Classification of Hyperspectral and Multispectral Images by Using Fractal Dimension of Spectral Response Curve

One of the most important problems in classification of Hyperspectral images is Hughes phenomena. The main reason for this problem is high dimension feature space anddeficiency of training samples. To solve this kind of problem, so many methods are applied which some of them emphasizefeature reduction and the others point to classification method with low sensitivity to training samples or consider spatial information or semi supervised training samples. In featurereduction algorithms intensity vectors of pixels are used individually. Additionally, in most of such techniques eachcomponent of intensity vector is considered as an individual element without attention to inner relation among them. In this paper we consider members of intensity vector as sampling points of spectral reflecting curve (SRC) of corresponding land cover (LC) and use fractal features of SRC in order to classify the picture. Moreover, we will show using these features improve the classification results in multispectral images.

Keywords:

Spectral signature , Spectral reflectance curve , Hyperspectral images , Multispectral images , Fractal dimension , classification , feature reduction