Abstract
In this paper the classification of hyperspectral images is investigated by using a supervised approach. The spectral feature are extracted with well known decision boundary feature extraction (DBFE) and non-parametric weighted feature extraction (NWFE) techniques. The most informative features are fed to random forest (RF) classifier to perform pixel-wise classification. The experiments are carried out on two benchmark hyperspectral images. The results show that RF classifier generates good classification accuracies for hyperspectral image with smaller execution time. Among feature extraction techniques, DBFE has produced better results than NWFE.
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Acknowledgement
Authors are very thankful to Dr. David Landgrebe, Purdue University, for providing Multispec tool and Dr. Paolo Gamba, Professor at PU, Italy, for providing Hyperspectral dataset used in this research work.
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Mourya, D., Bhatt, A. (2018). Classification of Hyperspectral Imagery Using Random Forest. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_5
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DOI: https://doi.org/10.1007/978-981-10-8657-1_5
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