2023
DOI: 10.1109/tgrs.2023.3241193
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Hybrid Fully Connected Tensorized Compression Network for Hyperspectral Image Classification

Abstract: Deep learning models, such as convolutional neural networks (CNNs), have made significant progress in hyperspectral image (HSI) classification. However, these models require a large number of parameters, which occupy a lot of storage space and suffer from overfitting, thus resulting in performance loss. To solve the above problems, in this article, we propose a new compression network [namely, a Hybrid Fully Connected Tensorized Compression Network (HybridFCTCN)] by considering the high dimensionality of HSI d… Show more

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Cited by 8 publications
(4 citation statements)
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“…To increase spectral image classification accuracy and efficacy, researchers have been looking into various parameter performance improvement strategies. One popular strategy is model compression [30], [31], which allows for a reduction in the number of network parameters by using methods such as pruning [32], [33], quantization [34], and decomposition [35]. However, this method results in information loss, which lowers the performance of the model in classification tasks.…”
Section: B Parameter Performance Optimization Methods In Hsi Classifi...mentioning
confidence: 99%
“…To increase spectral image classification accuracy and efficacy, researchers have been looking into various parameter performance improvement strategies. One popular strategy is model compression [30], [31], which allows for a reduction in the number of network parameters by using methods such as pruning [32], [33], quantization [34], and decomposition [35]. However, this method results in information loss, which lowers the performance of the model in classification tasks.…”
Section: B Parameter Performance Optimization Methods In Hsi Classifi...mentioning
confidence: 99%
“…Simplification methods of the model are mainly divided into model compression and lightweight model design. Li et al [37] proposed a compression network considering the high dimensionality of HSI. A fast and compact 3-D-CNN with few parameters was developed in [38].…”
Section: Hsi Classification Methodsmentioning
confidence: 99%
“…Guo et al 52 proposed a contrastive learning method for the compression task. In another work, Li et al 53 introduced a fully connected approach considering the hyperspectral datacube in terms of tensor.…”
Section: Tensor Ring Decompositionmentioning
confidence: 99%