Prabukumar M

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Sawant S.S

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

The Egyptian Journal of Remote Sensing and Space Science

The presence of irrelevant and highly correlated spectral bands significantly reduces the classification accuracy of the hyperspectral images. Therefore, the selection of suitable bands from the set of available spectral bands plays a crucial role in improving the classification accuracy. In this paper, a novel band selection approach is proposed based on nature inspired meta-heuristic algorithm to mitigate the effect of curse of dimensionality. Wind-driven optimization (WDO), among other meta-heuristic algorithms, has proven to be more efficient in solving global optimization problems. However, WDO is prone to premature convergence when solving the global optimization problem due to loss of diversity of air particles. Therefore, a modified WDO (MWDO) is proposed for band selection, which is able to avoid the premature convergence and control the explorationâexploitation search trade-off. Finally, in order to further improve the performance of the classification, the selected bands are fed into the deep learning architecture to extract the high-level useful features. The experiments are carried on three widely used standard datasets such as Indian Pines, Pavia University, and Salinas. The experimental results show that the proposed approach selects an optimal subset of bands with good convergence characteristics and provide high classification accuracy with fewer bands in comparison with other approaches. The proposed method achieves an overall accuracy of 93.26%, 94.76%, and 95.96% for Indian Pines, Pavia University, and Salinas datasets, respectively.

International Journal of Remote Sensing

New framework for hyperspectral band selection using modified wind-driven optimization algorithm

Unsupervised band selection based on weighted information entropy and 3D discrete cosine transform for hyperspectral image classification

Journal of Applied Remote Sensing

Band clustering using expectation–maximization algorithm and weighted average fusion-based feature extraction for hyperspectral image classification

2017 Innovations in Power and Advanced Computing Technologies (i-PACT)

Semi-supervised techniques based hyper-spectral image classification: A survey

IEEE Geoscience and Remote Sensing Letters

Semisupervised Hyperspectral Image Classification Using Small Sample Sizes

Pattern Recognition

A Survey on semi-supervised feature selection methods

IEEE Transactions on Geoscience and Remote Sensing

Sparse Graph Regularization for Hyperspectral Remote Sensing Image Classification

A review on graph-based semi-supervised learning methods for hyperspectral image classification

Journal	Data powered by TypesetThe Egyptian Journal of Remote Sensing and Space Science
Publisher	Data powered by TypesetElsevier BV
ISSN	1110-9823
Open Access	Yes