Classification of  Data By Morphological Analysis

This paper presents a new spectral-spatial classification method for hyperspectral images via morphological component analysis-based imageseparation rationale in sparse representation. The method consists of three main steps. First, the high-dimensional spectral domain of hyperspectral images is reduced into a low-dimensional feature domain by using minimum noise fraction (MNF). Second, the proposed separation method is acted on each features to generate the morphological components (MCs), i.e., the content and texture components. To this end, the dictionaries for these two components are built by using local curvelet and Gabor wavelet transforms within the randomly chosen imagepartitions.

Then, sparse coding of one of the MCs and update of the associated dictionary are sequentially performed with the other one fixed. To better direct the separation process, an undecimated Haar wavelet with soft threshold is performed for the content component to make it smooth. This process is repeated until some stopping criterion is met. Finally, a support vector machine is adopted to obtain the classification maps based on the MCs. The experimental results with hyperspectralimages collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory’s Airborne Visible/Infrared ImagingSpectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed scheme provides better performance when compared with other widely used methods.