Ms. Luxiao Zhu Profile

Luxiao Zhu

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 29 March 2023 Paper

Research on remote sensing image classification based on RA-UNet

Qihang Zhao, Bin Zhou, Ben Wang, Jin Lu, Luxiao Zhu

Proceedings Volume 12566, 125661A (2023) https://doi.org/10.1117/12.2667743

KEYWORDS: Remote sensing, Neural networks, Convolution, Image classification, Image processing

Read Abstract +

With the development of satellite remote sensing technology, the quality and quantity of remote sensing images are constantly improved. Remote sensing feature classification is also playing an increasingly important role in urban planning, resource exploration and other fields. In the early stage of remote sensing feature classification, machine learning algorithms such as SVM and K-means are mainly used. Nowadays, with the expansion of deep learning, various kinds of research in the computer vision field emerge in an endless manner. Remote sensing images are also mostly classified by different neural networks. According to the characteristics and advantages of U-NET, channel attention mechanism, ResNet, large convolution kernel and structural reparameterization, this paper proposes a network structure called RA-UNET. This paper uses the remote sensing ground object classification dataset LoveDA to conduct experiments. The results show that the network classification effect of this paper is better, with mIoU reaching 59.4% and mPA reaching 72.6%. And use the network in this paper and the four mainstream neural networks of FCN, SegNet, PSPNet and UNet to conduct comparative experiments. The comparative experimental results show that the classification effect of the network in this paper is better than the above four mainstream neural networks.

Proceedings Article | 20 October 2022 Paper

High-resolution remote sensing building extraction based on attention mechanism and DeepLabv3+

Zebin Si, Bin Zhou, Ben Wang, Xiya Wang, Luxiao Zhu

Proceedings Volume 12451, 124510J (2022) https://doi.org/10.1117/12.2656777

KEYWORDS: Convolution, Remote sensing, Image segmentation, Feature extraction, Image processing algorithms and systems, Image classification

Read Abstract +

For the high-resolution remote sensing building image, the object shape is irregular and the texture characteristics are similar to those in the foreground and background. Meanwhile, traditional manual methods spend considerable time extracting features. In this paper, a Deeplabv3+ algorithm is modified and improved to better meet the accuracy, massive, and real-time requirements in semantic segmentation of high-resolution remote-sensing building objects. First of all, the lightweight network MobileNetv2 is implemented to replace Xception as the backbone to extract the network, so as to reduce the number of network parameters and improve the training speed. Secondly, ASPP (Atrus Spatial Pyramid Pooling) obtains the depth effective feature layer and integrates three parallel channel attention mechanisms to improve the extraction effect of depth features. The newly proposed algorithm is tested on the Massachusetts dataset. The results show that the IOU and accuracy of evaluation indicators are increased by 8.31% and 2.11%, respectively, compared with the original Deeplabv3+ model. While reducing the number of model parameters and training time, the segmentation effect of the new algorithm has also been partially optimized.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years