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Article

Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation

1
School of Electronic Engineering, Xidian University, Xi’an 710071, China
2
Faculty of Electrical Engineering, University of Montenegro, 81000 Podgorica, Montenegro
*
Author to whom correspondence should be addressed.
Academic Editor: Tao Lei
Remote Sens. 2021, 13(9), 1772; https://doi.org/10.3390/rs13091772
Received: 7 April 2021 / Revised: 24 April 2021 / Accepted: 29 April 2021 / Published: 1 May 2021
Synthetic aperture radar (SAR) image interpretation has long been an important but challenging task in SAR imaging processing. Generally, SAR image interpretation comprises complex procedures including filtering, feature extraction, image segmentation, and target recognition, which greatly reduce the efficiency of data processing. In an era of deep learning, numerous automatic target recognition methods have been proposed based on convolutional neural networks (CNNs) due to their strong capabilities for data abstraction and mining. In contrast to general methods, CNNs own an end-to-end structure where complex data preprocessing is not needed, thus the efficiency can be improved dramatically once a CNN is well trained. However, the recognition mechanism of a CNN is unclear, which hinders its application in many scenarios. In this paper, Self-Matching class activation mapping (CAM) is proposed to visualize what a CNN learns from SAR images to make a decision. Self-Matching CAM assigns a pixel-wise weight matrix to feature maps of different channels by matching them with the input SAR image. By using Self-Matching CAM, the detailed information of the target can be well preserved in an accurate visual explanation heatmap of a CNN for SAR image interpretation. Numerous experiments on a benchmark dataset (MSTAR) verify the validity of Self-Matching CAM. View Full-Text
Keywords: synthetic aperture radar (SAR) image interpretation; target recognition; class activation mapping (CAM); explanation of convolution neural network (CNN) synthetic aperture radar (SAR) image interpretation; target recognition; class activation mapping (CAM); explanation of convolution neural network (CNN)
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MDPI and ACS Style

Feng, Z.; Zhu, M.; Stanković, L.; Ji, H. Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation. Remote Sens. 2021, 13, 1772. https://doi.org/10.3390/rs13091772

AMA Style

Feng Z, Zhu M, Stanković L, Ji H. Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation. Remote Sensing. 2021; 13(9):1772. https://doi.org/10.3390/rs13091772

Chicago/Turabian Style

Feng, Zhenpeng, Mingzhe Zhu, Ljubiša Stanković, and Hongbing Ji. 2021. "Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation" Remote Sensing 13, no. 9: 1772. https://doi.org/10.3390/rs13091772

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