Tree crown detection and delineation in satellite images using probabilistic voting

Crop yield forecasting in a region has become an important research area due to global warming and related climate changes. Although this can be performed by available statistical information, obtaining recent and up to date data to extract reliable statistical information is not easy. Very high resolution satellite images can be used for this purpose. However, manually processing these images acquired from large regions is neither feasible nor reliable. Therefore, automated methods are needed for this purpose. In this study, we propose a novel method to help forecasting the crop yield in an orchard. The number of trees in an orchard with the size and type of each tree crown gives an approximate crop that can be harvested. Therefore, we focus on both tree crown detection and delineation for this purpose. The proposed method for tree crown detection is based on probabilistic voting. For tree crown delineation, we propose a watershed segmentation based ellipse fitting method. We tested the proposed method on 17 satellite images containing 13,476 trees. We compared the method with the classical local maxima/minima filtering and a recent method in literature using three more test images. These tests indicate the strengths and weaknesses of the proposed method.