Description

ABSTRACT

Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyperspectral images(HSI) is limited, due to sensor limitations. In many applications,HSI with a high spectral as well as spatial resolution are required.In this paper, a new method for spatial resolution enhancementof a HSI using spectral unmixing and sparse coding (SUSC) isintroduced. The proposed method fuses high spectral resolutionfeatures from the HSI with high spatial resolution features from anMSI of the same scene. Endmembers are extracted from the HIS by spectral unmixing, and the exact location of the endmembersis obtained from the MSI. This fusion process by using spectral unmixing is formulated as an ill-posed inverse problem whichrequires a regularization term in order to convert it into a wellposedinverse problem. As a regularizer, we employ sparse coding(SC), for which a dictionary is constructed using high spatialresolution MSI or PAN images from unrelated scenes. The proposedalgorithm is applied to real Hyperion and ROSIS datasets.Compared with other state-of-the-art algorithms based on pansharpening, spectral unmixing, and SC methods, the proposedmethod is shown to significantly increase the spatial resolution while preserving the spectral content of the HSI.

EXISTING METHOD

• Averagingand Maximization methods based spatial level fusion
• Thresholding and K means clustering methods for segmentation

DRAWBACKS

• Contrast information loss due to averaging method
• Maximization method sensitive to sensor noise and high spatial distortion
• K means – It is not suitable for all lighting condition of images
• Difficult to measure the cluster quality

PROPOSED METHOD

• Dual Level Wavelet and Log Ration Transform
• Detection of Back scattering Changes at Building Scale            
• Building Detection using NN classifier

BLOCK DIAGRAM

ADVANTAGES

• Accurate detection of foreground changes by fusion
• Less sensitive to noises

APPLICATIONS

• Earth land changes detection in satellite field
• Medical field

SOFTWARE REQUIREMENTS

• MATLAB 7.14  and above versions

RESULTS