Convolutional neural networks’ efficiency on binary classification of damaged building on post hurricane satellite imagery

Bolun Liu; Chenhao Lyu; Sihan Wang

doi:10.1117/12.2641003

13 October 2022 Convolutional neural networks’ efficiency on binary classification of damaged building on post hurricane satellite imagery

Bolun Liu, Chenhao Lyu, Sihan Wang

Author Affiliations +

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122870Z (2022) https://doi.org/10.1117/12.2641003
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Assessment of which place was damaged after a hurricane is an emergent task. However, handling this task by humans is laborious and inefficient even with the help of hurricane satellite imagery. A good way to detect damaged buildings effectively and time-savingly is to apply a pre-trained convolutional neural network (CNN) based on post-hurricane satellite images for processing real-time satellite images. However, training a complex CNN is time-consuming, and choosing different networks could get different efficiencies in training the network and detecting damaged buildings. In this paper, we compare the efficiency of different CNN architectures that VGG16, VGG19, ResNet50, InceptionV3, and convolutional neural network built by us on binarily classifying damaged buildings on post-hurricane satellite images. We evaluate the efficiency of networks by the balanced and unbalanced tests accuracy, the training time, and the inference time. With the given data for training and testing, the network we built is significantly more efficient than other existing networks with less training time, less evaluation time, and more accuracy (over 99%); but it is less complex and has lower parameters compared to other existing networks.

Citation Download Citation

Bolun Liu, Chenhao Lyu, and Sihan Wang "Convolutional neural networks’ efficiency on binary classification of damaged building on post hurricane satellite imagery", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122870Z (13 October 2022); https://doi.org/10.1117/12.2641003

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Neural networks

Convolutional neural networks

Satellites

Earth observing sensors

Satellite imaging

Performance modeling

Image classification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years