Transactions of Nanjing University of Aeronautics & Astronautics
A CNN-Based Method for Sparse SAR Target Classification with Grad-CAM Interpretation
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Abstract:
In recent years， deeps learning has been widely applied in synthetic aperture radar （SAR） image processing. However， the collection of large-scale labeled SAR images is challenging and costly， and the classification accuracy is often poor when only limited SAR images are available. To address this issue， we propose a novel framework for sparse SAR target classification under few-shot cases， termed the transfer learning-based interpretable lightweight convolutional neural network （TL-IL-CNN）. Additionally， we employ enhanced gradient-weighted class activation mapping （Grad-CAM） to mitigate the “black box” effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets. Initially， we apply a novel bidirectional iterative soft thresholding （BiIST） algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering （MF） techniques. Subsequently， we pretrain multiple shallow CNNs on a simulated SAR image dataset. Using the sparse SAR dataset as input for the CNNs， we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further. Finally， Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework. The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90% classification accuracy with only 20% of the training data required under standard operating conditions （SOC）， surpassing typical deep learning methods such as vision Transformer （ViT） in the context of small samples. Remarkably， it even presents better performance under extended operating conditions （EOC）. Furthermore， the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets. The experiments indicate that the model focuses on the target and the background can differ among target classes. The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.
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Project Supported:
This work was supported in part by the National Natural Science Foundation（Nos.62271248， 62401256）， in part by the Natural Science Foundation of Jiangsu Province（Nos.BK20230090，BK20241384）， and in part by the Key Laboratory of Land Satellite Remote Sensing Application， Ministry of Natural Resources of China（No.KLSMNR-K202303）.
Clc Number:
TN925
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