Abstract
In the field of remote sensing, the value of the large number of hyper spectral bands during classification is well documented. The collection of labeled samples is a costly affair and many semi-supervised classification methods are introduced that can make use of unlabeled samples for training. Due to the nature of these images, high dimensional spectral features must be distinctive with preservation of absorption band in mineral mapping. We propose the method in which we consider the band parameters of the spectral data combined with the neighborhood spatial information for mineral classification using Active Labeling to compensate for the lack of a large number of labeled samples. Here we demonstrate that by using these parameters for classification in conjunction with their spatial information, higher accuracies can be achieved during classification.
S. Roy—Pursuing PhD in Department of Aerospace Engineering at IISc, Bangalore, Karnataka, India-560012.
S. Subbanna, S. Venkatesh Channagiri and S.R. Raj—Pursuing B.E in Computer Science & Engineering at BNMIT, Bangalore, Karnataka, India-560070.
S.N. Omkar—Chief Research Scientist in Department of Aerospace Engineering at IISc, Bangalore, Karnataka, India-560012.
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Roy, S., Subbanna, S., Venkatesh Channagiri, S., R Raj, S., S.N, O. (2017). Spectral-Spatial Mineral Classification of Lunar Surface Using Band Parameters with Active Learning. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_13
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DOI: https://doi.org/10.1007/978-3-319-61845-6_13
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