With massive amounts of SAR imagery and data being collected, the need for effective compression techniques is growing. One of the most popular applications for remote sensing is change detection, which compares two geo-registered images for changes in the scene. While lossless compression is needed for signal compression, the same is not often required for image compression. In almost every case the compression ratios are much higher in lossy compression making them more appealing when bandwidth and storage becomes an issue. This research analyzes different types of compression techniques that are adapted for SAR imagery, and tests these techniques with three different change detection algorithms. Many algorithms exist that allow large compression ratios, however, the usefulness of the data is always the final concern. It is necessary to identify compression methods that will not degrade the performance of change detection analysis
