Spectral clustering and spatial Frobenius norm-based Jaya optimisation for BS of hyperspectral images

The hyperspectral images (HSIs) often suffer from Hughes effect, as it records information of a single scene in several spectral bands. This can be mitigated by reducing the dimension of HSI. A novel framework for hybrid band selection (BS) is proposed in this work. The proposed technique is a multi-objective approach, which incorporates clustering (spectral) and intra-band (spatially filtered) de-correlation measure (Frobenius norm) as maximisation of two cost functions. Heuristic optimisers are very sensitive to their associated hyperparameters. So, in the proposed architecture, Jaya optimisation is used for BS, as it does not possess any algorithm-specific control parameter. Spatial and spectral features are extracted for both BS and classification (using support vector machine) for evaluating the effectiveness of the proposed/ experimented approaches. The evaluated performance measures are overall accuracy, average accuracy, and kappa (K). The experimental result shows that the proposed BS approach is better or competent with other experimented state-of-the-art methods. The advantages of the proposed framework can be stated as: (i) spectrally distinct and spatially invariant objective formulation; (ii) Jaya optimisation with minimal control parameters; (iii) optimised ranking for more accurate BS; and (iv) performing classification using spatial-spectral features for further band reduction with the desired accuracy.