ISBDD model for classification of hyperspectral remote sensing imagery

Li, N., Xu, Z., Zhao, H., Huang, X., Li, Z., Drummond, J. and Wang, D. (2018) ISBDD model for classification of hyperspectral remote sensing imagery. Sensors, 18(3), 780. (doi: 10.3390/s18030780) (PMID:29510547)

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.

• Item Type: Articles
• Additional Information: This work was supported by the National High Technology Research and Development Program (863 Program) (Grant No. 2012YQ05250 and No. 2016YFF0103604), National Key Technologies R&D Program (Grant No. 2016YFB0500505), National Natural Science Foundation of China (Grant No. 41402293), Program for Changjiang Scholars and Innovative Research Team (Grant No. IRT0705), China Scholarship Council (Ref No. 201606025034) and the UK Science and Technology Facilities Council (STFC) through the PAFiC project (Ref: ST/N006801/1).
• Keywords: Classification, diverse density, hyperspectral, multi-instance learning, training samples with interference.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Dr Zhenhong and Drummond, Dr Jane
• Authors: Li, N., Xu, Z., Zhao, H., Huang, X., Li, Z., Drummond, J., and Wang, D.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• Published Online: 05 March 2018
• Copyright Holders: Copyright © 2018 The Authors
• First Published: First published in Sensors 18(3): 780
• Publisher Policy: Reproduced under a Creative Commons License