TY - GEN
T1 - A Multi-Angle Encoding Spiking Convolutional Neural Network for Remote Sensing Classification
AU - Li, Xiang
AU - Zhang, Jingwei
AU - Wang, Peng
AU - Wang, Yanrong
AU - Zhang, Meng
AU - Xu, Feng
AU - Jing, An
AU - Zhang, Lizi
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Spiking Convolutional Neural Networks (SCNNs), known as the third generation of neural networks, are favored for their low energy consumption and biological plausibility, making them ideal for energy-limited applications like satellite remote sensing image classification. Traditional Convolutional Neural Networks (CNNs) consume significant energy, prompting a shift towards more efficient architectures like binary and adder neural networks. However, SCNNs have been overlooked due to their binary information transmission, which typically results in lower accuracy. This paper introduces the Multi-Angle Encoding Spiking Convolutional Neural Network (MASCNN), featuring a Multi-Angle Encoding Layer and a Deep Feature Extraction Module to enhance input information and improve classification accuracy. A new Multi-Angle Loss Function is also proposed to enrich learning. Testing on various datasets shows that MASCNN outperforms other low-energy networks in accuracy while maintaining minimal energy use.
AB - Spiking Convolutional Neural Networks (SCNNs), known as the third generation of neural networks, are favored for their low energy consumption and biological plausibility, making them ideal for energy-limited applications like satellite remote sensing image classification. Traditional Convolutional Neural Networks (CNNs) consume significant energy, prompting a shift towards more efficient architectures like binary and adder neural networks. However, SCNNs have been overlooked due to their binary information transmission, which typically results in lower accuracy. This paper introduces the Multi-Angle Encoding Spiking Convolutional Neural Network (MASCNN), featuring a Multi-Angle Encoding Layer and a Deep Feature Extraction Module to enhance input information and improve classification accuracy. A new Multi-Angle Loss Function is also proposed to enrich learning. Testing on various datasets shows that MASCNN outperforms other low-energy networks in accuracy while maintaining minimal energy use.
KW - Deep learning
KW - Low Energy Consumption
KW - Remote Sensing Images Classification
KW - Spiking Convolutional Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=105000622989&partnerID=8YFLogxK
U2 - 10.1109/ACAI63924.2024.10899489
DO - 10.1109/ACAI63924.2024.10899489
M3 - Conference contribution
AN - SCOPUS:105000622989
T3 - ACAI 2024 - 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence
BT - ACAI 2024 - 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence
A2 - Wang, Zenghui
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024
Y2 - 20 December 2024 through 22 December 2024
ER -
