Sea scene classification from synthetic aperture radar images using a modified MobileNetV1

Zhongbo Wang; Miao He; Qinghai Ding; Haibo Luo

doi:10.1117/12.3004752

20 October 2023 Sea scene classification from synthetic aperture radar images using a modified MobileNetV1

Zhongbo Wang, Miao He, Qinghai Ding, Haibo Luo

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129161I (2023) https://doi.org/10.1117/12.3004752
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

Synthetic aperture radar (SAR) has a special ability to work in any type of inclement weather, and is a very suitable tool for Ocean surveillance. Scene classification is an essential pre-task of other computer vision tasks for ocean monitoring. It is of great importance to develop scene classification technology of SAR sea images. Due to the excellent feature representation abilities of neural networks, the deep learning-based methods are far superior to the traditional methods based on manual features in scene classification task performance. Many lightweight classification networks have been proposed to improve the inference speed of the networks. But in comparison with ordinary CNNs, the lightweight networks have slightly lower accuracy for scene classification tasks. So in this article, we proposed an improved lightweight Convolutional Neural Network for scene classification of SAR sea images. First, in order to meet the real-time performance, we choose MobileNetv1 as the original classification network in this paper. Then, to compensate for the lack of accuracy, we use 1D asymmetric convolution kernels to strengthen each layer of the depthwise convolutions in the network. Finally, after training time, we merge the linear calculations of each layer of the network to convert it into the original structure. The experimental results show that the modified model has obtained an accuracy improvement than the original one on the scene classification of sea SAR images without extra computation.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhongbo Wang, Miao He, Qinghai Ding, and Haibo Luo "Sea scene classification from synthetic aperture radar images using a modified MobileNetV1", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129161I (20 October 2023); https://doi.org/10.1117/12.3004752

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
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Convolution

Scene classification

Synthetic aperture radar

Education and training

Convolutional neural networks

Matrices

Environmental monitoring

Show All Keywords

Keywords/Phrases

Search In:

Publication Years