As a branch application of the synthetic aperture radar (SAR), coherent change detection (CCD) is commonly utilized to help differentiate specific changes from the unchanged scene. To exactly pick out man-made changes of interest from change maps, the introduction of artificial neural networks (ANNs) has significantly enhanced detection performance but taken up great computational resources, making it challenging to deploy on edge devices with limited resources and a strict power budget. Spiking neural networks (SNNs) not only infer results more energy-efficiently but also have the potential to drive ultra-low-power neuromorphic computers which ANNs lack. In this study, to detect coherent changes precisely and with less energy consumption, a robust SNN-based change analysis method is proposed to make it available to implement CCD tasks via the “WenTian I” brain-like computer. Firstly, we adopt a Poisson spike encoding method based on fuzzy c-means (FCM) clustering to pre-classify pixels and translate continuous change maps into spikes. Secondly, we input the spike images into a novel deep SNN, multi-threshold Spiking-UNet. By representing information in the form of sparse spike flows, our proposed change detection model can reduce the energy consumption of CCD while maintaining excellent detection capabilities. The corresponding experimental results validate the comparable detection effectiveness and superior energy efficiency of our SNN-based method used for SAR coherent change detection.

Synthetic Aperture Radar (SAR) ship detection is critical to a wide range of maritime applications. Traditional artificial neural networks (ANNs) achieve high detection performance but incur substantial energy consumption, limiting their deployment on edge devices. In contrast, brain-inspired spiking neural networks (SNNs) offer energy-efficiency benefits. Moreover, attention mechanisms can strengthen ship feature representation and suppress background noise in SAR imagery, thereby improving detection accuracy. Motivated by these observations, we propose ESA-YOLO: an energy-efficient spiking attention–based YOLO network for SAR ship detection. First, ESA-YOLO adopts a spike-driven paradigm relying on sparse additive operations to effectively reduce energy consumption. Second, it integrates a spiking attention mechanism. This module captures content-dependent global interactions and long-range context, improving detection performance. Experiments on the HRSID dataset confirm ESA-YOLO’s energy efficiency for SAR ship detection. The results also show that SNNs can be deployed on neuromorphic hardware such as the Wentian chip to reduce energy consumption.