Szczegóły publikacji
Opis bibliograficzny
LENS-Net: low-energy spiking neural network for remote sensing saliency / Longlong Zhai, Marcin PIETROŃ, Roberto Corizzo, Zhaoru Guo, Yongke Li, Chong Peng, Cui Zhang, Shaochen Jiang, Panpan Zheng // Neurocomputing ; ISSN 0925-2312 . — 2026 — vol. 697 art. no. 134134, s. 1-12. — Bibliogr. s. 11-12, Abstr. — Publikacja dostępna online od: 2026-06-03
Autorzy (9)
- Zhai Longlong
- AGHPietroń Marcin
- Corizzo Roberto
- Guo Z.
- Li Yongke
- Peng Chong
- Zhang Cui
- Jiang Shaochen
- Zheng Panpan
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 168718 |
|---|---|
| Data dodania do BaDAP | 2026-07-30 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.neucom.2026.134134 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Neurocomputing (Amsterdam) |
Abstract
Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) is vital for applications such as urban planning and disaster monitoring. Yet, existing Artificial Neural Network (ANN) models remain energy-intensive and unsuitable for edge deployment. Based on this, we propose a Low-ENergy Spiking Neural Network (LENS-Net) for remote sensing saliency, the first fully spiking neural network for ORSI-SOD. LENS-Net employs an improved Spike-Driven Transformer v3 (SDTv3) encoder to extract multi-scale features with low-energy cost, and a novel Spike Multi-scale Attention Decoder (SpikeMAD) that fuses contextual cues via spike-driven channel attention and up-convolution, ensuring effective saliency representation under sparse computation. Moreover, to stabilize training and improve boundary recognition in complex scenes, we propose a Soft-Clip Spike Firing Approximation (SC-SFA). Across three benchmark datasets, LENS-Net demonstrates outstanding performance while maintaining high energy efficiency, outperforming all lightweight ANN counterparts. For instance, on the ORSSD dataset under a timestep T=4 during inference, LENS-Net achieves a of 92.79%, an of 0.0109, and consumes only 11.48 mJ of energy. These results establish an efficient, low-energy solution for practical ORSI-SOD deployment. The source code is available at: https://github.com/7dra/LENS-Net.
