Deep Learning Based Unmanned Aerial Vehicle Landcover Image Segmentation Method

被引：0
|
作者：
Liu W. [1 ]
Zhao L. [1 ]
Zhou Y. [1 ]
Zong S. [2 ]
Luo Y. [2 ]
机构：
[1] School of Information, Beijing Forestry University, Beijing
[2] School of Forestry, Beijing Forestry University, Beijing
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2020年 / 51卷 / 02期
关键词：
Convolutional neural network; Deep learning; Landcover images; Semantic segmentation; Unmanned aerial vehicle;
D O I：
10.6041/j.issn.1000-1298.2020.02.024
中图分类号：
学科分类号：
摘要：
Compilation of land-cover maps needs high qualified land-cover data with precise classification. Traditional techniques to obtain these have the problem of high cost, heavy workload and unsatisfied results. To this end, a semantic segmentation method was proposed for unmanned aerial vehicle (UAV) images, which was used to segment and classify different types of land areas to obtain land-cover data. Firstly, the UAV images were annotated which contained various land use types at pixel level according to the latest national standards, and the high-resolution complex land-cover image data set of UAV was established. Then, several significant improvements based on original design of semantic segmentation model DeepLabV3+ were made, including replacing the original backbone network Xception+ with the deep residual network ResNet+; adding joint upsampling unit after backbone network to enhance the encoder's capability of information transfer and conduct preliminary upsampling; adjusting dilated rates of atrous spatial pyramid pooling (ASPP) unit to smaller ones and removing global pooling connection of the module; and improving the decoder by fusing more low-level features. Finally, the models were trained and tested on the UAV high-resolution land-cover dataset. The presented model achieved good experimental results with pixel accuracy of 95.06% and mean intersection-over-union of 81.22% on the test set, which was 14.55 percentage points and 25.49 percentage points higher than that of the original DeepLabV3+ model respectively. The proposed method was also superior to the commonly used semantic segmentation methods FCN-8S (pixel accuracy was 32.39%, mean intersection-over-union was 8.39%) and PSPNet (pixel accuracy was 87.50%, mean intersection-over-union was 50.75%). The results showed that the proposed method can obtain more accurate land-cover data and meet the needs of compiling fine land-cover maps. © 2020, Chinese Society of Agricultural Machinery. All right reserved.
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页码：221 / 229
页数：8
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