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Sensors 2018, 18(3), 780; https://doi.org/10.3390/s18030780

ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

1
School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China
2
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
3
School of Geographical and Earth Sciences, University of Glasgow, Glasgow G12 8QQ, UK
4
China Geological Survey, Beijing 100037, China
*
Authors to whom correspondence should be addressed.
Received: 13 December 2017 / Revised: 25 January 2018 / Accepted: 3 February 2018 / Published: 5 March 2018
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
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Abstract

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively. View Full-Text
Keywords: hyperspectral; classification; training samples with interference; multi-instance learning; diverse density hyperspectral; classification; training samples with interference; multi-instance learning; diverse density
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Li, N.; Xu, Z.; Zhao, H.; Huang, X.; Li, Z.; Drummond, J.; Wang, D. ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery. Sensors 2018, 18, 780.

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