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Remote sensing image detection method based on brain-inspired spiking neural networks

  • role： First author第一作者
  • Affiliation：

    School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

  • Email：jumpywizard@sjtu.edu.cn
  • Introduction：段德鑫，研究方向为人工智能、神经形态计算。E-mail： jumpywizard@sjtu.edu.cn
DUAN Dexin1,  
  • Affiliation：

    Beijing Institute of Remote Sensing Information, Beijing 100192, China

LU Yao2,  
  • Affiliation：

    Beijing Institute of Remote Sensing Information, Beijing 100192, China

HUANG Liwei2,  
  • Affiliation：

    School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

LIU Peilin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

  • Email：wenfei@sjtu.edu.cn
  • Introduction：文飞，研究方向为类脑计算、机器学习、信号/图像处理。E-mail： wenfei@sjtu.edu.cn
WEN Fei1*

Resümee

In the intelligent processing of remote sensing images, third-generation brain-inspired Spiking Neural Network (SNN) surpasses its predecessor, the second-generation artificial neural network, showing remarkable advantages in terms of high energy efficiency, high precision, and high interpretability. These advantages stem from the SNN’s characteristic features, including high energy efficiency, elevated sparsity, and remarkable bio-plausibility. The integration of these features in the SNN presents an enthralling solution to challenges faced in remote sensing image processing and holds immense potential for advancing this field further.The proposed algorithm introduces a novel approach and initially establishes a target-detection neural network, which serves as the source artificial neural network for pretraining. This source network utilizes a dynamic clipping threshold activation function to optimize its performance. Subsequently, the algorithm transforms the source network into a brain-inspired SNN by leveraging the mapping relationship between activated neurons and spiking neurons. This conversion process effectively incorporates the clipping thresholds obtained during training. By seamlessly transitioning the source network into an SNN, the algorithm ensures the preservation and enhancement of key characteristics essential for remote sensing image processing.The integration of the third-generation brain-inspired SNN into remote sensing image processing holds tremendous potential primarily due to its high precision and low energy consumption. The proposed algorithm highlights the distinctive attributes of the SNN, such as its low delay, high bionics, and ability to inherit high precision observed in a source network. These characteristics are promising indicators of SNN’s capability to considerably enhance the intelligent processing of remote sensing images. By leveraging the SNN’s high precision and bio-plausible principles, the proposed algorithm lays a robust foundation for future advancements in the field of remote sensing. The SNN’s inclusion in the image processing pipeline causes a paradigm shift, challenging traditional assumptions and unlocking new possibilities. The SNN is highly accurate in identifying and classifying targets with remarkable precision during remote sensing target detection. In conclusion, the proposed algorithm exhibits low delay and high bionics while demonstrating the high precision of a source network, demonstrating its potential to considerably improve the intelligent processing of remote sensing images and offering high accuracy, precision, interpretability; low energy consumption; and high bio-plausibility.The efficacy of the proposed method was evaluated through extensive experiments performed on two widely recognized open remote sensing datasetsSAR-Ship-Detection-Datasets (SSDD) and RSOD. Experimental results highlighted the exceptional capabilities of the proposed method in transforming the source network into a brain-inspired spiking neural network, demonstrating negligible loss in performance. Furthermore, the transformed SNN exhibited remarkable accuracy in detecting and recognizing remote sensing targets within significantly reduced time steps. The performance achieved by the transformed SNN was comparable to that of the source artificial neural network while tremendously reducing power consumption by over two orders of magnitudes. This outcome highlights the immense potential of the proposed method in revolutionizing the field of remote sensing image processing by delivering high precision and interpretability which considerably reducing energy consumption.

Schlüsselwort

SAR;remote sensing image;optical remote sensing;target detection;deep learning;spiking neural networks

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