Adaptive Material Matching for Hyperspectral Imagery Destriping

Due to instrument instability, slit contamination, and light interference, hyperspectral images often suffer from striping artifacts, which greatly impairs the data quality. Real hyperspectral data are usually characterized by a small amount of historical data, complex material distribution, insignificant periodicity of noise, and so on, which brings significant challenges for the destriping task. However, the assumptions made by traditional destriping methods are often inconsistent with these characteristics. To this end, we propose a novel destriping method based on adaptive material matching (MAM) without making explicit assumptions of hyperspectral data. Specifically, to identify pixels that belong to the same material, we propose a principal material analysis (PMA) to adaptively generate thresholds within each superpixel. The pixels are matched by thresholding their vertical gradients and leveraging both inner stripe gradient feature (ISGF) and neighbor-stripe geometry feature (NSGF). Correction pixels selected from the same material can then be used to calculate the offsets and gains of pixels to adjust adjacent columns. To further improve the stability of the destriping process, we generate a set of correction candidates for each column and select the optimal candidate by considering the prior distribution and destriping nonuniformity. The stripe noise within the whole image is finally removed by iteratively performing the correction between adjacent columns. We compare the proposed model against traditional and deep learning methods on both synthetic and real hyperspectral images. The promising results indicate that MAM can effectively remove the image stripes, retain original image information, and improve the nonuniformity.