Next Article in Journal
Joint Optimization Loss Function for Tiny Object Detection in Remote Sensing Images
Previous Article in Journal
Semi-Supervised Object Detection for Remote Sensing Images Using Consistent Dense Pseudo-Labels
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Research on Camouflage Target Classification and Recognition Based on Mid Wave Infrared Hyperspectral Imaging

School of Physics, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(8), 1475; https://doi.org/10.3390/rs17081475
Submission received: 23 January 2025 / Revised: 18 April 2025 / Accepted: 19 April 2025 / Published: 21 April 2025

Abstract

Mid-wave infrared (MWIR) hyperspectral imaging integrates MWIR technology with hyperspectral remote sensing, enabling the capture of radiative information that is difficult to obtain in the visible spectrum, thus demonstrating significant value in camouflage recognition and stealth design. However, there is a notable lack of open-source datasets and effective classification methods in this field. To address these challenges, this study proposes a dual-channel attention convolutional neural network (DACNet). First, we constructed four MWIR camouflage datasets (GCL, SSCL, CW, and LC) to fill a critical data gap. Second, to address the issues of spectral confusion between camouflaged targets and backgrounds and blurred spatial boundaries, DACNet employs independent spectral and spatial branches to extract deep spectral–spatial features while dynamically weighting these features through channel and spatial attention mechanisms, significantly enhancing target–background differentiation. Our experimental results demonstrate that DACNet achieves an average accuracy (AA) of 99.96%, 99.45%, 100%, and 95.88%; an overall accuracy (OA) of 99.94%, 99.52%, 100%, and 96.39%; and Kappa coefficients of 99.91%, 99.41%, 100%, and 95.21% across the four datasets. The classification results exhibit sharp edges and minimal noise, outperforming five deep learning methods and three machine learning approaches. Additional generalization experiments on public datasets further validate DACNet’s superiority in providing an efficient and novel approach for hyperspectral camouflage data classification.
Keywords: hyperspectral remote sensing; convolutional neural network; deep learning; disguising targets; medium-wave infrared hyperspectral remote sensing; convolutional neural network; deep learning; disguising targets; medium-wave infrared
Graphical Abstract

Share and Cite

MDPI and ACS Style

Zhang, S.; Cao, Y.; Bai, L.; Wu, Z. Research on Camouflage Target Classification and Recognition Based on Mid Wave Infrared Hyperspectral Imaging. Remote Sens. 2025, 17, 1475. https://doi.org/10.3390/rs17081475

AMA Style

Zhang S, Cao Y, Bai L, Wu Z. Research on Camouflage Target Classification and Recognition Based on Mid Wave Infrared Hyperspectral Imaging. Remote Sensing. 2025; 17(8):1475. https://doi.org/10.3390/rs17081475

Chicago/Turabian Style

Zhang, Shikun, Yunhua Cao, Lu Bai, and Zhensen Wu. 2025. "Research on Camouflage Target Classification and Recognition Based on Mid Wave Infrared Hyperspectral Imaging" Remote Sensing 17, no. 8: 1475. https://doi.org/10.3390/rs17081475

APA Style

Zhang, S., Cao, Y., Bai, L., & Wu, Z. (2025). Research on Camouflage Target Classification and Recognition Based on Mid Wave Infrared Hyperspectral Imaging. Remote Sensing, 17(8), 1475. https://doi.org/10.3390/rs17081475

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop