An automatic approach for palm tree counting in UAV images

In this paper, we develop an automatic method for counting palm trees in UAV images. First we extract a set of keypoints using the Scale Invariant Feature Transform (SIFT). Then, we analyze these keypoints with an Extreme Learning Machine (ELM) classifier a priori trained on a set of palm and no-palm keypoints. As output, the ELM classifier will mark each detected palm tree by several keypoints. Then, in order to capture the shape of each tree, we propose to merge these keypoints with an active contour method based on level-sets (LS). Finally, we further analyze the texture of the regions obtained by LS with local binary patterns (LBPs) to distinguish palm trees from other vegetations. Experimental results obtained on a UAV image acquired over a palm farm are reported and discussed.