Comparative Study of Various Algorithms on Hyperspectral Data
Keywords:
Hyperspectral, Support Vector Classification (SVC),, effectiveness, algorithmsAbstract
Hyperspectral remote sensing data, captured across a broad spectrum, provides rich information for various applications, which is including land cover classification and the environmental monitoring. In this research paper, we conduct a comprehensive comparative study of machine learning and deep learning algorithms on three widely used hyperspectral datasets: Indian Pines, Pavia University, and Salinas. For machine learning, Support Vector Classification (SVC) and Random Forest algorithms were selected due to their proven effectiveness in classification tasks. Additionally, we explored deep learning techniques by implementing 1D CNN and 2D CNN models, leveraging the spatial and spectral characteristics inherent in hyperspectral data. Our experimental results reveal that among the machine learning algorithms, Random Forest demonstrated competitive performance, while SVC exhibited commendable accuracy. However, the deep learning models, particularly the 2D CNN architecture, outperformed the traditional machine learning algorithms, achieving an impressive accuracy of 98%. This outcome highlights the capability of deep learning models, specifically designed to capture spatial patterns in hyperspectral data, to provide superior classification accuracy.
Downloads
References
Geospatial World. (2010). High Resolution Remote Sensing Sensors. [online] Available at:https://www.geospatialworld.net/article/high-resolution-remote-sensing-sensors/.
Shrestha, R. and Hardeberg, J.Y., 2014, November. Evaluation and comparison of multispectral imaging systems. In Color and Imaging Conference (Vol. 2014, No. 2014, pp. 107-112). Society for Imaging Science and Technology.
Yokoya, N., Grohnfeldt, C. and Chanussot, J., 2017. Hyperspectral and multispectral data fusion: A comparative review of the recent literature. IEEE Geoscience and Remote Sensing Magazine, 5(2), pp.29-56.
Abdulraheem, M.I., Zhang, W., Li, S., Moshayedi, A.J., Farooque, A.A. and Hu, J., 2023. Advancement of Remote Sensing for Soil Measurements and Applications: A Comprehensive Review. Sustainability, 15(21), p.15444.
Lv, W. and Wang, X., 2020. Overview of hyperspectral image classification. Journal of Sensors, 2020.
Stuart, M.B., McGonigle, A.J. and Willmott, J.R., 2019. Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems. Sensors, 19(14), p.3071.
Shimoni, M., Haelterman, R. and Perneel, C., 2019. Hyperspectral imaging for military and security applications: Combining myriad processing and sensing techniques. IEEE Geoscience and Remote Sensing Magazine, 7(2), pp.101- 117.
Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R. and Sousa, J.J., 2017. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote sensing, 9(11), p.1110.
Cui, R., Yu, H., Xu, T., Xing, X., Cao, X., Yan, K. and Chen, J., 2022. Deep learning in medical hyperspectral images: A review. Sensors, 22(24), p.9790.
Makantasis, K., Karantzalos, K., Doulamis, A. and Doulamis, N., 2015, July. Deep supervised learning for hyperspectral data classification through convolutional neural networks. In 2015 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 4959-4962). IEEE.
Lee, H. and Kwon, H., 2017. Going deeper with contextual CNN for hyperspectral image classification. IEEE Transactions on Image Processing, 26(10), pp.4843-4855.
Bai, J., Ding, B., Xiao, Z., Jiao, L., Chen, H. and Regan, A.C., 2021. Hyperspectral image classification based on deep attention graph convolutional network. IEEE Transactions on Geoscience and Remote Sensing, 60, pp.1-16.
Gao, H., Chen, Z. and Li, C., 2021. Sandwich convolutional neural network for hyperspectral image classification using spectral feature enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp.3006-3015.
Ghaderizadeh, S., Abbasi-Moghadam, D., Sharifi, A., Zhao, N. and Tariq, A., 2021. Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp.7570-7588.
Song, W., Li, S., Fang, L. and Lu, T., 2018. Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing, 56(6), pp.3173-3184.
Ahmad, M., Mazzara, M. and Distefano, S., 2021. Regularized CNN feature hierarchy for hyperspectral image classification. Remote Sensing, 13(12), p.2275.
He, X. and Chen, Y., 2019. Optimized input for CNN-based hyperspectral image classification using spatial transformer network. IEEE Geoscience and Remote Sensing Letters, 16(12), pp.1884-1888.
Ahmad, M., Shabbir, S., Raza, R.A., Mazzara, M., Distefano, S. and Khan, A.M., 2021. Hyperspectral image classification: Artifacts of dimension reduction on hybrid CNN. arXiv preprint arXiv:2101.10532.
Roy, S.K., Krishna, G., Dubey, S.R. and Chaudhuri, B.B., 2019. HybridSN: Exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 17(2), pp.277-281.
Vaddi, R. and Manoharan, P., 2020. Hyperspectral image classification using CNN with spectral and spatial features integration. Infrared Physics & Technology, 107, p.103296.
Fan, X., Chen, L., Xu, X., Yan, C., Fan, J. and Li, X., 2023. Land Cover Classification of Remote Sensing Images Based on Hierarchical Convolutional Recurrent Neural Network. Forests, 14(9), p.1881.
Hong, D., Han, Z., Yao, J., Gao, L., Zhang, B., Plaza, A. and Chanussot, J., 2021. SpectralFormer: Rethinking hyperspectral image classification with transformers. IEEE Transactions on Geoscience and Remote Sensing, 60, pp.1- 15.
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P. and Benediktsson, J.A., 2019. Deep learning for hyperspectral image classification: An overview. IEEE Transactions on Geoscience and Remote Sensing, 57(9), pp.6690-6709.
Qing, C., Ruan, J., Xu, X., Ren, J. and Zabalza, J., 2019. Spatial‐spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling. IET Image Processing, 13(2), pp.235-245.
Gao, Q., Lim, S. and Jia, X., 2018. Hyperspectral image classification using convolutional neural networks and multiple feature learning. Remote Sensing, 10(2), p.299.
Chen, Y., Jiang, H., Li, C., Jia, X. and Ghamisi, P., 2016. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing, 54(10), pp.6232-6251.
Petersson, H., Gustafsson, D. and Bergstrom, D., 2016, December. Hyperspectral image analysis using deep learning—A review. In 2016 sixth international conference on image processing theory, tools and applications (IPTA) (pp. 1-6). IEEE.
www.ehu.eus. (n.d.). Hyperspectral Remote Sensing Scenes - Grupo de Inteligencia Computacional (GIC). [online] Available at: https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.
A. Abdollahi, B. Pradhan, S. Gite and A. Alamri, "Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture," in IEEE Access, vol. 8, pp. 209517-209527, 2020, doi: 10.1109/ACCESS.2020.3038225.
Abdollahi, A., Pradhan, B., Gite, S., & Alamri, A. (2020). Building footprint extraction from high resolution aerial images using generative adversarial network (GAN) architecture. IEEE Access, 8, 209517-209527.
Joshi, A.; Pradhan, B.; Gite, S.; Chakraborty, S. Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review. Remote Sens. 2023, 15, 2014. https://doi.org/10.3390/rs15082014
Serbouti, I.; Raji, M.; Hakdaoui, M.; El Kamel, F.; Pradhan, B.; Gite, S.; Alamri, A.; Maulud, K.N.A.; Dikshit, A. Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers. Remote Sens. 2022, 14, 5498. https://doi.org/10.3390/rs14215498
S. Gite, B. Pradhan, A. Alamri and K. Kotecha, "ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks," in IEEE Access, vol. 9, pp. 99312-99326, 2021, doi: 10.1109/ACCESS.2021.3096032.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
