改进的U-Net算法在遥感图像典型农作物分类研究

针对传统算法提取遥感图像分类特征不全，及识别农作物分类准确率不高的问题，以无人机遥感图像为数据源，提出改进U-Net模型对研究区域薏仁米、玉米等农作物进行分类识别。实验中首先对遥感影像进行预处理，并进行数据集标注与增强；其次通过加深U-Net网络结构、引入SFAM模块和ASPP模块，多级多尺度特征聚合金字塔方法等对算进行法改进，构建改进的U-Net算法，最后进行模型训练与改进修正。实验结果表明:总体分类精度OA达到88.83%，均交并比MIoU达到0.52，较传统U-Net模型、FCN模型和SegNet模，在分类指标和精度上都有明显的提升。

Research on the classification of typical crops in remote sensing images by improved U-Net algorithm

Aiming at the problem of incomplete classification features of remote sensing images extracted by traditional algorithms and low accuracy of crop classification, we use drone remote sensing images as the data source and propose an improved U-Net model to classify and recognize crops such as barley, corn, etc. in the study area. In the experiment, the remote sensing image is preprocessed, and the data set is labeled and enhanced. Secondly, the algorithm is improved by deepening the U-Net network structure, introducing the SFAM module and the ASPP module, and using the multi-level and multi-scale feature aggregation pyramid method to construct an improved U-Net algorithm. Finally model training and improvement are completed. The experimental results show that the overall classification accuracy OA reaches 88.83%, and the combined ratio of MIoU reaches 0.52. Compared with the traditional U-Net model, FCN model and SegNet model, the classification index and accuracy are significantly improved.