Spectral–spatial hyperspectral image classification with adaptive mean filter and jump regression detection

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A new remotely sensed hyperspectral image data classification algorithm which integrates the adaptive mean filter and jump regression in a variational framework is introduced. First, the adaptive mean filter is used to build the posterior probability distributions in each subpixel, and the jump detection method is then used to provide the content-aware information to adjust the smoothing extent of total variation in image discontinuous areas. Experimental results on real hyperspectral datasets show the relatively good performance of the proposed algorithm in terms of the overall accuracy, average accuracy and kappa statistic.