ETRO VUB
About ETRO  |  News  |  Events  |  Vacancies  |  Contact  
Home Research Education Industry Publications About ETRO

ETRO Publications

Full Details

Journal Publication

Content-Guided Convolutional Neural Network for Hyperspectral Image Classification

This publication appears in: IEEE Transactions on Geoscience and Remote Sensing

Authors: Q. Liu, L. Xiao, J. Yang and J. C-W Chan

Publication Date: Mar. 2020


Abstract:

Convolutional neural networks (CNNs) are of great interest and have demonstrated remarkable performance in hyperspectral images (HSIs) classification. However, due to the current configuration of the convolution layers with a fixed kernel shape, regular CNNs are inherently limited in modeling the diverse land-cover structures, particularly in the cross-classes edge regions, where irregular class boundaries would lead to high classification errors. To address this issue, we propose a content-guided CNN (CGCNN) for HSI classification. Compared with the shape-fixed kernel in the traditional CNN, the proposed content-guided convolution adaptively adjusts its kernel shape according to the spatial distribution of land covers. The content pattern is reflected by a latent guide map automatically learned from HSI. Such content-Adaptive kernel with CGCNN could suppress the irregularity and unexpected features in class boundaries and, thus, improve the feature learning in cross-classes regions. Based on the content-guided convolution, a novel guided feature extraction unit (GFEU) is constructed for spectral-spatial feature learning of HSI. Finally, the CGCNN classification framework is established by stacking multiple GFEUs with dense connection, which is helpful for mitigating the gradient vanishing and increasing the robustness to overfitting. Extensive experiments on several HSIs demonstrate that the proposed approach possesses great details' preserving ability and its performance outperforms other state-of-The-Art methods.

Other Reference Styles
Current ETRO Authors

Prof. Dr. Jonathan C-W Chan

+32 (0)02 629 128

jcheungw@etrovub.be

more info

Other Publications

• Journal publications

IRIS • LAMI • AVSP

• Conference publications

IRIS • LAMI • AVSP

• Book publications

IRIS • LAMI • AVSP

• Reports

IRIS • LAMI • AVSP

• Laymen publications

IRIS • LAMI • AVSP

• PhD Theses

Search ETRO Publications

Author:

Keyword:  

Type:








- Contact person

- IRIS

- AVSP

- LAMI

- Contact person

- Thesis proposals

- ETRO Courses

- Contact person

- Spin-offs

- Know How

- Journals

- Conferences

- Books

- Vacancies

- News

- Events

- Press

Contact

ETRO Department

info@etro.vub.ac.be

Tel: +32 2 629 29 30

©2021 • Vrije Universiteit Brussel • ETRO Dept. • Pleinlaan 2 • 1050 Brussels • Tel: +32 2 629 2930 (secretariat) • Fax: +32 2 629 2883 • WebmasterDisclaimer