A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification
<p>Hyperspectral imaging (HSI) plays a pivotal role in remote sensing, enabling precise material identification through spectral data across many bands. Despite its advantages, challenges like high dimensionality, spectral mixing, and limited labelled data hinder its classification accuracy. T...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| _version_ | 1864513532018556928 |
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| author | Mohamed Sultan Mohamed Ali (17317003) |
| author2 | Md Sakib Bin Islam (22804121) Molla E. Majid (21323921) Saad Bin Abul Kashem (17773188) Amith Khandakar (14151981) Muhammad E.H. Chowdhury (17151154) |
| author2_role | author author author author author |
| author_facet | Mohamed Sultan Mohamed Ali (17317003) Md Sakib Bin Islam (22804121) Molla E. Majid (21323921) Saad Bin Abul Kashem (17773188) Amith Khandakar (14151981) Muhammad E.H. Chowdhury (17151154) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed Sultan Mohamed Ali (17317003) Md Sakib Bin Islam (22804121) Molla E. Majid (21323921) Saad Bin Abul Kashem (17773188) Amith Khandakar (14151981) Muhammad E.H. Chowdhury (17151154) |
| dc.date.none.fl_str_mv | 2025-11-04T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.rsase.2025.101779 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_hybrid_3D_CNN-LSTM_model_with_soft_spatial_attention_mechanism_for_accurate_hyperspectral_image_classification/30820007 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Agricultural biotechnology Environmental sciences Environmental management Information and computing sciences Artificial intelligence Data management and data science Hyperspectral imaging (HSI) Remote sensing 3D convolutional neural networks (CNNs) Long short-term memory (LSTM) Mixed pixels |
| dc.title.none.fl_str_mv | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Hyperspectral imaging (HSI) plays a pivotal role in remote sensing, enabling precise material identification through spectral data across many bands. Despite its advantages, challenges like high dimensionality, spectral mixing, and limited labelled data hinder its classification accuracy. This study introduces a hybrid deep learning model that combines 3D Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, incorporating residual connections and a soft spatial attention mechanism to overcome these limitations. The model utilizes 3D CNNs for the extraction of joint spatial-spectral features, employs LSTMs to capture sequential dependencies across spectral bands, and incorporates soft spatial attention mechanism to emphasize discriminative spatial regions. The model has been evaluated using benchmark datasets—Indian Pines (16 land cover classes) achieved remarkable overall accuracies of 99.66 % and 99.58 % on external validation in Salinas (16 agricultural) dataset, respectively, exceeding the performance of current leading methods. Ablation studies confirmed the essential functions of residual connections in enhancing training stability and soft spatial attention mechanism in mitigating redundant features. The findings indicate strong generalization across various datasets, showing only a few misclassifications, even among spectrally similar classes. The proposed model addresses the challenges of spectral feature extraction and enhances classification accuracy by leveraging advanced deep learning techniques. Ablation studies revealed that residual connections improve training stability, while the attention mechanism effectively reduces redundancy. The results suggested that the proposed model could be applied to various fields, including agriculture, environmental monitoring, and land management, with significant improvements in classification accuracy.</p><h2>Other Information</h2> <p> Published in: Remote Sensing Applications: Society and Environment<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.rsase.2025.101779" target="_blank">https://dx.doi.org/10.1016/j.rsase.2025.101779</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3b12357dd10987684e44d48cde507d1e |
| identifier_str_mv | 10.1016/j.rsase.2025.101779 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30820007 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classificationMohamed Sultan Mohamed Ali (17317003)Md Sakib Bin Islam (22804121)Molla E. Majid (21323921)Saad Bin Abul Kashem (17773188)Amith Khandakar (14151981)Muhammad E.H. Chowdhury (17151154)Agricultural, veterinary and food sciencesAgricultural biotechnologyEnvironmental sciencesEnvironmental managementInformation and computing sciencesArtificial intelligenceData management and data scienceHyperspectral imaging (HSI)Remote sensing3D convolutional neural networks (CNNs)Long short-term memory (LSTM)Mixed pixels<p>Hyperspectral imaging (HSI) plays a pivotal role in remote sensing, enabling precise material identification through spectral data across many bands. Despite its advantages, challenges like high dimensionality, spectral mixing, and limited labelled data hinder its classification accuracy. This study introduces a hybrid deep learning model that combines 3D Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, incorporating residual connections and a soft spatial attention mechanism to overcome these limitations. The model utilizes 3D CNNs for the extraction of joint spatial-spectral features, employs LSTMs to capture sequential dependencies across spectral bands, and incorporates soft spatial attention mechanism to emphasize discriminative spatial regions. The model has been evaluated using benchmark datasets—Indian Pines (16 land cover classes) achieved remarkable overall accuracies of 99.66 % and 99.58 % on external validation in Salinas (16 agricultural) dataset, respectively, exceeding the performance of current leading methods. Ablation studies confirmed the essential functions of residual connections in enhancing training stability and soft spatial attention mechanism in mitigating redundant features. The findings indicate strong generalization across various datasets, showing only a few misclassifications, even among spectrally similar classes. The proposed model addresses the challenges of spectral feature extraction and enhances classification accuracy by leveraging advanced deep learning techniques. Ablation studies revealed that residual connections improve training stability, while the attention mechanism effectively reduces redundancy. The results suggested that the proposed model could be applied to various fields, including agriculture, environmental monitoring, and land management, with significant improvements in classification accuracy.</p><h2>Other Information</h2> <p> Published in: Remote Sensing Applications: Society and Environment<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.rsase.2025.101779" target="_blank">https://dx.doi.org/10.1016/j.rsase.2025.101779</a></p>2025-11-04T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.rsase.2025.101779https://figshare.com/articles/journal_contribution/A_hybrid_3D_CNN-LSTM_model_with_soft_spatial_attention_mechanism_for_accurate_hyperspectral_image_classification/30820007CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308200072025-11-04T06:00:00Z |
| spellingShingle | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification Mohamed Sultan Mohamed Ali (17317003) Agricultural, veterinary and food sciences Agricultural biotechnology Environmental sciences Environmental management Information and computing sciences Artificial intelligence Data management and data science Hyperspectral imaging (HSI) Remote sensing 3D convolutional neural networks (CNNs) Long short-term memory (LSTM) Mixed pixels |
| status_str | publishedVersion |
| title | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification |
| title_full | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification |
| title_fullStr | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification |
| title_full_unstemmed | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification |
| title_short | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification |
| title_sort | A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification |
| topic | Agricultural, veterinary and food sciences Agricultural biotechnology Environmental sciences Environmental management Information and computing sciences Artificial intelligence Data management and data science Hyperspectral imaging (HSI) Remote sensing 3D convolutional neural networks (CNNs) Long short-term memory (LSTM) Mixed pixels |