Abstract:

Terraces are the main water and soil conservation projects on sloping farmland. Accurate extraction of terrace information is important for monitoring and evaluating soil and water conservation. Aiming at the problem of terrace feature automatic learning in the research on UAV（Unmanned Aerial Vehicle） remote sensing terrace identification, this paper makes a set of terrace orthophoto sample sets with pixel level annotation and designs a terrace identification method combining the FCN-8s（Fully Convolutional Networks, FCN） model with the DenseCRF（Conditional Random Field, CRF） model. The experimental results show that the mean overall accuracy, F1 score and Kappa coefficient of this method in identification of ridged terraces, intensive horizontal terraces and irregular terraces is 86.85%, 87.28%, and 80.41%. Compared with other methods, the identification effect of this paper is better. This method is applicable to the field of UAV remote sensing terrace identification, and it is an accurate and effective identification method.

Key words: terrace identification, UAV image classification, deep learning, semantic segmentation

摘要：

梯田是坡耕地上最主要的水土保持工程，准确地提取梯田信息对水土保持监测和评价十分重要。为了解决无人机遥感梯田识别研究中梯田特征自动学习的问题，制作了一套像素级标注的梯田正射影像样本集并设计FCN-8s模型与DenseCRF模型结合的梯田识别方法。实验结果表明，该方法在山脊区梯田、密集水平梯田和不规则梯田识别的总体精度、F1分数和Kappa系数均值分别为86.85%、87.28%、80.41%，与其他方法相比，效果较好。该方法适用于无人机遥感梯田识别领域，是一种精确有效的识别方法。

关键词: 梯田识别, 无人机影像分类, 深度学习, 语义分割