Self-Mutual Information-Based Band Selection for Hyperspectral Image Classification

Due to significant inter-band correlation resulting from the use of hundreds of contiguous spectral bands, band selection (BS) is commonly used to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with crucial self-retained spectral information, while also avoiding highly correlated bands to be selected. This article presents a novel approach, referred to as self-mutual information-based band selection (SMI-BS) for hyperspectral image classification (HSIC) to address these two issues. It first constructs a hyperspectral band channel from a hyperspectral image and then takes advantage of such a band channel to coin a new concept of SMI, which is defined as the mutual information (MI) between a selected band, b, and the set of full bands, ${\boldsymbol{\Omega}} $ , $I$ (b; ${\boldsymbol{\Omega}}$ ). As a result, a curve plotted as a function of $I$ (b; ${\boldsymbol{\Omega}}$ ) versus individual band b, called SMI curve, can be used as a BS criterion which selects those bands with large $I$ (b; ${\boldsymbol{\Omega}}$ ) values as desired bands. Since such selected bands may be highly correlated, another new concept, called prominent band (PB), which is defined as a band corresponding to a prominent peak of an SMI curve, is further introduced to avoid selecting highly inter-correlated spectral bands. To validate the utility of SMI-BS in HSIC, experiments are conducted to compare existing state-of-the-art BS methods for performance evaluation. The results demonstrate that SMI-BS is indeed a very effective BS method and also performs better than other test BS methods.