Abstract

Convolutional neural networks (CNNs) have successfully achieved high accuracy in synthetic aperture radar (SAR) target recognition; however, the intransparency of CNNs is still a limiting or even disqualifying factor. Therefore, visually interpreting CNNs with SAR images has recently drawn increasing attention. Various class activation mapping (CAM) methods are adopted to discern the relationship between CNN’s decision and image regions. Unfortunately, most existing CAM methods are based on optical images; thus, they usually lead to a limiting visualization effect for SAR images. Although a recently proposed Self-Matching CAM can obtain a satisfactory effect for SAR images, it is quite time-consuming, due to there being hundreds of self-matching operations per image. G-SM-CAM reduces the time of such operation dramatically, but at the cost of visualization effect. Based on the limitations of the above methods, we propose an efficient method, Spectral-Clustering Self-Matching CAM (SC-SM CAM). Spectral clustering is first adopted to divide feature maps into groups for efficient computation. In each group, similar feature maps are merged into an enhanced feature map with more concentrated energy in a specific region; thus, the saliency heatmaps may more accurately tally with the target. Experimental results demonstrate that SC-SM CAM outperforms other SOTA CAM methods in both effect and efficiency.

Highlights

  • Synthetic aperture radar (SAR) imaging has been widely applied in remote sensing, geoscience, electronic reconnaissance, etc., due to its all-weather, day-and-night working conditions and high-resolution imaging ability [1,2,3,4]

  • The superiority of SC-SM class activation mapping (CAM) in both validity and efficiency will be demonstrated by numerous experiments

  • Both G-SM-CAM and SC-SM CAM adopt the “grouping” strategy; we investigated the influence of group number G on the saliency heatmaps generated by G-SM

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Summary

Introduction

Synthetic aperture radar (SAR) imaging has been widely applied in remote sensing, geoscience, electronic reconnaissance, etc., due to its all-weather, day-and-night working conditions and high-resolution imaging ability [1,2,3,4]. Target recognition is usually deemed one of the most challenging tasks in SAR image processing, due to the blurred edge and heavy speckle noise in SAR images [5,6]. Extraction [9], and feature fusion before a classifier-like support vector machine (SVM), perceptron, decision tree, etc., are used to categorize a SAR image to its most probabilistic classes. These multiple individual pre-processing steps are quite time-consuming and unfriendly for real-time applications. [6] adopted CNN as a classifier in ATR tasks and obtained higher accuracy than SVM. Ref. [11] designed a large margin, softmax batch-normalization CNN (LM-NB-CNN), for the ATR of ground vehicles

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