Multispectral infrared image classification using filters derived from independent component analysis

Spectral-spatial independent component analysis (ICA) basis functions of visible color images are similar to some processing elements in the human visual systems in that they resemble Gabor filters and show color opponencies. In this research we studied combined spectral-spatial ICA basis functions of multispectral mid wave infrared (MWIR) images. These ICA spectral-spatial basis functions were then used as filters to extract features from multispectral MWIR images for classification. The images were captured in the 3.0–5.0 µm, 3.7–4.2 µm, and 4.0–4.5 µm bands using a multispectral MWIR camera. In the proposed algorithm, phase relationships between the basis functions indicate how the extracted features from the different spectral band images can be combined. We used classification performance to compare features obtained by filtering using multispectral ICA basis functions, multispectral principal component analysis basis functions, and Gabor filters.

[1]  Robert Jenssen,et al.  Independent component analysis for texture segmentation , 2003, Pattern Recognit..

[2]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[3]  T. W. Lee,et al.  Chromatic structure of natural scenes. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  T. Sejnowski,et al.  Color opponency is an efficient representation of spectral properties in natural scenes , 2002, Vision Research.

[5]  Glenn Healey,et al.  Hyperspectral texture recognition using a multiscale opponent representation , 2003, IEEE Trans. Geosci. Remote. Sens..

[6]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[7]  Amit Jain,et al.  A multiscale representation including opponent color features for texture recognition , 1998, IEEE Trans. Image Process..

[8]  James V. Stone.  Independent Component Analysis: A Tutorial Introduction , 2007 .

[9]  E. Oja,et al.  Independent Component Analysis , 2013 .

[10]  F. Ade,et al.  Characterization of textures by ‘Eigenfilters’ , 1983 .

[11]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[12]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[14]  Frederic Truchetet,et al.  Karhunen-Loeve transform applied to region- based segmentation of color aerial images , 2001 .

[15]  Aapo Hyvärinen,et al.  Feature extraction from colour and stereo images using ICA , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.