基于DenseNet模型的无人机高分辨率影像树种分类研究

Tree Species Classification Based on DenseNet Model in UAV High Resolution Images

摘要: 如今深度学习广泛应用于医学、工业、人工智能以及地理学等领域。本文基于DenseNet模型，在其残差块之间加入1 × 1的小型卷积核作为瓶颈层得到了一种改进的DenseNet_BL模型，以琅琊山林场为研究区，使用DenseNet121_BL和DenseNet169_BL模型对研究区的无人机高分辨率光学影像进行分类研究实验。得到的实验结果表明DenseNet121_BL模型在进行树种分类时正确率最高，达到了88.29%。说明改进后的DenseNet_BL模型是一种有效的树种分类算法。

Abstract: Deep learning is widely used in medicine, industry, artificial intelligence, geography and other fields. This paper proposes an improved DenseNet_BL model based on DenseNet model. An improved DenseNet_BL model is obtained by adding a 1 × 1 small convolution kernel between the Residual Blocks as the Bottleneck Layer. Taking Langya Mountain Forest as the research area, DenseNet 121_BL and DenseNet169_BL models were used to classify UAV high-resolution optical images in the research area. The experimental results showed that DenseNet_BL121 model had the highest accuracy in tree species classification, reaching 88.29%. The improved DenseNet_BL model is an effective tree species classification algorithm.