Mangrove area classification in Pichavaram using Hyperspectral Imaging and Optimized Channel-Level Residual CNN framework
- Title
- Mangrove area classification in Pichavaram using Hyperspectral Imaging and Optimized Channel-Level Residual CNN framework
- Creator
- Parthiban, Ilamathi; Somanathan, Chidambaram
- Description
- 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.
- Source
- Wetlands Ecology and Management;Volume;34;Issue;2;Article No.;16;
- Date
- 01-01-2026
- Publisher
- Springer Science and Business Media B.V.
- Subject
- AVIRIS-NG; EO-1 Hyperion; Hyperspectral Image (HSI); Mangrove forest classification; Optimized Channel-Level Residual CNN (OC-LRCNN) model; Pichavaram area
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 9234861; CODEN: WEMAE
- Format
- online
- Language
- English
- Type
- Article
Collection
Citation
Parthiban, Ilamathi; Somanathan, Chidambaram, “Mangrove area classification in Pichavaram using Hyperspectral Imaging and Optimized Channel-Level Residual CNN framework,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/21962.