Hyperspectral Imagery Classification Using Technologies of Computational Intelligence

Texture information is exploited for classification of HSI (Hyperspectral Imagery) at high spatial resolution. For this purpose, framework employs to LBP (Local Binary Pattern) to extract local image features such as edges, corners & spots. After the extraction of LBP feature two levels of fusions are applied along with Gabor feature & spectral feature, i.e. Feature level fusion & Decision level fusion. In Feature level fusion multiple features are concurred before pattern classification. While in decision level fusion, it works on probability output of each individual classification pipeline combines the distinct decisions into final one. Decision level fusion consists of either hard fusion, soft fusion method. In hard fusion we consider majority part & in soft fusion linear logarithmic opinion pool at probability level (LOGP). In addition to this, extreme learning machine (ELM) classifier is included which is more efficient than support vector machine (SVM), used to provide probability classification output. It has simple structure with one hidden layer & one linear output layer. ELM trained much faster than SVM.