Abstract
This paper presents a novel double weight-based synthetic aperture radar (SAR) and infrared (IR) sensor fusion method (DW-SIF) for automatic ground target recognition (ATR). IR-based ATR can provide accurate recognition because of its high image resolution but it is affected by the weather conditions. On the other hand, SAR-based ATR shows a low recognition rate due to the noisy low resolution but can provide consistent performance regardless of the weather conditions. The fusion of an active sensor (SAR) and a passive sensor (IR) can lead to upgraded performance. This paper proposes a doubly weighted neural network fusion scheme at the decision level. The first weight ( α ) can measure the offline sensor confidence per target category based on the classification rate for an evaluation set. The second weight ( β ) can measure the online sensor reliability based on the score distribution for a test target image. The LeNet architecture-based deep convolution network (14 layers) is used as an individual classifier. Doubly weighted sensor scores are fused by two types of fusion schemes, such as the sum-based linear fusion scheme ( α β -sum) and neural network-based nonlinear fusion scheme ( α β -NN). The experimental results confirmed the proposed linear fusion method ( α β -sum) to have the best performance among the linear fusion schemes available (SAR-CNN, IR-CNN, α -sum, β -sum, α β -sum, and Bayesian fusion). In addition, the proposed nonlinear fusion method ( α β -NN) showed superior target recognition performance to linear fusion on the OKTAL-SE-based synthetic database.
Highlights
Automatic target surveillance and recognition is important for protecting borders and countries.Among several sensors available, infrared (IR) cameras, mid-wave infrared band (3–5 μm), are used frequently in military applications because of the day and night operation capability [1,2].The research scope of this paper focuses only on ground target recognition assuming that the target regions or locations are detected by IR only [3,4,5], synthetic aperture radar (SAR) only [6,7,8], and fused sensor [9,10,11]
Weighted sensor scores are fused by two types of fusion schemes, such as the sum-based linear fusion scheme and neural network-based nonlinear fusion scheme
Among the several deep learning architectures, the LeNet-based deep convolutional neural network architecture [62] is used by changing the input size and number of layers for 16 IR target recognition, as shown in Figure 6.The deep learning architecture used in [63] consists of full convolutional layers without fully connected layers
Summary
Automatic target surveillance and recognition is important for protecting borders and countries. Various image learning-based target recognition methods have been proposed. SAR can measure the electromagnetic scattering property of targets under any weather and light conditions [20] This method is used frequently to recognize a range of targets because it provides a strong radar cross section (RCS) and shape information of non-stealth targets. In SAR-IR fusion-based ATR research, there are three issues: preparation of the database (DB), fusion level, and fusion architecture Based on these issues, the contributions of this paper can be summarized as follows.
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