## 2 Tree Detection and Counting
With a goal opposite to that of identifying individual trees [PERSON] and [PERSON] [17] aimedto localize, using VHR aerial images missing plants in olive groves planted according to a geometrical squared-grid pattern. The parcels were considered as a topological graph of vertices, formed by centroids identified on individual plants, whose mutual position conforms to a set of geometrical rules on orientation and length. In particular, the algorithm built and used the topological and geometrical relationships between the vertices, in order to identify the \"missing vertices\", i.e., the missing plants. Other authors based their tree detection on the canopies' shape. [PERSON] et al. [29] proposed an automatic algorithm to count olive trees using satellite imagery (Quickbird) based on canopies' circular shape. Specifically, this method involved the segmentation of the image using Otsu thresholding [34] for obtaining a binary map. Olive trees were detected by exploiting their circular shape using the Circular Hough Transform (CHT) algorithm [35]. Results showed a very high overall accuracy (96%). However, the circular shape of objects other than olive trees, such as bushes, rocks, and uneven land patches, induces misclassification errors. Also, [PERSON] and [PERSON] [15] proposed a method for automatic olive tree extraction and counting (in IKONOS and Quickbird imagery) based on the circular shape of olive trees seen as blobs that appear brighter at the tips in the center with shadows following towards their bases. Olive tree extraction was performed after locating the local spatial maxima of the Laplacian, a differential operator exploited for edge detection along with blob detection. Trees appear as blobs in a binary image that can be labeled and counted. However, blob detection can be prone to errors since it classifies all trees with circular shapes as olive trees. In presenting their approach, [PERSON] et al. [22] also considered olive trees as blobs in IKONOS' imagery. Firstly, the olive trees were distinguished from other classes using a Gaussian process classifier [36], producing a binary map in which olive trees represent the primary information. Then, the map was pre-processed using the erosion morphological operator to isolate single tree canopies that were automatically counted, considering each blob valid only if its size was within a specific predetermined range referring to the actual size of trees. Similarly, [PERSON] et al. [30] proposed multi-step image processing techniques applied over a single (red) band for counting olive trees in Quickbird imagery. The single band was sharpened using the high pass filter and edges were detected, converting each olive tree's closed boundaries into blobs using morphological reconstruction. A binarization permitted distinguishing background from trees. [PERSON] et al. [12] proposed an approach based on morphological image analysis for olive tree identification in VHR imagery using airborne orthophotos, Quickbird, and IKONOS single band imagery. The morphological image analysis focuses on the pixel groups' shape and spatial arrangement. In particular, considering that tree crowns appear as dark objects, they contain a regional minimum which can be defined as a connected component of pixels whose neighbors have a higher intensity value [37]. The regional minima method was compared with the semi-automatic approach of OLICOUNT software. The software exploits an algorithm based on image spectral threshold, region growing, and tree morphological parameters. The two approaches showed similar omission errors, while the omission errors were higher for the regional minima method. The OLICOUNT software was used by [PERSON] et al.