Prabukumar M

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Shrutika S Sawant

Sawant S.S

Manoharan Prabukumar

Sathishkumar Samiappan

Samiappan S.

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

A Band Selection Method For Hyperspectral Image Classification Based On Cuckoo Search Algorithm With Correlation Based Initialization

2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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

Journal of Applied Remote Sensing

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

Journal of Signal Processing Systems

A Band Selection Method for Hyperspectral Image Based on Particle Swarm Optimization Algorithm with Dynamic Sub-Swarms

Applied Soft Computing

Gray Wolf Optimizer for hyperspectral band selection

IEEE Transactions on Geoscience and Remote Sensing

A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection

Journal	Data powered by Typeset2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Publisher	Data powered by TypesetIEEE
Open Access	0