Zebin Wu

TL;DR: A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation based on the separation of the background and the anomalies in the observed data.

TL;DR: This model takes full advantage of exploiting the spatial and contextual information present in the hyperspectral image, and outperforms many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic.

TL;DR: The novelty of this work consists in presenting a framework of spatial-spectral KSRC and measuring the spatial similarity by means of neighborhood filtering in the kernel feature space, which opens a wide field for future developments in which filtering methods can be easily incorporated.

TL;DR: Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.

TL;DR: A framework for hyperspectral compressive sensing with anomaly detection which reconstruct the HSI and detect the anomalies simultaneously simultaneously is proposed and outperforms several state-of-the-art methods on both reconstruction and anomaly detection accuracies.