Derivative-based band clustering and multi-agent PSO optimization for optimal band selection of hyper-spectral images

Images (HSIs) are popular in diversified applications, such as geosciences, biomedical imaging, molecular biology, agriculture, astronomy, food quality and safety assessment, surveillance and physics-related research. The rich spatial and spectral information of HSI is the key factors for robust representation of class-specific objects, in remote sensing applications. But, these images often suffer from Hughes effect. This effect causes the recording of information about a single scene in multiple spectral bands. This demands a dimensionality reduction step, which can either be a feature reduction/extraction or a feature selection. The feature selection process is commonly called band selection (BS) in the HS data set. The current study proposes an unsupervised BS technique, which is accomplished in three steps, including preprocessing of spectral bands, adjacent band clustering, and multi-agent optimization. Spatio-spectral (using a simple Gaussian filter) information is extracted to evaluate the performance using SVM classifier with different state-of-the-art band selection approaches. The performance of the proposed approach is evaluated for metrics including overall accuracy (OA), average accuracy (AA) and Kappa ( κ ). The experimental results are promising as these surpass that of other approaches.