A fast mosaic method for airborne images: the new Template-Convolution Speed-Up Robust Features (TSURF) algorithm

Abstract

In this paper, we present a new feature extraction algorithm termed Template-Convolution Speed-Up Robust Features (TSURF), which uses template convolution to extract points of interest based on the Speed-Up Robust Features (SURF) algorithm. Feature extraction is applied to extract corresponding characteristics in overlapping fields from adjacent images. The characteristics include area, line, and point features. As the point feature is easy to compute and is invariant to image scale, rotation, intensity, and so on, we use it to register and mosaic images widely. By filtering redundant points of interest, TSURF can greatly reduce the running time and keep the mosaic quality good during real-time mosaicking. SURF and TSURF are applied to airborne images to compare the efficiency of each algorithm in constructing a mosaic. We calculate the average coordinate error and angle error for evaluating mosaic precision. We also use the average gradient, standard deviation, and forecast root mean-square error to compare the mosaic quality of SURF with that of TSURF. The first mosaic experiment consisting of 20 groups of airborne images showed that the mosaic speed of TSURF is three times faster than that of SURF and maintains comparative mosaic accuracy. In the second experiment, a series of continuous thumbnail images were mosaicked using the SURF and TSURF algorithms fully automatically. The TSURF speed was 63.40% faster than that of SURF and the accuracy remained consistent.