ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Spectral-Spatial Mineral Classification of Lunar Surface Using Band Parameters with Active Learning

Roy, Sukanta and Subbanna, Sujai and Channagiri, Srinidhii Venkatesh and Raj, Sharath R and Omkar, S N (2017) Spectral-Spatial Mineral Classification of Lunar Surface Using Band Parameters with Active Learning. In: 2nd International Conference on Data Mining and Big Data (DMBD), JUL 27-AUG 01, 2017, Fukuoka, JAPAN, pp. 127-136.

Full text not available from this repository. (Request a copy)
Official URL: http://dx.doi.org/10.1007/978-3-319-61845-6_13

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.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belong to SPRINGER INTERNATIONAL PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Depositing User: Id for Latest eprints
Date Deposited: 27 Aug 2018 14:57
Last Modified: 27 Aug 2018 14:57
URI: http://eprints.iisc.ac.in/id/eprint/60497

Actions (login required)

View Item View Item