Maximin distance based band selection for endmember extraction in hyperspectral images using simplex growing algorithm

With the fast growing technologies in the field of remote sensing, hyperspectral image analysis has made a great breakthrough. It provides accurate and detailed information of objects in the image when compared to any other remotely sensed data. It is possible because of its high redundancy in nature. But this redundancy in hyperspectral images leads to high computational complexity in their analysis. Hence Dimensionality Reduction (DR) is a significant task in all hyperspectral image processing. DR can be achieved either by feature extraction or feature selection. Feature selection or Band selection is adopted in this paper because of no compromise in original data. Despite many algorithms that exist for band selection, this paper proposes a new concept of Maximin distance algorithm using Spectral Angle Distance (SAD) as distance measure for band selection. Virtual Dimensionality (VD) is used to provide the number of bands to be selected because it has been proved to be reliable estimate. Simplex Growing Algorithm (SGA) is deployed for endmember extraction in the experiment work. In order to evaluate the performance of the proposed band selection algorithm, the Spectral Angle Distance (SAD) and Spectral Similarity Value (SSV) are used as measures. The efficacy of our proposed algorithm has been proved from experimental results in comparison with Constrained Band Selection (CBS), Similarity Based Band Selection (SBBS), Clustering Based Band Selection (CBBS), Uniform Band Selection (UBS), Minimum Variance Principal Component Analysis (MVPCA) and Exemplar Component Analysis (ECA) and Firefly Algorithm Based Band Selection (FABBS).