We have developed methods to dynamically and autonomously detect transient surface features, such as dust devil tracks or dark slope streaks on Mars, from images. Most prior work on this subject has relied on manual examination of image pairs. Exciting discoveries of new surface features such as gullies and impact craters have been made, usually serendipitously. How many more such features remain undiscovered in the massive volume of images being collected and returned?
Automated methods can help reduce the manual effort needed to find and catalog new and interesting features. Previous techniques for automated analysis have focused on changes at the pixel level. They require an initial, sometimes slow, full registration between a candidate pair of images. Once the images are registered, subtracting one from the other yields changes. These are usually thresholded or subjected to further analysis to help filter out noise and other uninteresting "changes".