DOES multispectral / hyperspectral pansharpening improve the performance of anomaly detection?

Abstract

Pansharpening refers to the fusion of a high spatial resolution panchromatic image with high spectral resolution multispectral or hyperspectral images (MSI or HSI) to yield high resolution data in both spectral and spatial domains. It has been widely adopted as a primary preprocessing step for numerous applications. In this paper, we perform a literature survey of various pansharpening algorithms including the most advanced deep learning approaches for both multispectral and hyperspectral images. We further evaluate the effect of the resolution difference on anomaly detection. Synthetic multispectral and hyperspectral images are generated to evaluate the performance of anomaly detection on high resolution images. Eight state-of-the-art MSI and HSI pansharpening methods are compared in this paper. Experimental results show that, performing anomaly detection on high resolution images improves the detection rate, and at the mean time suppresses the false alarm rate.