%0 Journal Article
%T 基于DenseNet模型的无人机高分辨率影像树种分类研究<br>Tree Species Classification Based on DenseNet Model in UAV High Resolution Images
%A 张泽宇
%A 王妮
%A 朱锦富
%A 孟祥端
%A 董思萌
%J Geomatics Science and Technology
%P 139-145
%@ 2329-7239
%D 2021
%I Hans Publishing
%R 10.12677/GST.2021.94017
%X 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。<br />
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.
%K DenseNet，残差网络，无人机，深度学习，树种分类<br>DenseNet
%K Residual Network
%K UAV
%K Deep Learning
%K Tree Species Classification
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=45993