2023
DOI: 10.1049/cvi2.12218
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LiteCCLKNet: A lightweight criss‐cross large kernel convolutional neural network for hyperspectral image classification

Abstract: High‐performance convolutional neural networks (CNNs) stack many convolutional layers to obtain powerful feature extraction capability, which leads to huge storing and computational costs. The authors focus on lightweight models for hyperspectral image (HSI) classification, so a novel lightweight criss‐cross large kernel convolutional neural network (LiteCCLKNet) is proposed. Specifically, a lightweight module containing two 1D convolutions with self‐attention mechanisms in orthogonal directions is presented. … Show more

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Cited by 5 publications
(1 citation statement)
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“…It extracts important information in the channel domain, while the large-kernel attention (LKA) mechanism extracts a larger range of information in the spatial domain. The large-kernel attention mechanism has high performance in image super-resolution ( 16 ), image classification ( 17 ), and image segmentation ( 18 ). Gondara et al ( 19 ) proposed a convolutional neural network–denoising autoencoder (CNN-DAE) model for medical image denoising.…”
Section: Introductionmentioning
confidence: 99%
“…It extracts important information in the channel domain, while the large-kernel attention (LKA) mechanism extracts a larger range of information in the spatial domain. The large-kernel attention mechanism has high performance in image super-resolution ( 16 ), image classification ( 17 ), and image segmentation ( 18 ). Gondara et al ( 19 ) proposed a convolutional neural network–denoising autoencoder (CNN-DAE) model for medical image denoising.…”
Section: Introductionmentioning
confidence: 99%