Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.078314
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

DGRDet: Dynamic Gaussian Receptive Field Encoding-Based Spiking Neural Networks for Remote Sensing Object Detection

Li Chen1, Fan Zhang2,*, Guangwei Xie3, Yanzhao Gao1, Xiaofeng Qi1, Mingqian Sun2
1 National Digital Switching System Engineering & Technological R&D Center, Information Engineering University, Zhengzhou, China
2 School of Computer Science, Fudan University, Shanghai, China
3 Shanghai HONGZHEN Information Science & Technology Corporation, Shanghai, China
* Corresponding Author: Fan Zhang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.078314

Received 29 December 2025; Accepted 16 April 2026; Published online 09 May 2026

Abstract

Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery. Spiking Neural Networks (SNNs), benefiting from their implicit feedback-based and event-driven brain-inspired dynamics, offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models. However, existing SNN-based approaches for remote sensing object detection—particularly for small, arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts. In this work, we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into the encoding stage, proposing a high-precision spiking object detection framework tailored for remote sensing image. Specifically, we design a Hierarchical Feedback-based Gaussian Encoding (HFG) scheme, in which the parameters of Gaussian kernels are dynamically adjusted through spike-triggered top-down feedback connections. This mechanism enables the encoding process to adaptively respond to complex geometric variations of remote sensing objects, including rotation and scale changes. Based on the proposed encoding strategy, we develop DGRDet (Dynamic Gaussian Receptive Field Encoding-based Spiking Neural Networks for Remote Sensing Object Detection), a directly trained deep SNN detector for remote sensing image. Extensive evaluations on the large-scale public DOTA dataset demonstrate that DGRDet achieves competitive detection accuracy, outperforming existing SNN-based object detection methods. Moreover, compared with ANN models of comparable detection performance, DGRDet reduces spike activity by 81.31% and requires only 0.12% of the inference energy consumption, achieving a favorable balance between detection accuracy, efficiency, and energy efficiency.

Keywords

Remote sensing image; object detection; spiking neural networks (SNNs); hierarchical sparse; dynamic gaussian encoding

  • 169

    View

  • 71

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link