Fundamentals of digital image processing
Fundamentals of digital image processing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Variable temporal-length 3-D discrete cosine transform coding
IEEE Transactions on Image Processing
Color space projection, feature fusion and concurrent neural modules for biometric image recognition
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Concurrent neural classifiers for pattern recognition in multispectral satellite imagery
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
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This paper presents an original research for hyperspectral satellite image compression using a fully neural system with the following processing stages: (1) a Hebbian network performing the principal component selection; (2) a system of "k" circular self-organizing maps for vector quantization of the previously extracted components. The software implementation of the above system has been trained and tested for a hyperspectral image segment of type AVIRIS with 16 bits/pixel/band (b/p/b). One obtains the peak-signal-to-quantization noise ratio of about 50 dB, for a bit rate of 0.07 b/p/b (a compression ratio of 228:1). We also extend the previous model for removal of the spectral redundancy (between the R, G, B channels) of color images as a particular case of multispectral image compression; we consider both the case of color still images and that of color image sequences.