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Biological soil crust is an important feature of desert ecosystem composition and surface landscape. Determining the role of biological soil crust in the energy flow and logistics cycle in the desert ecosystem is one of the frontier areas of ecological restoration in arid and semi-arid areas. Obtaining biological soil crust information from drone images is an efficient, fast, and low-cost method. However, due to the scattered and uneven growth of biological soil crusts and the complexity of the field environment, it is difficult to accurately extract biological soil crusts. In view of this, this study used the improved UNet++ model to extract biological soil crusts based on UAV image data. Firstly, the optimal Epoch, Backbone, and Loss function are selected and trained based on the network structure of UNet++ model. Then, the improved UNet++ model proposed in this paper, which takes ResNeXt as the Backbone and Soft Cross-Entropy Loss+Dice Loss as the Loss Function, is obtained. Finally, the test results of UNet++, U-Net, LinkNet, FPN, and PSPNet are compared with the improved UNet++ model in this paper. The results showed that the improved UNet++ model had the best segmentation effect, and the precision, recall, F1-Score, and IoU were 0.9788, 0.9501, 0.9495, and 0.9309, respectively. UAV image biological soil crust recognition based on the improved UNet++ model in this paper can obtain high-precision extraction results, provide good data support for studying the development of biological soil crusts in arid areas, and provide a new method for precise segmentation of different features in arid areas. In particular, it is of great significance to evaluate the governance effect of ecological restoration projects.

Unmanned aerial vehicle image biological soil crust recognition based on UNet++