{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T14:20:45Z","timestamp":1768746045799,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,25]],"date-time":"2024-02-25T00:00:00Z","timestamp":1708819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (CNNs), despite their architectural constraints, offers a promising solution for precise spectral data classification. However, challenges persist in object classification in hyperspectral imagery or hyperspectral image classification, including the curse of dimensionality, data redundancy, overfitting, and computational costs. To tackle these hurdles, we introduce the spectrally segmented-enhanced neural network (SENN), a novel model integrating segmentation-based, multi-layer CNNs, SVM classification, and spectrally segmented dimensionality reduction. SENN adeptly integrates spectral\u2013spatial data and is particularly crucial for agricultural land classification. By strategically fusing CNNs and support vector machines (SVMs), SENN enhances class differentiation while mitigating overfitting through dropout and early stopping techniques. Our contributions extend to effective dimensionality reduction, precise CNN-based classification, and enhanced performance via CNN-SVM fusion. SENN harnesses spectral information to surmount challenges in \u201chyperspectral image classification in hyperspectral imagery\u201d, marking a significant advancement in accuracy and efficiency within this domain.<\/jats:p>","DOI":"10.3390\/rs16050807","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T10:40:17Z","timestamp":1708944017000},"page":"807","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9437-8299","authenticated-orcid":false,"given":"Touhid","family":"Islam","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3475-4217","authenticated-orcid":false,"given":"Rashedul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4429-6590","authenticated-orcid":false,"given":"Palash","family":"Uddin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"},{"name":"School of Information Technology, Deakin University, Geelong 3220, Australia"}]},{"given":"Anwaar","family":"Ulhaq","sequence":"additional","affiliation":[{"name":"School of Engineering & Technology, Central Queensland University Australia, 400 Kent Street, Sydney 2000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, H., Yao, W., Cheng, L., and Li, B. (2021). Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13071248"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Teke, M., Deveci, H.S., Halilo\u011flu, O., G\u00fcrb\u00fcz, S.Z., and Sakarya, U. (2013, January 12\u201314). A Short Survey of Hyperspectral Remote Sensing Applications in Agriculture. Proceedings of the Recent Advances in Space Technologies (RAST), Istanbul, Turkey.","DOI":"10.1109\/RAST.2013.6581194"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TGRS.2014.2358934","article-title":"A Survey on Spectral\u2013Spatial Classification Techniques Based on Attribute Profiles","volume":"53","author":"Ghamisi","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MSP.2013.2279179","article-title":"Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods","volume":"31","author":"Tuia","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1109\/TGRS.2017.2786718","article-title":"Joint Reconstruction and Anomaly Detection from Compressive Hyperspectral Images Using Mahalanobis Distance-Regularized Tensor RPCA","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pyo, J., Duan, H., Ligaray, M., Kim, M., Baek, S., Kwon, Y.S., Lee, H., Kang, T., Kim, K., and Cha, Y. (2020). An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12071073"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"127167","DOI":"10.1109\/ACCESS.2020.3008029","article-title":"SSDANet: Spectral-Spatial Three-Dimensional Convolutional Neural Network for Hyperspectral Image Classification","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","unstructured":"Karamizadeh, S., Abdullah, S.M., Manaf, A.A., Zamani, M., Hooman, A., and Publishing, S.R. (2023, November 20). An Overview of Principal Component Analysis. Available online: https:\/\/www.scirp.org\/journal\/paperinformation.aspx?paperid=38103."},{"key":"ref_9","first-page":"337","article-title":"PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification","volume":"38","author":"Mamun","year":"2021","journal-title":"IETE Tech. Rev."},{"key":"ref_10","unstructured":"Joelsson, S.R., Benediktsson, J.A., and Sveinsson, J.R. (2005, January 29). Random forest classifiers for hyperspectral data. Proceedings of the 2005 International Geoscience and Remote Sensing Symposium (IGARSS \u201805), Seoul, Republic of Korea."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Leng, J., Li, T., Bai, G., Dong, Q., and Dong, H. (2016, January 6\u20138). Cube-CNN-SVM: A Novel Hyperspectral Image Classification Method. Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), San Jose, CA, USA.","DOI":"10.1109\/ICTAI.2016.0158"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103296","DOI":"10.1016\/j.infrared.2020.103296","article-title":"Hyperspectral image classification using CNN with spectral and spatial features integration","volume":"107","author":"Vaddi","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_13","unstructured":"Ebied, H.M. (2012, January 14\u201316). Feature extraction using PCA and Kernel-PCA for face recognition. Proceedings of the 2012 8th International Conference on Informatics and Systems (INFOS), Giza, Egypt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110218","DOI":"10.1016\/j.patcog.2023.110218","article-title":"Latent Linear Discriminant Analysis for feature extraction via Isometric Structural Learning","volume":"149","author":"Zhou","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_15","unstructured":"Cristianini, N. (2004). Dictionary of Bioinformatics and Computational Biology, Wiley."