[ci skip] changed "might" for "can" in Step 4 of the paper

paper/paper.md

@@ -31,7 +31,7 @@ DetecTree is based on the supervised learning approach of @yang2009tree, which r
 * **Step 1**: selection of the tiles to be used for training a classifier. As a supervised learning task, the ground-truth maps must be provided for some subset of the dataset. Since this part is likely to involve manual work, it is crucial that the training set has as few tiles as possible. At the same time, to enhance the classifier's ability to detect trees in the diverse scenes of the dataset, the training set should contain as many of the diverse geographic features as possible. Thus, in order to optimize the representativity of the training set, the training tiles are selected according to their GIST descriptor [@oliva2001modeling], *i.e.*, a vector describing the key semantics of the tile's scene. More precisely, *k*-means clustering is applied to the GIST descriptors of all the tiles, with the number of clusters *k* set to the number of tiles of the training set (by default, one percent of the tiles is used). Then, for each cluster, the tile whose GIST descriptor is closest to the cluster's centroid is added to the training set. In DetecTree, this is done by the `train_test_split` method of the `TrainingSelector` class.
 * **Step 2**: provision of the ground truth tree/non-tree masks for the training tiles. For each tile of the training set, the ground-truth tree/non-tree masks must be provided to get the pixel-level responses that will be used to train the classifier. To that end, an image editing software such as GIMP or Adobe Photoshop might be used. Alternatively, if LIDAR data for the training tiles is available, it might also be exploited to create the ground truth masks.
 * **Step 3**: train a binary pixel-level classifier. For each pixel of the training tiles, a vector of 27 features is computed, where 6, 18 and 3 features capture characteristics of color, texture and entropy respectively. A binary AdaBoost classifier [@freund1995desicion] is then trained by mapping the feature vector of each pixel to its class in the ground truth masks (*i.e.*, tree or non-tree).
-* **Step 4**: tree detection in the testing tiles. Given a trained classifier, the `classify_img` and `classify_imgs` methods of the `Classifier` class might respectively be used to classify the tree pixels of a single image tile or of multiple image tiles at scale. For each image tile, the pixel-level classification is refined by means of a graph cuts algorithm [@boykov2004experimental] to avoid sparse pixels classified as trees by enforcing consistency between adjacent tree pixels. An example of an image tile, its pre-refinement pixel-level classification and the final refined result is displayed below:
+* **Step 4**: tree detection in the testing tiles. Given a trained classifier, the `classify_img` and `classify_imgs` methods of the `Classifier` class can respectively be used to classify the tree pixels of a single image tile or of multiple image tiles at scale. For each image tile, the pixel-level classification is refined by means of a graph cuts algorithm [@boykov2004experimental] to avoid sparse pixels classified as trees by enforcing consistency between adjacent tree pixels. An example of an image tile, its pre-refinement pixel-level classification and the final refined result is displayed below:
 
 ![Example of an image tile (left), its pre-refinement pixel-level classification (center) and the final refined result (right).](figure.png)