Abstract
The emergence of deep learning models has paved way for solving many real world problems. Hyperspectral image classification has its significance in very critical applications associated with defence and agricultural research. The existing methods showed that there is need for improving quality in hyperspectral image classification for computer vision applications. In this paper, we proposed a framework known as Hyperspectral Image Classification Framework (HICF) with underlying mechanisms. We proposed an algorithm known as deep learning-based hyperspectral image classification (DL-HIF) for improving classification of hyperspectral images. A prototype is built using Python data science platform for evaluating the performance of the proposed algorithm. Our experimental results are compared with two existing Machine Learning (ML) models such as Support Vector Machine (SVM) and Neural Network (NN). The proposed DL-HIF outperforms the state-of-the-art models.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Gongalla, L., Sudha, S.V. (2023). A Deep Learning Framework for Classification of Hyperspectral Images. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_15
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DOI: https://doi.org/10.1007/978-981-19-5443-6_15
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