Capsule attention module-based CapsNet for hyperspectral image classification

Xinsheng Zhang; Zhaohui Wang

doi:10.1117/12.2679994

27 June 2023 Capsule attention module-based CapsNet for hyperspectral image classification

Xinsheng Zhang, Zhaohui Wang

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270514 (2023) https://doi.org/10.1117/12.2679994
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Hyperspectral image (HSI) classification aims to assign each pixel with a proper land-cover label. Over the past few years, HSI classification using convolutional neural networks (CNNs) has progressed significantly. In spite of their effectiveness, CNN is not efficient in capturing the hierarchical structure of the entities in the images and does not fully consider the spatial information that is important to classification. Capsule network (CapsNet) preserves the hierarchy between different parts of the entity in an image by replacing scalar representations with vectors which has become an active area in the classification field in the past years. In this article, a capsule attention module-based CapsNet (CAM-CapsNet) is proposed which is not only employed to improve the performance of HSI classification but also to reduce the computation cost of the model. Specifically, 3-D convolutional layers are used to extract higher level spatial and spectral features. The local connection dynamic routing is proposed to reduce the number of parameters in the network. For the sake of boosting the representational capacity of CapsNet for spectral-spatial HSI classification, the network is improved by discriminating the significance of different spectral bands. A capsule attention module is designed to adaptively recalibrate spectral bands by selectively emphasizing informative bands and suppressing the less useful ones. The CAM-CapsNet was trained on three HSI datasets and achieved higher accuracy by comparing with some of the state-of-the-art models.

Citation Download Citation

Xinsheng Zhang and Zhaohui Wang "Capsule attention module-based CapsNet for hyperspectral image classification", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270514 (27 June 2023); https://doi.org/10.1117/12.2679994

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
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Convolution

Feature extraction

3D modeling

Deep learning

Content addressable memory

Education and training

Image classification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years