Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Attention Mechanism

Liu, Yang; Cao, Kejing; Wang, Ruiyi; Tian, Meng; Xie, Yi

doi:10.1109/lgrs.2022.3172410

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…Involving recalibration of the spectrum features. Numerous scholars have successfully integrated attention mechanisms into spectral similarity measurements, effectively aggregating similar spectra and optimize cognitive performance of convolutional neural networks [44], [45], [46], [47].…”

Section: A Prerequisitementioning

confidence: 99%

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

Jiang,

Zhang,

Tang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral remote sensing images with high spatial resolution (H 2 imagery) have an abundant spatial-spectral information, holding tremendous potential for remote sensing fine-grained monitoring and classification. However, challenges such as high spatial heterogeneity, severe intra-class spectral variability and poor signal-to-noise ratio especially in unmanned aerial vehicle (UAV) hyperspectral imagery constrain and hinder the performance of fine-grained classification. Convolutional neural network (CNN) emerges as a formidable and excellent tool for image mining and feature extraction, offering effective utility for land cover classification. In this paper, a parallel convolutional classification network model based on multi-modal filters (including ICA-2D-FPN and SA-3D-CNN branching structures) PCCN-MSS is proposed for precise H 2 imagery classification. The ICA-2D-FPN branch integrates independent component analysis (ICA) into 2D-CNN to extract the multispatial scale and spectral information of H 2 imagery by Feature Pyramid Networks (FPN), meanwhile the SA-3D-CNN branch is designed to extract the spatial and spectral information by combining spectral attention (SA) mechanism and 3D-CNN. Taking hyperspectral imagery of unmanned aerial vehicles containing vegetation and artifactual material ground as an example, the proposed PCCN-MSS model achieves an overall accuracy (OA) of 78.18%, which outperforms by 9.58% than the compared methods. The proposed PCCN-MSS method can mitigate the classification issues of severe salt-and-pepper noise and inaccurate boundary, delivering more satisfactory classification results with robust classification performance and remarkable advantages for H 2 imagery.

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Section: A Prerequisitementioning

confidence: 99%

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

Jiang,

Zhang,

Tang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Rate coding is coded according to the firing frequency of the spike trains, where the firing frequency is proportional to the intensity of the input pixel values. Most of the current image classification work of the SNN adopts this coding scheme [44,45,46]. Fig.…”

Section: Spiking Encoder Schemesmentioning

confidence: 99%

“…In order to further verify the advantages of the framework on time steps, we compare the classification effects of the proposed model with SNN-SSEM [44] The spiking time step is an important metric to measure the SNN model, The fewer time steps, the higher the accuracy, indicating that the SNN has a strong learning ability. It can be seen from the experimental results that the proposed model can learn the general characteristics of the HSIs over 5 time steps.…”

Section: B Comparison With Spiking Neural Networkmentioning

confidence: 99%

“…Since our research does not include hardware aspects, the SNN model constructed in this paper cannot calculate the actual energy consumption value. However, some of the current literature on SNN roughly estimate the model's energy consumption by calculating the number of network operations [38,44].…”

Section: Network Computing Energy Consumption Estimationmentioning

confidence: 99%

“…In order to further verify the effectiveness of the approximate derivative algorithm proposed in this paper, we apply this algorithm to the SNN-SSEM [44] model, which is used to classify HSI. Then we construct a classification model SNN with the same architecture as SNN-SSEM.…”

Section: Algorithm Evaluationmentioning

confidence: 99%

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Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Approximate Derivative Algorithm

Liu

Cao

et al. 2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

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Recently, deep learning methods have made significant progress in solving hyperspectral images (HSIs) classification problems of high-dimensional features, band redundancy, and spectral mixture. However, the deep neural network is too complex, with a long training time and high energy consumption, making it difficult to deploy on edge computing devices. In order to solve the above problems, this paper proposes a brain-inspired computing framework based on the spiking leaky integrate-andfire neuron model for HSIs classification. Then we design an approximate derivative algorithm to solve the non-differentiable spike activity of the spiking neuron. The framework uses direct coding to generate spatiotemporal spikes for input HSI and achieves efficient extraction of spatial-spectral features through spiking standard convolution and spiking depthwise separable convolution. Extensive experiments are performed on four benchmark hyperspectral data sets and two public unmanned aerial vehicle-borne hyperspectral data sets. Experiments show that the proposed model has the advantages of high classification accuracy and fewer spiking time steps. The proposed model can save about 10 times computational energy consumption compared with the CNN of the same architecture. This research has great significance for overcoming the technical bottleneck of HSI classification based on brain-inspired computing, solving the critical problems of mobile computing in unmanned autonomous systems, and realizing the engineering application of unmanned aerial vehicles and software-defined satellites. The source code will be made available at https://github.com/Katherine-Cao/HSI SNN.

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Research Progress of spiking neural network in image classification: a review

et al. 2023

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Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Attention Mechanism

Cited by 8 publications

References 15 publications

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Approximate Derivative Algorithm

Research Progress of spiking neural network in image classification: a review

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