Deep semantic segmentation of unmanned aerial vehicle remote sensing images based on fully convolutional neural network

In the era of artificial intelligence and big data, semantic segmentation of images plays a vital role in various fields, such as people's livelihoods and the military. The accuracy of semantic segmentation results directly affects the subsequent data analysis and intelligent applications. Presently, semantic segmentation of unmanned aerial vehicle (UAV) remote-sensing images is a research hotspot. Compared with manual segmentation and object-based segmentation methods, semantic segmentation methods based on deep learning are efficient and highly accurate segmentation methods. The author has seriously studied the implementation principle and process of the classical deep semantic segmentation model-the fully convolutional neural network (FCN), including convolution and pooling in the encoding stage, deconvolution and upsampling, etc., in the decoding stage. The author has applied the three structures (i.e., FCN-32s, FCN-16s, and FCN-8s) to the UAV remote sensing image dataset AeroScapes. And the results show that the accuracy of vegetation recognition is stable at about 94%. The accuracy of road recognition can reach up to more than 88%. The mean pixel accuracy rate of the whole test dataset is above 91%. Applying full convolution neural network to semantic segmentation of UAV remote sensing images can improve the efficiency and accuracy of semantic segmentation significantly.

Automated summary

In the era of artificial intelligence and big data, semantic segmentation of images is crucial for various applications. Deep learning based semantic segmentation methods, specifically the fully convolutional neural network (FCN), have shown to be efficient and highly accurate. The author applied FCN-32s, FCN-16s, and FCN-8s to a UAV remote sensing image dataset and achieved stable accuracy rates of around 94% for vegetation recognition and over 88% for road recognition. The overall mean pixel accuracy rate was above 91%. This demonstrates that utilizing FCN in semantic segmentation of UAV remote sensing images significantly improves efficiency and accuracy.