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2019 | OriginalPaper | Chapter

Hyperspectral Image Classification Using Semi-supervised Random Forest

Authors : Sunit Kumar Adhikary, Sourish Gunesh Dhekane

Published in: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

Publisher: Springer International Publishing

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Abstract

In this paper, a hyperspectral image classification technique is proposed using semi-supervised random forest (SSRF). Robust node splitting in the random forest requires enormous training data, which is scarce in remote sensing applications. In order to overcome this drawback, we propose utilizing unlabeled data in conjunction with labeled data to assist the splitting process. Moreover, in order to tackle the curse of dimensionality associated with a hyperspectral image, we explore nonnegative matrix factorization (NMF) to remove redundant information. Experimental results confirm the efficacy of the proposed method.

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Metadata
Title
Hyperspectral Image Classification Using Semi-supervised Random Forest
Authors
Sunit Kumar Adhikary
Sourish Gunesh Dhekane
Copyright Year
2019
Book
Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

Print ISBN: 978-3-030-00664-8

Electronic ISBN: 978-3-030-00665-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-00665-5_102