A Combination of Spectral-Spatial Detection Methods of Hyperspectral Images for the Better Separation of Special Building's Roofs in Urban Area

Recently, hyperspectral images analysis has obtained successful results from information extraction in urban areas. Building detection is one of the important applications in processing hyperspectral images. In order to detect complete and precise building information from hyperspectral data, advanced data analysis methods are required. Algorithms based on spectral-identification are sensitive to spectral variability and noise in acquisition. In most cases, the spectral signature is unknown, so each pixel is separately examined and if it significantly differs from the background, it is regarded as an object. On the other hand, there are many algorithms e.g. Spectral Angle Measure (SAS), Spectral Correlation Similarity (SCS), Spectral Information Divergence (SID), Jeffries-Matusita Distance (JMD), Constrained Energy Minimizing (CEM) and Covariance-based Matched Filter Measure (CMFM) for building detection. In this study, first we employed the SAS, SCS, SID, JMD, CEM and CMFM algorithms for building detection. Then, in the next step to improve the spectral detection algorithms, two strategies, the combining algorithms using Adaptive Neuro-Fuzzy Inference System (ANFIS) method and spectral-spatial detection, was employed. Our experiments results demonstrate a significant improvement of accuracy using proposed strategies on two CASI hyperspectral images taken from an urban area.