Improved satellite image compression and reconstruction via genetic algorithms

Brendan Babb; Frank Moore; Michael Peterson; Gary Lamont

doi:10.1117/12.799891

7 October 2008 Improved satellite image compression and reconstruction via genetic algorithms

Brendan Babb, Frank Moore, Michael Peterson, Gary Lamont

Proceedings Volume 7114, Electro-Optical Remote Sensing, Photonic Technologies, and Applications II; 711405 (2008) https://doi.org/10.1117/12.799891
Event: SPIE Security + Defence, 2008, Cardiff, Wales, United Kingdom

Abstract

A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.

Citation Download Citation

Brendan Babb, Frank Moore, Michael Peterson, and Gary Lamont "Improved satellite image compression and reconstruction via genetic algorithms", Proc. SPIE 7114, Electro-Optical Remote Sensing, Photonic Technologies, and Applications II, 711405 (7 October 2008); https://doi.org/10.1117/12.799891

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