High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images

Abstract

Ship recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study has proposed a high-efficiency and low-energy ship recognition strategy based on the spiking neural network (SNN) in the SAR images. First, the visual attention mechanism is used to extract the visual saliency map from the SAR image, and then the Poisson encoder is used to encode it into a spike train, which can suppress the background noise while retaining the visual saliency feature of the SAR image. Besides, an SNN model integrating the time-series information is constructed by combining the leaked and integrated firing spiking neurons with the convolutional neural network (CNN), which can use the firing frequency of the spiking neurons to realize the ship recognition in SAR images. Finally, to solve the problem that SNN model is difficult to train, the arctangent function is used as the surrogate gradient function of the spike emission function during the backpropagation. Hence, applying this backpropagation method to the training process can optimize the SNN model. The experimental results show the following: (1) the proposed strategy can more accurately recognize the ship in the SAR image, and the F1 score can reach 98.55%, which has a better recognition performance than the other traditional deep learning methods; (2) the proposed strategy has the least amount of model parameters (only 3.11MB), which is far less than the model parameters of the other traditional deep learning methods; (3) the proposed strategy has fewer operations (only 17.97M) and can reach 1/30 time of operands of the other traditional deep learning methods, which shows the high efficiency of the proposed strategy using the spike emission signals; (4) the proposed strategy has the energy loss of 1.38 × 10−6J, which can achieve the low energy advantage of nearly three orders of the magnitude compared to the other traditional deep learning methods, indicating that the proposed strategy has a significant energy efficiency.

Optimization of energy management in hybrid SOFC-based DC microgrid considering high efficiency and operating safety when external load power goes up

“…The direct-current (DC) microgrid plays an important role in the development of the smart grid as it has the advantages of efficiency, reliability, high power quality, reduced power loss, improved transactive energy, and elimination of the frequency and phase control, 1,2 which has gained wide attention in civilian and military elds. 3,4 Different distributed generations were tried and developed in the past decades to solve the incremental electric power consumption. There are various types of power generation systems 5 available in the market, which mainly include fuel cells (FCs), 6 lithium ion, 7 sodium sulphur, supercapacitor, 8 superconducting magnetic, wind, and solar photovoltaics (PV).…”

Fast Factorized Backprojection Algorithm in Orthogonal Elliptical Coordinate System for Ocean Scenes Imaging Using Geosynchronous Spaceborne–Airborne VHF UWB Bistatic SAR

“…The synthetic aperture radar (SAR) system plays an indispensable role in marine remote sensing, with the applications ranging from shallow water topography mapping to marine military target detection [1][2][3][4][5]. Compared to other sensors such as optical and infrared sensors, the SAR can obtain high-resolution images actively in severe environments and conditions [6].…”

Energy-Efficient and High-Performance Ship Classification Strategy Based on Siamese Spiking Neural Network in Dual-Polarized SAR Images

“…Figure 1 displays some examples of the ship targets in the public SAR image dataset, i.e., the OpenSARShip2.0 dataset [8]. The traditional SAR ship classification method usually achieves the goal of classifying the SAR ships by manually designing the features (such as the geometric structure features, electromagnetic scattering features, transform domain features, local invariant features), but the generalization ability of the traditional SAR ship classification method is usually weak [9,10]. With the development of the deep learning (DL) technology, the artificial neural network (ANN) is gradually replacing the traditional SAR target classification method and becoming the mainstream choice for SAR ship classification [10].…”

“…The traditional SAR ship classification method usually achieves the goal of classifying the SAR ships by manually designing the features (such as the geometric structure features, electromagnetic scattering features, transform domain features, local invariant features), but the generalization ability of the traditional SAR ship classification method is usually weak [9,10]. With the development of the deep learning (DL) technology, the artificial neural network (ANN) is gradually replacing the traditional SAR target classification method and becoming the mainstream choice for SAR ship classification [10]. However, due to the lack of the significant feature differences between the SAR ship classes, severe imbalance class distribution of the SAR ships, and a small number of the SAR ship images, SAR ship classification based on the DL also faces the significant challenges.…”