Hyperspectral Anomaly Detection via Local Gradient Guidance

In this paper, a novel hyperspectral image (HSI) anomaly detection method is proposed. This method is inspired by three ideas. First, the spatial resolution of the HSIs is sacrificed for their spectral information. Structural information of the HSIs tends to be smooth and distorts from that of the real scene. Second, with a loose false alarm rate, it is not difficult to pick out all the anomalies. Third, gradients of these probable anomalies can be transformed to enhance the spatial information of the HSI. Meanwhile, it is desirable that the enhanced HSI could be detected more precisely. Three modules are designed with respect to these three ideas, which are locating the probable pixels, local gradient guidance, and the anomaly detection for the enhanced HSI. Specifically, some probable anomalies are firstly selected. Secondly, the gradients of these selected pixels are transformed and utilized to guide the spatial enhancement for the HSI locally. Finally, the final detection is implemented on the enhanced HSI. Experimental results obtained on four real HSIs demonstrate the effectiveness of the proposed method.