Quantitative analysis on lossy compression in remote sensing image classification

In this paper, we propose to use a quantitative approach based on LS-SVM to perform estimation of the impact of lossy compression on remote sensing image compression. Kernel function selection and the model parameters computation are studied for remote sensing image classification when LS-SVM analysis model is establish. The experiments show that our LS-SVM model achieves a good performance in remote sensing image compression analysis. Classification accuracy variation according to compression ratio scales are summarized based on our experiments.