Image segmentation using mean shift for extracting croplands from high-resolution remote sensing imagery

A new image segmentation algorithm based on mean shift (MS) is proposed, with an objective to single out croplands in high-resolution remote sensing imagery (HRRSI). The algorithm is composed of two parts. First, in order to improve the discontinuity preserving smoothing of MS filtering for cropland HRRSI, normal and uniform kernels are used to filter inner fields and boundary areas, respectively. A new spectral bandwidth estimation is also developed for better suppressing intra-field variation. Second, a two-stage region-merging technique, with the second stage combining mutual best-fit rule and iterative thresholding, is implemented. An HRRSI scene is used for validation, the results of which indicate good performance of our method.