Systematic Benchmarking of Aerial Image Segmentation

This letter presents a benchmarking study for aerial image segmentation. We construct an image data set consisting of various aerial scenes. Segmentations generated by different human subjects are used as ground truth. We analyze the consistency between segmentations from different subjects. We select six leading segmentation algorithms, which include not only the algorithms specifically designed for aerial images but also more generally applicable algorithms. We also select a recently proposed algorithm due to its promising performance in handling texture regions. We apply these algorithms to the aerial image data set and quantitatively evaluate their performance. We interpret the evaluation results based on the characteristics of algorithms, which provide general guidance for selecting proper algorithms in specific applications.