Version 1
: Received: 10 December 2017 / Approved: 11 December 2017 / Online: 11 December 2017 (06:55:22 CET)
How to cite:
Ahmadi, S.A.; Mehrshad, N.; Razavi, S.M. Relative Weighted Feature Space for Dimensionality Reduction and Classification of Hyperspectral Images. Preprints2017, 2017120057. https://doi.org/10.20944/preprints201712.0057.v1
Ahmadi, S.A.; Mehrshad, N.; Razavi, S.M. Relative Weighted Feature Space for Dimensionality Reduction and Classification of Hyperspectral Images. Preprints 2017, 2017120057. https://doi.org/10.20944/preprints201712.0057.v1
Ahmadi, S.A.; Mehrshad, N.; Razavi, S.M. Relative Weighted Feature Space for Dimensionality Reduction and Classification of Hyperspectral Images. Preprints2017, 2017120057. https://doi.org/10.20944/preprints201712.0057.v1
APA Style
Ahmadi, S.A., Mehrshad, N., & Razavi, S.M. (2017). Relative Weighted Feature Space for Dimensionality Reduction and Classification of Hyperspectral Images. Preprints. https://doi.org/10.20944/preprints201712.0057.v1
Chicago/Turabian Style
Ahmadi, S.A., Nasser Mehrshad and Seyyed Mohammad Razavi. 2017 "Relative Weighted Feature Space for Dimensionality Reduction and Classification of Hyperspectral Images" Preprints. https://doi.org/10.20944/preprints201712.0057.v1
Abstract
Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.
Copyright:
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.
