Hyperspectral remote sensing IQA via learning multiple kernels from mid-level features

Abstract Hyperspectral image quality assessment (HIQA) is an indispensable technique in both academic and industry domain However, HIQA is still a challenging task since those fine-grained and quality-aware visual details are difficult to be captured. Compared with the conventional low-level features, mid-level features usually contain more semantic and quality clues and exhibit higher discriminant ability. Thus, we aim to leverage the mid-level features for HIQA. More specifically, three-scale superpixel mosaics are generated from the input image pre-processed by PCA. Each superpixel scale corresponds to various homogenous object parts. Subsequently, three mid-level visual features (fisher vector, combined mean features, reconstructed image matrix) as well as deep features of hyperspectral images are calculated with three-scale superpixel images to constitute multiple kernels. Afterwards, we integrate these kernels into a multimodal one, which is further integrated into a feature vector by row-wise stacking. The image quality evaluation can be calculated based on the designed similarity metric. Comprehensive experiments have demonstrated the effectiveness of our proposed HIQA algorithm.