A satellite image set for the evolution of image transforms for defense applications

• Authors: Michael R. Peterson;Gary B. Lamont;Frank Moore;Patrick Marshall
• Venue: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
• Year: 2007

In recent years, wavelets have been widely applied instate-of-the-art image processing algorithms, providing efficient compression while maintaining superior image quality. However, wavelet performance may not be sufficient when extreme compression ratios are required. Defense applications often require robust transforms simultaneously minimizing bandwidth requirements and image resolution loss. Image processing algorithms take advantage of quantization to provide substantial lossy compression ratios at the expense of resolution. Recent research demonstrates that genetic algorithms (GAs) evolve filters out performing standard discrete wavelet transforms in conditions subject to high quantization error. Evolved filters must be trained using images appropriate to their intended application. We present a set offifty satellite images used to evolve image transforms appropriate for satellite and unmanned aerial vehicle (UAV) reconnaissance applications. We identify the best training and test images. Image transforms evolved using appropriate training images reduce the mean squared error (MSE) by an average of greater than 15% across the entire image set under conditions subject to high quantization error.