Multispectral Remote Sensing Image Database (MRSID)

Multispectral remote sensing images suffer from a wide range of degradations due to noise, blur, haze, compression, striping, and other distortions. While there is rich literature in the restoration of such multipsectral images, the quality assessment (QA) of multispectral images has received much less attention. In addition, often a reference image is not available, which motivates the study of no reference (NR) image QA (IQA). While several algorithms for NR-IQA have been designed for natural images, their relevance for multispectral images has not been validated. The major impediment to achieving this is the lack of a subjective QA database for validation. We make three contributions in our work. We first design a multispectral IQA database, namely Multispectral Remote Sensing Image Database (MRSID), with images suffering from various distortions and conduct a subjective study to obtain pairwise image quality rankings. We then benchmark several NR-IQA methods developed for natural images on MRSID. We show that recent vision-language based models can yield excellent performance when fine-tuned on our dataset, especially when multispectral images are trained with images pooled from all bands. Finally, we investigate the reasons for such superior performance and show that improving the alignment of features across different spectral bands can further improve performance.