},{"key":"ref_16","unstructured":"Kishore, K.M.S., Behera, M.K., Chakravarty, S., and Dash, S. (2020, January 26-27). Hyperspectral Image Classification using Minimum Noise Fraction and Random Forest. Proceedings of the 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, J.-Z., Yan, W.-D., Ni, W.-P., and Bian, H. (2013, January 21\u201326). Feature extraction for hyperspectral data based on MNF and singular value decomposition. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium\u2014IGARSS, Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6723053"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Islam, M.T., Islam, M.R., Uddin, M.P., and Ulhaq, A. (2023). A Deep Learning-Based Hyperspectral Object Classification Approach via Imbalanced Training Samples Handling. Remote Sens., 15.","DOI":"10.3390\/rs15143532"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Islam, M.R., Ahmed, B., Hossain, M.A., and Uddin, M.P. (2023). Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification. Sensors, 23.","DOI":"10.3390\/s23020657"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MGRS.2021.3075491","article-title":"Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review","volume":"10","author":"Peng","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhong, S., Chang, C.-I., and Zhang, Y. (2018, January 7\u201310). Iterative Support Vector Machine for Hyperspectral Image Classification. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451145"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107298","DOI":"10.1016\/j.patcog.2020.107298","article-title":"Deep support vector machine for hyperspectral image classification","volume":"103","author":"Okwuashi","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, Q., Xiao, Y., Wang, D., and Luo, B. (2020). Csa-mso3dcnn: Multiscale octave 3d cnn with channel and spatial attention for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12010188"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kanthi, M., Sarma, T.H., and Bindu, C.S. (2020, January 1\u20134). A 3d-Deep CNN Based Feature Extraction and Hyperspectral Image Classification. Proceedings of the 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), Online.","DOI":"10.1109\/InGARSS48198.2020.9358920"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Zhou, T., Peng, Y., and Tang, Y. (2020, January 24\u201325). 3D Convolutional Neural Network for Hyperspectral Image Classification Using Generative Adversarial Network. Proceedings of the 2020 13th International Conference on Intelligent Computation Technology and Automation (ICICTA), Xi\u2019an, China.","DOI":"10.1109\/ICICTA51737.2020.00065"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4420","DOI":"10.1109\/TGRS.2018.2818945","article-title":"3-D Deep Learning Approach for Remote Sensing Image Classification","volume":"56","author":"Benoit","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Islam, M.R., Islam, M.T., and Uddin, M.P. (2023). Improving hyperspectral image classification through spectral-spatial feature reduction with a hybrid approach and deep learning. J. Spat. Sci., 1\u201318.","DOI":"10.1080\/14498596.2023.2227948"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Firat, H., and Hanbay, D. (2021, January 9\u201311). Classification of Hyperspectral Images Using 3D CNN Based ResNet50. Proceedings of the 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey.","DOI":"10.1109\/SIU53274.2021.9477899"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN Feature Hierarchy for Hyperspectral Image Classification","volume":"17","author":"Roy","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","unstructured":"Chakraborty, T., and Trehan, U. (2021). SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, L., Shi, Z., Pan, B., Zhang, N., Luo, H., and Lan, X. (2020). Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification. Remote Sens., 12.","DOI":"10.3390\/rs12020280"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017). Pyramid Scene Parsing Network. arXiv.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., and Funkhouser, T. (2017). Dilated Residual Networks. arXiv.","DOI":"10.1109\/CVPR.2017.75"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100316","DOI":"10.1016\/j.atech.2023.100316","article-title":"A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images","volume":"5","author":"Noshiri","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alkhatib, M.Q., Al-Saad, M., Aburaed, N., Almansoori, S., Zabalza, J., Marshall, S., and Al-Ahmad, H. (2023). Tri-CNN: A Three Branch Model for Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15020316"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Islam, M.T., Kumar, M., Islam, M.R., and Sohrawordi, M. (2022, January 17\u201319). Subgrouping-Based NMF with Imbalanced Class Handling for Hyperspectral Image Classification. Proceedings of the 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ICCIT57492.2022.10055177"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Islam, M.T., Kumar, M., and Islam, M.R. (2022, January 29\u201331). MC-NET: Spectral-Spatial Feature Reduction for Hyperspectral Image Classification with Optimized Technique Series. Proceedings of the 2022 4th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), Rajshahi, Bangladesh.","DOI":"10.1109\/ICECTE57896.2022.10114513"},{"key":"ref_41","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.3390\/rs15041147","article-title":"Hyperspectral Image Classification via Information Theoretic Dimension Reduction","volume":"15","author":"Islam","year":"2023","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/807\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:04:46Z","timestamp":1760105086000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/807"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,25]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050807"],"URL":"https:\/\/doi.org\/10.3390\/rs16050807","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,25]]}}}