Next Article in Journal
Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification
Previous Article in Journal
Mapping Invasive Phragmites australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, and Synthetic Aperture Radar
Article

Compound Multiscale Weak Dense Network with Hybrid Attention for Hyperspectral Image Classification

1
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
3
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Academic Editor: Johannes R. Sveinsson
Remote Sens. 2021, 13(16), 3305; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163305
Received: 12 July 2021 / Revised: 17 August 2021 / Accepted: 18 August 2021 / Published: 20 August 2021
Recently, hyperspectral image (HSI) classification has become a popular research direction in remote sensing. The emergence of convolutional neural networks (CNNs) has greatly promoted the development of this field and demonstrated excellent classification performance. However, due to the particularity of HSIs, redundant information and limited samples pose huge challenges for extracting strong discriminative features. In addition, addressing how to fully mine the internal correlation of the data or features based on the existing model is also crucial in improving classification performance. To overcome the above limitations, this work presents a strong feature extraction neural network with an attention mechanism. Firstly, the original HSI is weighted by means of the hybrid spectral–spatial attention mechanism. Then, the data are input into a spectral feature extraction branch and a spatial feature extraction branch, composed of multiscale feature extraction modules and weak dense feature extraction modules, to extract high-level semantic features. These two features are compressed and fused using the global average pooling and concat approaches. Finally, the classification results are obtained by using two fully connected layers and one Softmax layer. A performance comparison shows the enhanced classification performance of the proposed model compared to the current state of the art on three public datasets. View Full-Text
Keywords: hyperspectral image classification; deep learning; attention mechanism; multiscale feature extraction; feature fusion; skip connection hyperspectral image classification; deep learning; attention mechanism; multiscale feature extraction; feature fusion; skip connection
Show Figures

Graphical abstract

MDPI and ACS Style

Ge, Z.; Cao, G.; Shi, H.; Zhang, Y.; Li, X.; Fu, P. Compound Multiscale Weak Dense Network with Hybrid Attention for Hyperspectral Image Classification. Remote Sens. 2021, 13, 3305. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163305

AMA Style

Ge Z, Cao G, Shi H, Zhang Y, Li X, Fu P. Compound Multiscale Weak Dense Network with Hybrid Attention for Hyperspectral Image Classification. Remote Sensing. 2021; 13(16):3305. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163305

Chicago/Turabian Style

Ge, Zixian, Guo Cao, Hao Shi, Youqiang Zhang, Xuesong Li, and Peng Fu. 2021. "Compound Multiscale Weak Dense Network with Hybrid Attention for Hyperspectral Image Classification" Remote Sensing 13, no. 16: 3305. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163305

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop