FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification
Convolutional neural networks and Transformers have garnered substantial attention in hyperspectral image (HSI) classification, and recently Mamba has made significant progress in modeling long sequences. However, existing Mamba-based approaches flatten 2-D images into 1-D sequences, inevitably disr...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10877784/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850204983665885184 |
|---|---|
| author | Peixian Zhuang Xiaochen Zhang Hao Wang Tianxiang Zhang Leiming Liu Jiangyun Li |
| author_facet | Peixian Zhuang Xiaochen Zhang Hao Wang Tianxiang Zhang Leiming Liu Jiangyun Li |
| author_sort | Peixian Zhuang |
| collection | DOAJ |
| description | Convolutional neural networks and Transformers have garnered substantial attention in hyperspectral image (HSI) classification, and recently Mamba has made significant progress in modeling long sequences. However, existing Mamba-based approaches flatten 2-D images into 1-D sequences, inevitably disrupting 2-D local dependencies, thereby disregarding the distinctive difference between high-frequency and low-frequency components in the frequency domain. For HSI tasks, these methods suffer from inherent receptive field limitations and insufficient exploitation of spectral information, resulting in unclear classification of regional boundaries and poor generalizability. To address these issues, we present a novel frequency-aware hierarchical Mamba (FAHM) for HSI classification. Specifically, FAHM is comprised of three main parts: a spatial-spectral interaction block (SSIB), a frequency group embedding module (FGEM), and an adaptive bidirectional Mamba block (ABMB). Building on the observation that the high-frequency component contains detailed features while the low-frequency information provides abundant high-level semantics, we design FGEM to enhance feature representation in the frequency domain. To prepare spectral–spatial contextual tokens for FGEM, we incorporate SSIB to extract shallow features, and ABMB is designed to capture spectral dependencies by modeling spectral variation bidirectionally. We construct our Mamba in a hierarchical manner, and refine multilevel features using a feature affinity module for improved accuracy. Experiments conducted on four HSI benchmark datasets show that the proposed FAHM outperforms eleven existing approaches in classification accuracy and model generalization. |
| format | Article |
| id | doaj-art-d302f0b5520b4083a879e627d2dc15c2 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d302f0b5520b4083a879e627d2dc15c22025-08-20T02:11:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01186299631310.1109/JSTARS.2025.353979110877784FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image ClassificationPeixian Zhuang0https://orcid.org/0000-0002-7143-9569Xiaochen Zhang1https://orcid.org/0009-0002-5905-8541Hao Wang2https://orcid.org/0009-0002-8865-8589Tianxiang Zhang3https://orcid.org/0000-0002-0996-2586Leiming Liu4Jiangyun Li5https://orcid.org/0000-0003-2288-7901Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaKey Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaKey Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, ChinaConvolutional neural networks and Transformers have garnered substantial attention in hyperspectral image (HSI) classification, and recently Mamba has made significant progress in modeling long sequences. However, existing Mamba-based approaches flatten 2-D images into 1-D sequences, inevitably disrupting 2-D local dependencies, thereby disregarding the distinctive difference between high-frequency and low-frequency components in the frequency domain. For HSI tasks, these methods suffer from inherent receptive field limitations and insufficient exploitation of spectral information, resulting in unclear classification of regional boundaries and poor generalizability. To address these issues, we present a novel frequency-aware hierarchical Mamba (FAHM) for HSI classification. Specifically, FAHM is comprised of three main parts: a spatial-spectral interaction block (SSIB), a frequency group embedding module (FGEM), and an adaptive bidirectional Mamba block (ABMB). Building on the observation that the high-frequency component contains detailed features while the low-frequency information provides abundant high-level semantics, we design FGEM to enhance feature representation in the frequency domain. To prepare spectral–spatial contextual tokens for FGEM, we incorporate SSIB to extract shallow features, and ABMB is designed to capture spectral dependencies by modeling spectral variation bidirectionally. We construct our Mamba in a hierarchical manner, and refine multilevel features using a feature affinity module for improved accuracy. Experiments conducted on four HSI benchmark datasets show that the proposed FAHM outperforms eleven existing approaches in classification accuracy and model generalization.https://ieeexplore.ieee.org/document/10877784/Classification accuracyfrequency domainhierarchical structurehyperspectral image (HSI) classificationstate space model (SSM) |
| spellingShingle | Peixian Zhuang Xiaochen Zhang Hao Wang Tianxiang Zhang Leiming Liu Jiangyun Li FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification accuracy frequency domain hierarchical structure hyperspectral image (HSI) classification state space model (SSM) |
| title | FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification |
| title_full | FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification |
| title_fullStr | FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification |
| title_full_unstemmed | FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification |
| title_short | FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification |
| title_sort | fahm frequency aware hierarchical mamba for hyperspectral image classification |
| topic | Classification accuracy frequency domain hierarchical structure hyperspectral image (HSI) classification state space model (SSM) |
| url | https://ieeexplore.ieee.org/document/10877784/ |
| work_keys_str_mv | AT peixianzhuang fahmfrequencyawarehierarchicalmambaforhyperspectralimageclassification AT xiaochenzhang fahmfrequencyawarehierarchicalmambaforhyperspectralimageclassification AT haowang fahmfrequencyawarehierarchicalmambaforhyperspectralimageclassification AT tianxiangzhang fahmfrequencyawarehierarchicalmambaforhyperspectralimageclassification AT leimingliu fahmfrequencyawarehierarchicalmambaforhyperspectralimageclassification AT jiangyunli fahmfrequencyawarehierarchicalmambaforhyperspectralimageclassification |