Crop Identification of UAV Images Based on an Unsupervised Semantic Segmentation Method

Crop identification is a fundamental task in remote sensing image interpretation. The rapid development of unmanned aerial vehicle (UAV) has revolutionized the acquisition of super-high-resolution images. Compared with remote sensing ones, the fact that UAV images are easier to flexibly acquire and contain more information brings opportunities for refined semantic segmentation. Recently deep learning methods have gained substantial popularity in the field of semantic segmentation. However, the practical application of deep learning methods is often hindered by heavy labeling tasks and computational resources. The object-based Markov random field (OMRF) offers a both time and labor cost-effective unsupervised approach. Nevertheless, the high-spatial heterogeneity exhibited by crops in UAV images brings great difficulties to the application of this method. To address these challenges, this letter introduces RA-OMRF model, an unsupervised approach that improves the OMRF model through our newly proposed pre-processing step named Region Aid (RA). RA is used to increase the amount of data for categories with fewer samples to alleviate the problem of high-spatial heterogeneity of ground object categories, thereby improving the performance of the OMRF model. Compared to five deep learning models, our method achieves matching or even higher segmentation accuracy [for instance, an overall accuracy (OA) of 97.43% on the UAV Dataset of Wheats in Shuidao Township (DWST)] in crop identification tasks of UAV images, while reducing time and labor costs.