A superpixel region merging based nonuniformity correction method for hyperspectral images

The nonuniformity existing in hyperspectral (HS) images severely degrades the data quality, reduces the interpretation accuracy of HS images, and restricts the subsequent applications. To effectively eliminate the nonuniformity in HS images, we propose a superpixel region merging-based nonuniformity correction method (called NUC-SRM). A superpixel segmentation strategy is exploited to obtain a set of partitions, and a region merging strategy is utilized to merge partitions with high spatial correlation and spectral similarity. Then, a number of homogeneous regions are obtained, where their average responses are taken as reference calibration data. Finally, a 2-point multi-section nonuniformity correction method is applied to correct images. The experimental results conducted on real HS images confirm that the proposed method can effectively eliminate the nonuniformity of HS images without any significant information loss compared with the state-of-the-art techniques.

Automated summary

A nonuniformity correction method for hyperspectral images based on superpixel region merging is proposed, showing effectiveness in eliminating nonuniformity without significant information loss according to experimental results.