Image compression used improved error back-propagation neural network

To overcome the dramatically increasing data amount of space remote sensing image, an improved back-propagation(BP) neural network was put forward to compress it. The algorithm used the Levenberg-Marquart algorithm to improve the convergence speed of neural network and used algorithm to improve the generalization ability of neural network. We compared and analyzed the compression result and error performance function of the improved algorithm and the standard BP algorithm to the same image. The experimental results show that, when the image compression ratio is 1/2, the mean square error (MSE) of standard BP algorithm is 343.3750; for improved BP algorithm, the MSE is 69.5796 when the image compression ratio is 1/16, the MSE is 20.9561 when the image compression ratio is 1/8, and the MSE is 5.5123 when the ratio is 1/4. Moreover, the peak signal-to-noise ratio (PSNR) of the improved algorithm is always in the range from 30 dB to 40 dB. The improved algorithm has been applied in practical engineering, which meets the need of practical work.