Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features

The rapid increasing of remote sensing (RS) data in many applications ignites a spark of interest in the process of satellite image matching and registration. These data are collected through remote sensors then processed and interpreted by means of image processing algorithms. They are taken from different sensors, viewpoints, or times for many industrial and governmental applications covering agriculture, forestry, urban and regional planning, geology, water resources, and others. In this chapter, a feature-based registration of optical and radar images from same and different sensors using invariant local features is presented. The registration process starts with the feature extraction and matching stages which are considered as key issues when processing remote sensing data from single or multi-sensors. Then, the geometric transformation models are applied followed by the interpolation method in order to get a final registered version. As a pre-processing step, speckle noise removal is performed on radar images in order to reduce the number of false detections. In a similar fashion, optical images are also processed by sharpening and enhancing edges in order to get more accurate detections. Different blob, corner and scale based feature detectors are tested on both optical and radar images. The list of tested detectors includes: SIFT, SURF, FAST, MSER, Harris, GFTT, ORB, BRISK and Star. In this work, five of these detectors compute their own descriptors (SIFT, SURF, ORB, BRISK, and BRIEF), while others use the steps involved in SIFT descriptor to compute the feature vectors describing the detected keypoints. A filtering process is proposed in order to control the number of extracted keypoints from high resolution satellite images for a real time processing. In this step, the keypoints or the ground control points (GCPs) are sorted according to the response strength measured based on their cornerness. A threshold value is chosen to control the extracted keypoints and finalize the extraction phase. Then, the pairwise matches between the input images are calculated by matching the corresponding feature vectors. Once the list of tie points is calculated, a full registration process is followed by applying different geometric transformations to perform the warping phase. Finally and once the transformation model estimation is done, it is followed by blending and compositing the registered version. The results included in this chapter showed a good performance for invariant local feature detectors. For example, SIFT, SURF, Harris, FAST and GFTT achieve better performance on optical images while SIFT gives also better results on radar images which suffer from speckle noise. Furthermore, through measuring the inliers ratios, repeatability, and robustness against noise, variety of comparisons have been done using different local feature detectors and descriptors in addition to evaluating the whole registration process. The tested optical and radar images are from RapidEye, Pleiades, TET-1, ASTER, IKONOS-2, and TerraSAR-X satellite sensors in different spatial resolutions, covering some areas in Australia, Egypt, and Germany.

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