A Multiscale Cross Interaction Attention Network for Hyperspectral Image Classification Article Swipe

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Dongxu Liu , Yirui Wang , Peixun Liu , Qingqing Li , Hang Yang , Dianbing Chen , Zhichao Liu , Guangliang Han ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.3390/rs15020428

Convolutional neural networks (CNNs) have demonstrated impressive performance and have been broadly applied in hyperspectral image (HSI) classification. However, two challenging problems still exist: the first challenge is that redundant information is averse to feature learning, which damages the classification performance; the second challenge is that most of the existing classification methods only focus on single-scale feature extraction, resulting in underutilization of information. To resolve the two preceding issues, this article proposes a multiscale cross interaction attention network (MCIANet) for HSI classification. First, an interaction attention module (IAM) is designed to highlight the distinguishability of HSI and dispel redundant information. Then, a multiscale cross feature extraction module (MCFEM) is constructed to detect spectral–spatial features at different scales, convolutional layers, and branches, which can increase the diversity of spectral–spatial features. Finally, we introduce global average pooling to compress multiscale spectral–spatial features and utilize two fully connection layers, two dropout layers to obtain the output classification results. Massive experiments on three benchmark datasets demonstrate the superiority of our presented method compared with the state-of-the-art methods.

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Computer Science

Artificial Intelligence

Convolutional Neural Network

Benchmark (Surveying)

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Computer science Pattern recognition (psychology) Artificial intelligence Convolutional neural network Pooling Hyperspectral imaging Benchmark (surveying) Feature (linguistics) Feature extraction Dropout (neural networks) Focus (optics) Machine learning Optics Physics Geography Linguistics Philosophy Geodesy

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/rs15020428
PDF: https://www.mdpi.com/2072-4292/15/2/428/pdf?version=1674011379
OA Status: gold
Cited By: 7
References: 81
Related Works: 10
OpenAlex ID: https://openalex.org/W4315568068

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DOI: https://doi.org/10.3390/rs15020428

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Title: A Multiscale Cross Interaction Attention Network for Hyperspectral Image Classification

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Language: en

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Publication year: 2023

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Publication date: 2023-01-10

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Authors: Dongxu Liu, Yirui Wang, Peixun Liu, Qingqing Li, Hang Yang, Dianbing Chen, Zhichao Liu, Guangliang Han

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Landing page: https://doi.org/10.3390/rs15020428

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Concepts: Computer science, Pattern recognition (psychology), Artificial intelligence, Convolutional neural network, Pooling, Hyperspectral imaging, Benchmark (surveying), Feature (linguistics), Feature extraction, Dropout (neural networks), Focus (optics), Machine learning, Optics, Physics, Geography, Linguistics, Philosophy, Geodesy

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Cited by: 7

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