATHI M.B;
Page : 30-37
Keywords : Unmanned Aerial Vehicle; Low Altitude remote sensing; Tree crown mapping; Fully Convolutional Neural Network (FCN);
Abstract
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.
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Last modified: 2018-12-11 16:09:07