Optimized Hyperspectral Vegetation Detection Using Lightweight Cascaded DCNN

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : Sandhya Shinde, Priya Charles, Dipak Mahurkar, D. Hire, Rashmi Deshpande

10.14445/23488549/IJECE-V12I4P124

How to Cite?

Sandhya Shinde, Priya Charles, Dipak Mahurkar, D. Hire, Rashmi Deshpande, "Optimized Hyperspectral Vegetation Detection Using Lightweight Cascaded DCNN," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 4, pp. 251-264, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I4P124

Abstract:

Hyperspectral Image Classification (HIC) is essential for distinguishing surface objects and monitoring materials in remote sensing. However, the large number of spectral bands increases computational complexity and classification time. This study proposes a HIC system that uses Deep Convolutional Neural Networks (DCNN) with spectral band selection strategies. The Indian Pines and Salinas datasets are used for evaluation, employing PCA, LDA, and ILDA for band selection. Performance is assessed using accuracy, recall, precision, F1-score, training time, and classification time. The first phase utilizes a three layered DCNN with PCA for feature representation, achieving 98.20% accuracy on the Indian Pines dataset with 30 spectral bands and a 25×25-pixel window. The second phase introduces a Lightweight Cascaded DCNN (LC-DCNN) with ILDA, enhancing classification accuracy. LC-DCNN+ILDA achieves 99.51% accuracy on Indian Pines and 99.71% on Salinas, outperforming other methods. ILDA proves more effective in selecting discriminative spectral bands than PCA and LDA. In the future, adding bigger datasets with more extensive objects can improve performance for real-time datasets.

Keywords:

Hyperspectral Image Classification (HIC), Lightweight Cascaded DCNN, Convolutional Neural Network, Principle Component Analysis (PCA), Improved linear discriminant analysis.

References:

[1] Maryam Imani, and Hassan Ghassemian, “An Overview on Spectral and Spatial Information Fusion for Hyperspectral Image Classification: Current Trends and Challenges,” Information Fusion, vol. 59, pp. 59-83, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Wenjing Lv, and Xiaofei Wang, “Overview of Hyperspectral Image Classification,” Journal of Sensors, vol. 2020, pp. 1-13, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Sen Jia et al., “A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples,” Neurocomputing, vol. 448, pp. 179-204, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Gizem Ortac, and Giyasettin Ozcan, “Comparative Study of Hyperspectral Image Classification by Multidimensional Convolutional Neural Network Approaches to Improve Accuracy,” Expert Systems with Applications, vol. 182, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Bing Tu et al., “KNN-Based Representation of Superpixels for Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 11, pp. 4032-4047, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yanhui Guo et al., “Hyperspectral Image Classification with SVM and Guided Filter,” EURASIP Journal on Wireless Communications and Networking, vol. 2019, pp. 1-9, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Jianwei Zheng et al., “Hyperspectral Image Classification Using Mixed Convolutions and Covariance Pooling,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 522-534, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] A. Villa et al., “Independent Component Discriminant Analysis for Hyperspectral Image Classification,” 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland, pp. 4865-4876, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Chao Xia et al., “Maize Seed Classification Using Hyperspectral Image Coupled with Multi-Linear Discriminant Analysis,” Infrared Physics & Technology, vol. 103, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Yushi Chen et al., “Deep Learning Ensemble for Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 6, pp. 1882-1897, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Ding Yao et al., “Deep Hybrid: Multi-Graph Neural Network Collaboration for Hyperspectral Image Classificatio,” Defence Technology, vol. 23, pp. 164-176, 2023. [CrossRef] [Google Scholar] [Publisher Link]
[12] Yang-Lang Chang et al., “Consolidated Convolutional Neural Network for Hyperspectral Image Classification,” Remote Sensing, vol. 14, no. 7, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yuan Yuan, Chengze Wang, and Zhiyu Jiang, “Proxy-Based Deep Learning Framework for Spectral–Spatial Hyperspectral Image Classification: Efficient and Robust,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Fuding Xie et al., “Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning,” Remote Sensing, vol. 13, no. 5, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Alou Diakite, Gui Jiangsheng, and Fu Xiaping, “Hyperspectral Image Classification Using 3D 2D CNN,” IET Image Processing, vol. 15, no. 5, pp. 1083-1092, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Saeed Ghaderizadeh et al., “Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 7570-7588, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Hongmin Gao, Zhonghao Chen, and Chenming Li, “Sandwich Convolutional Neural Network for Hyperspectral Image Classification Using Spectral Feature Enhancement,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 3006-3015, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jing Bai et al., “Hyperspectral Image Classification Based on Deep Attention Graph Convolutional Network,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yang-Lang Chang et al., “Consolidated Convolutional Neural Network for Hyperspectral Image Classification,” Remote Sensing, vol. 14, no. 7, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]