Remote Sensing Image Fusion Based on Integer Wavelet Transformation and Ordered Nonnegative Independent Component Analysis

A novel remote sensing image fusion method is proposed based on integer wavelet transformation and ordered nonnegative independent component analysis (IWT-ONICA) to take advantage of the IWT characteristics of multi-resolution and high-speed computation and the fact that independent component analysis can remove higher-order redundancy. The proposed method is applied to the fusion of IKONOS satellite images. Statistical and visual results show that IWT-ONICA is superior to other fusion algorithms in terms of clarity and visual effect, while evaluation indicators such as the peak signal-to-noise ratio, correlation coefficient, spectral angle mapper, and relative dimensionless global error confirm its superior performance.

[1]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[2]  Jinwen Tian,et al.  Remote sensing image fusion based on average gradient of wavelet transform , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[3]  Patrick Hostert,et al.  Mapping megacity growth with multi-sensor data , 2010 .

[4]  Yun Zhang,et al.  Wavelet based image fusion techniques — An introduction, review and comparison , 2007 .

[5]  Hu Jun-wei Novel method for merging panchromatic and multi-spectral images based on sensor spectral response , 2009 .

[6]  Francesca Bovolo,et al.  A detail-preserving scale-driven approach to change detection in multitemporal SAR images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Rafael García,et al.  Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Michel Barlaud,et al.  Image coding using wavelet transform , 1992, IEEE Trans. Image Process..

[9]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[10]  Ravikrishna Vantipalli,et al.  Multisensor image fusion , 1998 .

[11]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[12]  Xi Zhang,et al.  A new method for multi-source remote sensing image fusion , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[13]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[14]  Libao Zhang,et al.  A remote sensing image fusion algorithm based on integer wavelet transform , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[15]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[16]  Peter P. Wolter,et al.  Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota , 2011 .

[17]  Bo Zhang,et al.  Packed integer wavelet transform constructed by lifting scheme , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[18]  Mallika K.,et al.  Framelet Based Image Fusion for the Enhancement of Cloud Associated Shadow areas in Satellite Images , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[19]  I. Daubechies,et al.  Factoring wavelet transforms into lifting steps , 1998 .

[20]  I. Daubechies,et al.  Wavelet Transforms That Map Integers to Integers , 1998 .

[21]  Marc Acheroy,et al.  Fusion of PolSAR and PolInSAR data for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.