Comparison of Supervised Classifiers and Image Features for Crop Rows Segmentation on Aerial Images

In this paper we present a comparison of supervised classifiers and image features for crop row segmentation of aerial images captured from an unmanned aerial vehicle (UAV). The main goal is to investigate which methods are the most suitable to solve this specific problem, as well as to test quantitatively how well they perform for robust segmentation of row patterns. For this purpose, we conducted a systematic literature review over the recent methods specifically designed for aerial image crop row segmentation, and for comparison purposes we implemented the most prominent approaches. Most used Color-texture features were faced against most used classifiers, resulting into a total of 48 combinations, usually having their construction concepts based on the following two step-procedures: (i) supervised training step to build some model over the selected color-texture feature space which is also based upon user-selected samples from the input image; and (ii) classification step, where each pixel of the input image is classified employing the corresponding classifier. The obtained results were compared against a Ground-Truth (GT) image, performed by a human expert, using two distinct evaluation metrics, indicating the most suitable combination of color-texture descriptors and classifiers able to solve the segmentation problem of specific cultures obtained from UAV images.