The alignment of images acquired using the synthetic aperture radar (SAR) is a central task in image processing for remote sensing. Owing to speckle noise and the particular features of SAR imaging, stable feature detection and accurate matching remain challenging tasks. We propose a registration method for SAR images. In feature detection, we develop a hybrid feature detection method to identify structural and textural features. A stable convex corner point feature is detected using stable convex polygons obtained by a proposed method for local stable extremal region extraction and convex polygon fitting. The stable local extremum points in each convex polygon are detected by an improved scale invariant feature transform method. In feature matching, a multifeature constraint matching method is designed for accurate matching. Coarse matching is implemented using the constraints on the region and its shape as well as the network. The use of stable local extremum points with spatial constraints can eliminate mismatches and yield a fine match. The results of experiments verified the effectiveness and accuracy of the proposed method.
Post-launch vicarious calibration method, as an important post launch method, not only can be used to evaluate the onboard calibrators but also can be allowed for a traceable knowledge of the absolute accuracy, although it has the drawbacks of low frequency data collections due expensive on personal and cost. To overcome the problems, CEOS Working Group on Calibration and Validation (WGCV) Infrared Visible Optical Sensors (IVOS) subgroup has proposed an Automated Radiative Calibration Network (RadCalNet) project. Baotou site is one of the four demonstration sites of RadCalNet. The superiority characteristics of Baotou site is the combination of various natural scenes and artificial targets. In each artificial target and desert, an automated spectrum measurement instrument is developed to obtain the surface reflected radiance spectra every 2 minutes with a spectrum resolution of 2nm. The aerosol optical thickness and column water vapour content are measured by an automatic sun photometer. To meet the requirement of RadCalNet, a surface reflectance spectrum retrieval method is used to generate the standard input files, with the support of surface and atmospheric measurements. Then the top of atmospheric reflectance spectra are derived from the input files. The results of the demonstration satellites, including Landsat 8, Sentinal-2A, show that there is a good agreement between observed and calculated results.