Augmenting a convolutional neural network with local histograms - A case study in crop classification from high-resolution UAV imagery

The advent of affordable drones capable of taking high resolution images of agricultural fields creates new challenges and opportunities in aerial scene understanding. This paper tackles the problem of recognizing crop types from aerial imagery and proposes a new hybrid neural network architecture which combines histograms and convolutional units. We evaluate the performance of the hybrid model on a 23-class classification task and compare it to convolutional and histogram-based models. The result is an improvement of the classification performance.