Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Parthiban, Ilamathi; Somanathan, Chidambaram Title A name given to the resource Mangrove area classification in Pichavaram using Hyperspectral Imaging and Optimized Channel-Level Residual CNN framework Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Wetlands Ecology and Management;Volume;34;Issue;2;Article No.;16; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11273-026-10118-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11273-026-10118-y</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030571136?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030571136?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Parthiban I., Department of Electronics and Communication Engineering, Christ University, Karnataka, Bangalore, India; Somanathan C., Department of Electronics and Communication Engineering, Christ University, Karnataka, Bangalore, India Description An account of the resource The Pichavaram mangrove forest in Tamil Nadu is one of Indias most ecologically significant regions, supporting coastal health and local communities. However, effective mangrove area classification remains challenging due to field inaccessibility and inefficiency of traditional assessment methods, highlighting the demand for advanced solutions. As the existing remote sensing-based studies suffer from limited classification accuracy and high computational complexity, this study combined Hyperspectral Image (HSI) with an Optimized Channel Level Residual CNN (OC-LRCNN) model for improved results in mangrove-related research. The proposed model employs unsupervised feature extraction to capture essential patterns with minimal training data while channel-level residual connections enhance discriminative feature selection and reduce spectral redundancy. Utilizing the Pichavaram EO-1 Hyperion and AVIRIS-NG datasets, the proposed model is compared with traditional CNN, state-of-the-art deep learning architectures (VGG, ResNet, DenseNet) and machine learning methods like SVM and RF. The OC-LRCNN achieved classification accuracies of 98.2% and 99.0% for the Hyperion and AVIRIS-NG datasets with consistently high precision, recall, F1-score and kappa values. These findings demonstrate the models effectiveness in reliable mangrove classification and monitoring applications. The Author(s), under exclusive licence to Springer Nature B.V. 2026. Subject The topic of the resource AVIRIS-NG; EO-1 Hyperion; Hyperspectral Image (HSI); Mangrove forest classification; Optimized Channel-Level Residual CNN (OC-LRCNN) model; Pichavaram area Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Relation A related resource ISSN: 9234861; CODEN: WEMAE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online