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

页码：9756 / 9759

页数：4

共 50 条

[21] Sequentially Delineation of Rooftops with Holes from VHR Aerial Images Using a Convolutional Recurrent Neural Network
Huang, Wei
Liu, Zeping
Tang, Hong
Ge, Jiayi
REMOTE SENSING, 2021, 13 (21)
[22] Disaster Detection from Aerial Imagery with Convolutional Neural Network
Amit, Siti Nor Khuzaimah Binti
Aoki, Yoshimitsu
2017 INTERNATIONAL ELECTRONICS SYMPOSIUM ON KNOWLEDGE CREATION AND INTELLIGENT COMPUTING (IES-KCIC), 2017, : 239 - 245
[23] Damage Detection from Aerial Images via Convolutional Neural Networks
Fujita, Aito
Sakurada, Ken
Imaizumi, Tomoyuki
Ito, Riho
Hikosaka, Shuhei
Nakamura, Ryosuke
PROCEEDINGS OF THE FIFTEENTH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS - MVA2017, 2017, : 5 - 8
[24] Classification of retinal images based on convolutional neural network
El-Hag, Noha A.
Sedik, Ahmed
El-Shafai, Walid
El-Hoseny, Heba M.
Khalaf, Ashraf A. M.
El-Fishawy, Adel S.
Al-Nuaimy, Waleed
Abd El-Samie, Fathi E.
El-Banby, Ghada M.
MICROSCOPY RESEARCH AND TECHNIQUE, 2021, 84 (03) : 394 - 414
[25] SoybeanNet: Transformer-based convolutional neural network for soybean pod counting from Unmanned Aerial Vehicle (UAV) images
Li, Jiajia
Magar, Raju Thada
Chen, Dong
Lin, Feng
Wang, Dechun
Yin, Xiang
Zhuang, Weichao
Li, Zhaojian
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 220
[26] Sports Sequence Images Based on Convolutional Neural Network
Chen, Yonghao
MATHEMATICAL PROBLEMS IN ENGINEERING, 2021, 2021
[27] Traversability Learning from Aerial Images with Fully Convolutional Neural Networks
Borges, Carlos David Braga
Sa Junior, Jarbas Joaci de Mesquita
NEURAL PROCESSING LETTERS, 2023, 55 (09) : 11993 - 12015
[28] Traversability Learning from Aerial Images with Fully Convolutional Neural Networks
Carlos David Braga Borges
Jarbas Joaci de Mesquita Sá Junior
Neural Processing Letters, 2023, 55 (9) : 11993 - 12015
[29] Vehicle detection in aerial images based on lightweight deep convolutional network
Shen, Jiaquan
Liu, Ningzhong
Sun, Han
IET IMAGE PROCESSING, 2021, 15 (02) : 479 - 491
[30] Convolutional neural network based prediction of effective diffusivity from microscope images
Sethi, Smruti Ranjan
Kumawat, Vinit
Ganguly, Somenath
JOURNAL OF APPLIED PHYSICS, 2022, 131 (21)

← 1 2 3 4 5 →