期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2012
卷号：3
期号：4
页码：203
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：As multisensory data is made available in many areas such as remote sensing, medical imaging, etc, thesensor fusion has become a new field for research. Multisensor image fusion mainly focuses on combiningspatial information of a high resolution panchromatic (PAN) image with spectral information of a lowresolution multispectral image (MS) to produce an image with highest spatial content while preservingspectral resolution. A geometrical transform called contourlet transform (CT) is introduced, whichrepresents images containing contours and textures. This paper derived an efficient block based featurelevel contourlet transform with neural network (BFCN) model for image fusion. The proposed BFCN modelintegrates CT with neural network (NN), which plays a significant role in feature extraction and detectionin machine learning applications. In the proposed BFCN model, the two fusion techniques, CT and NN arediscussed for fusing the IRS-1D images using LISS III scanner about the locations Hyderabad,Vishakhapatnam, Mahaboobnagar and Patancheru in Andhra Pradesh, India. Also Landsat 7 image dataand QuickBird image data are used to perform experiments on the proposed BFCN model. The featuresunder study are contrast visibility, spatial frequency, energy of gradient, variance and edge information.Feed forward back propagation neural network is trained and tested for classification, since the learningcapability of NN makes it feasible to customize the image fusion process. The trained NN is then used tofuse the pair of source images. The proposed BFCN model is compared with other techniques to assess thequality of the fused image. Experimental results clearly prove that the proposed BFCN model is an efficientand feasible algorithm for image fusion.
