SPECTRAL-SPATIAL CLASSIFICATION OF SPECTRAL IMAGES WITH SUPER PIXEL-BASED DISCRIMINATIVE SPARES MODEL

Author(s):

C.Sudha , Christian College of Engineering and Technology Oddanchatram, Tamilnadu-624619, India; C.Jerlin Ajith, Christian College of Engineering and Technology Oddanchatram, Tamilnadu-624619, India

Keywords:

SBDSM, SVM, MLR

Abstract:

Hyper spectral imaging has been widely used in the remote sensing which can acquire images from hundreds of narrow contiguous bands, spanning the visible-to-infrared spectrum. In the hyper spectral image (HSI), each pixel is a high-dimensional vector and its entries represent the spectral responses of different spectral bands. In the existing system, sparse representation has been also applied in HSI classification, using the observation that hyper spectral pixels approximately lie in a low-dimensional subspace spanned by dictionary atoms from the same class. In this paper the system proposed a super pixel-based discriminative sparse model (SBDSM) to effectively exploit the spatial information of the HSI.

Other Details:

Manuscript Id	:	IJSTEV2I10079
Published in	:	Volume : 2, Issue : 10
Publication Date	:	01/05/2016
Page(s)	:	357-362

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