S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classification
Deep learning has made significant progress in hyperspectral image (HSI) classification, and its powerful ability to automatically learn abstract features is well recognized. Recently, the simple architecture of multi-layer perceptron (MLP) has been extensively employed to extract long-range depende...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224001080 |
| _version_ | 1797224312982208512 |
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| author | Yanan Jiang Zitong Zhang Chunlei Zhang Heng Zhou Qiaoyu Ma Chengcheng Zhong |
| author_facet | Yanan Jiang Zitong Zhang Chunlei Zhang Heng Zhou Qiaoyu Ma Chengcheng Zhong |
| author_sort | Yanan Jiang |
| collection | DOAJ |
| description | Deep learning has made significant progress in hyperspectral image (HSI) classification, and its powerful ability to automatically learn abstract features is well recognized. Recently, the simple architecture of multi-layer perceptron (MLP) has been extensively employed to extract long-range dependencies of HSI and achieved impressive results. However, existing MLP-based models exhibit insufficient representation of spectral–spatial information in HSI and generally aggregate features with fixed weights, which limits their ability to capture semantic differences. To tackle these challenges, this paper proposes a novel spectral–spatial wave network (S2WaveNet) for HSI classification tasks to enhance the representation capability of spectral–spatial features in ground objects. Specifically, the spectral–spatial wave mixer (S2WaveMixer) block is designed as a key component to represent each HSI input as a wave function with amplitude and phase parts. Thus, it enables a deeper dynamic perception and facilitates the extraction of spectral–spatial feature variations of ground objects. The amplitude represents the original features and the phase term is a complex value changing based on the semantic contents of the input images. Furthermore, the inception unit is introduced into the S2WaveMixer block to consider spectral–spatial information at multiple granularity levels. Experiments conducted on five public datasets demonstrate the superiority of S2WaveNet in classification performance and generalization compared to competitors. |
| first_indexed | 2024-04-24T13:51:08Z |
| format | Article |
| id | doaj.art-0f69179a007f4d6d8f3b191ac5f8699a |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-24T13:51:08Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-0f69179a007f4d6d8f3b191ac5f8699a2024-04-04T05:03:45ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-04-01128103754S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classificationYanan Jiang0Zitong Zhang1Chunlei Zhang2Heng Zhou3Qiaoyu Ma4Chengcheng Zhong5School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China; Correspondence to: China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing 100083, China.Beijing Zhongdi Runde Petroleum Technology Co., Ltd., Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaDeep learning has made significant progress in hyperspectral image (HSI) classification, and its powerful ability to automatically learn abstract features is well recognized. Recently, the simple architecture of multi-layer perceptron (MLP) has been extensively employed to extract long-range dependencies of HSI and achieved impressive results. However, existing MLP-based models exhibit insufficient representation of spectral–spatial information in HSI and generally aggregate features with fixed weights, which limits their ability to capture semantic differences. To tackle these challenges, this paper proposes a novel spectral–spatial wave network (S2WaveNet) for HSI classification tasks to enhance the representation capability of spectral–spatial features in ground objects. Specifically, the spectral–spatial wave mixer (S2WaveMixer) block is designed as a key component to represent each HSI input as a wave function with amplitude and phase parts. Thus, it enables a deeper dynamic perception and facilitates the extraction of spectral–spatial feature variations of ground objects. The amplitude represents the original features and the phase term is a complex value changing based on the semantic contents of the input images. Furthermore, the inception unit is introduced into the S2WaveMixer block to consider spectral–spatial information at multiple granularity levels. Experiments conducted on five public datasets demonstrate the superiority of S2WaveNet in classification performance and generalization compared to competitors.http://www.sciencedirect.com/science/article/pii/S1569843224001080Hyperspectral image classificationMulti-layer perceptron (MLP)Wave representationDynamic feature aggregationInception unit |
| spellingShingle | Yanan Jiang Zitong Zhang Chunlei Zhang Heng Zhou Qiaoyu Ma Chengcheng Zhong S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classification International Journal of Applied Earth Observations and Geoinformation Hyperspectral image classification Multi-layer perceptron (MLP) Wave representation Dynamic feature aggregation Inception unit |
| title | S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classification |
| title_full | S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classification |
| title_fullStr | S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classification |
| title_full_unstemmed | S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classification |
| title_short | S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classification |
| title_sort | s2wavenet a novel spectral spatial wave network for hyperspectral image classification |
| topic | Hyperspectral image classification Multi-layer perceptron (MLP) Wave representation Dynamic feature aggregation Inception unit |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224001080 |
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