Most of the image processing techniques have been first proposed and developed on small size images and\r\nprogressively applied to larger and larger data sets resulting from new sensors and application requirements. In\r\ngeosciences, digital cameras and remote sensing images can be used to monitor glaciers and to measure their\r\nsurface velocity by different techniques. However, the image size and the number of acquisitions to be processed\r\nto analyze time series become a critical issue to derive displacement fields by the conventional correlation\r\ntechnique. In this paper, a mathematical optimization of the classical normalized cross-correlation and its\r\nimplementation are described to overcome the computation time and window size limitations. The proposed\r\nimplementation is performed with a specific memory management to avoid most of the temporary result recomputations.\r\nThe performances of the software resulting from this optimization are assessed by computing the\r\ncorrelation between optical images of a serac fall, and between Synthetic Aperture Radar (SAR) images of Alpine\r\nglaciers. The optical images are acquired by a digital camera installed near the ArgentiÃ?¨re glacier (Chamonix,\r\nFrance) and the SAR images are acquired by the high resolution TerraSAR-X satellite over the Mont-Blanc area. The\r\nresults illustrate the potential of this implementation to derive dense displacement fields with a computational\r\ntime compatible with the camera images acquired every 2 h and with the size of the TerraSAR-X scenes covering\r\n30 Ã?â?? 50 km2.
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