Abstract
In the recent days the Hyperspectral images are most popularly used for remote sensing. Hyperspectral imaging has many applications including resource management, mineral exploration, agriculture, environmental monitoring and other tasks for earth observation. Earlier these images are very rarely available. But with recent appearance of Airborne Hyperspectral Imaging System, hyperspectral images entered into main stream of remote sensing. In this work we have considered few officially and publicly available hyperspectral image data. As the image contains spectral, spatial and temporal resolutions in the image, so to classify several regions in the images we have considered the powerful machine learning technique that is Support Vector Machine (SVM) optimized with Stochastic Gradient Descent (SGD) for image classification task.
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Sampurnima, P., Satapathy, S.K., Mishra, S., Mallick, P.K. (2020). Hyperspectral Image Classification Using Stochastic Gradient Descent Based Support Vector Machine. In: Dehuri, S., Mishra, B., Mallick, P., Cho, SB., Favorskaya, M. (eds) Biologically Inspired Techniques in Many-Criteria Decision Making. BITMDM 2019. Learning and Analytics in Intelligent Systems, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-39033-4_8
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DOI: https://doi.org/10.1007/978-3-030-39033-4_8
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