FLOODED AREAS EVALUATION FROM AERIAL IMAGES BASED ON CONVOLUTIONAL NEURAL NETWORK

被引：0
|
作者：
Ichim, Loretta [1 ]
Popescu, Dan [1 ]
机构：
[1] Univ Politehn Bucuresti, Bucharest, Romania
来源：
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年
关键词：
flood evaluation; unmanned aerial vehicle; convolutional neural network; image processing;
D O I：
10.1109/igarss.2019.8898140
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
The most convenient method to assess flood damage in rural areas is to analyze the images taken over by a UAV (unmanned aerial vehicle) team. The paper presents such an aerial unmanned system, implemented by the authors in a research project. The images are directly transmitted via internet to the image processing sub-system. After creating an orthophotoplan, the images are partitioned in patches and then a convolutional neural network is used to classify the patch pixels in flooded type or non-flooded. A set of 100 images with flooded and non flooded zones was used and corresponding 5000 patches (3000 for the learning phase and 2000 for the testing phase). The experimental results show good performances regarding the accuracy and the calculation time.
引用
收藏
页码：9756 / 9759
页数：4
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