One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification
In recent years, hyperspectral image (HSI) classification has become a hot research direction in remote sensing image processing. Benefiting from the development of deep learning, convolutional neural networks (CNNs) have shown extraordinary achievements in HSI classification. Numerous methods combi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/2265 |
| _version_ | 1797502970832617472 |
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| author | Haizhu Pan Moqi Liu Haimiao Ge Liguo Wang |
| author_facet | Haizhu Pan Moqi Liu Haimiao Ge Liguo Wang |
| author_sort | Haizhu Pan |
| collection | DOAJ |
| description | In recent years, hyperspectral image (HSI) classification has become a hot research direction in remote sensing image processing. Benefiting from the development of deep learning, convolutional neural networks (CNNs) have shown extraordinary achievements in HSI classification. Numerous methods combining CNNs and attention mechanisms (AMs) have been proposed for HSI classification. However, to fully mine the features of HSI, some of the previous methods apply dense connections to enhance the feature transfer between each convolution layer. Although dense connections allow these methods to fully extract features in a few training samples, it decreases the model efficiency and increases the computational cost. Furthermore, to balance model performance against complexity, the AMs in these methods compress a large number of channels or spatial resolutions during the training process, which results in a large amount of useful information being discarded. To tackle these issues, in this article, a novel one-shot dense network with polarized attention, namely, OSDN, was proposed for HSI classification. More precisely, since HSI contains rich spectral and spatial information, the OSDN has two independent branches to extract spectral and spatial features, respectively. Similarly, the polarized AMs contain two components: channel-only AMs and spatial-only AMs. Both polarized AMs can use a specially designed filtering method to reduce the complexity of the model while maintaining high internal resolution in both the channel and spatial dimensions. To verify the effectiveness and lightness of OSDN, extensive experiments were carried out on five benchmark HSI datasets, namely, Pavia University (PU), Kennedy Space Center (KSC), Botswana (BS), Houston 2013 (HS), and Salinas Valley (SV). Experimental results consistently showed that the OSDN can greatly reduce computational cost and parameters while maintaining high accuracy in a few training samples. |
| first_indexed | 2024-03-10T03:43:48Z |
| format | Article |
| id | doaj.art-45ffd122f29d4f96ba16fedd4b271d9a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:43:48Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-45ffd122f29d4f96ba16fedd4b271d9a2023-11-23T09:13:04ZengMDPI AGRemote Sensing2072-42922022-05-01149226510.3390/rs14092265One-Shot Dense Network with Polarized Attention for Hyperspectral Image ClassificationHaizhu Pan0Moqi Liu1Haimiao Ge2Liguo Wang3College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, ChinaCollege of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, ChinaCollege of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, ChinaCollege of Information and Communication Engineering, Dalian Nationalities University, Dalian 116000, ChinaIn recent years, hyperspectral image (HSI) classification has become a hot research direction in remote sensing image processing. Benefiting from the development of deep learning, convolutional neural networks (CNNs) have shown extraordinary achievements in HSI classification. Numerous methods combining CNNs and attention mechanisms (AMs) have been proposed for HSI classification. However, to fully mine the features of HSI, some of the previous methods apply dense connections to enhance the feature transfer between each convolution layer. Although dense connections allow these methods to fully extract features in a few training samples, it decreases the model efficiency and increases the computational cost. Furthermore, to balance model performance against complexity, the AMs in these methods compress a large number of channels or spatial resolutions during the training process, which results in a large amount of useful information being discarded. To tackle these issues, in this article, a novel one-shot dense network with polarized attention, namely, OSDN, was proposed for HSI classification. More precisely, since HSI contains rich spectral and spatial information, the OSDN has two independent branches to extract spectral and spatial features, respectively. Similarly, the polarized AMs contain two components: channel-only AMs and spatial-only AMs. Both polarized AMs can use a specially designed filtering method to reduce the complexity of the model while maintaining high internal resolution in both the channel and spatial dimensions. To verify the effectiveness and lightness of OSDN, extensive experiments were carried out on five benchmark HSI datasets, namely, Pavia University (PU), Kennedy Space Center (KSC), Botswana (BS), Houston 2013 (HS), and Salinas Valley (SV). Experimental results consistently showed that the OSDN can greatly reduce computational cost and parameters while maintaining high accuracy in a few training samples.https://www.mdpi.com/2072-4292/14/9/2265hyperspectral image classificationconvolutional neural networkone-shot dense networkspolarized attention mechanisms |
| spellingShingle | Haizhu Pan Moqi Liu Haimiao Ge Liguo Wang One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification Remote Sensing hyperspectral image classification convolutional neural network one-shot dense networks polarized attention mechanisms |
| title | One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification |
| title_full | One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification |
| title_fullStr | One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification |
| title_full_unstemmed | One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification |
| title_short | One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification |
| title_sort | one shot dense network with polarized attention for hyperspectral image classification |
| topic | hyperspectral image classification convolutional neural network one-shot dense networks polarized attention mechanisms |
| url | https://www.mdpi.com/2072-4292/14/9/2265 |
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