Title: Using Convolutional Neural Networks to Count Palm Trees in Satellite Images,
Abstract: In this paper we propose a supervised learning system for counting and
localizing palm trees in high-resolution, panchromatic satellite imagery
(40cm/pixel to 1.5m/pixel). A convolutional neural network classifier trained
on a set of palm and no-palm images is applied across a satellite image scene
in a sliding window fashion. The resultant confidence map is smoothed with a
uniform filter. A non-maximal suppression is applied onto the smoothed
confidence map to obtain peaks. Trained with a small dataset of 500 images of
size 40x40 cropped from satellite images, the system manages to achieve a tree
count accuracy of over 99%.