We present a method of registering a sequence of aerial consecutive images taken from an airborne sensor into a mosaic, as well as a method for visualizing moving objects on the large satellite image, thus providing information about traffic conditions following a disaster. Our approach uses features obtained from histograms of oriented gradients (HOG) and the scale invariant feature transform (SIFT) to register both the aerial images and a large satellite image. Since the SIFT is used to find keypoints for image registration and the HOG is used to localize these points by using local texture information, we registered a precise image sequence even though the images were not captured at the same time. We then applied moving object detection on the basis of the results of tracking the points from the registered images and visualized them on the satellite image. We present the results on real image sequences from a helicopter, and show that visualization of moving objects is a useful way to display road conditions over a wide area in a disaster situation.
