Residual Diagnosis-Guided Abundance Correction for Blind Hyperspectral Image Unmixing

Recent blind hyperspectral image unmixing methods have achieved effective spectral–spatial representation, yet the reliability of estimated abundances remains difficult to assess because errors in jointly estimated endmembers and abundances can compensate for each other. To address this issue, we propose Residual Diagnosis-Guided Abundance Correction (RDAC), a post-correction framework that preserves the initial abundance estimate as an anchor and characterizes its mismatch using structured reconstruction residuals. Abundance candidates are then combined with structured residual information, while only 1% reference abundances are used to learn the correction. RDAC was evaluated on the Jasper Ridge, Samson, and Urban datasets. With UNMamba as the primary blind-unmixing backbone, RDAC achieved A-RMSEs of 0.0229, 0.0175, and 0.0359, reducing the initial errors by 55.05%, 70.80%, and 59.63%, respectively. It also achieved the lowest mean A-RMSE among the compared methods on Jasper Ridge and Samson while remaining competitive on Urban. Further experiments showed that reconstruction error alone does not reliably indicate abundance accuracy. When applied to NMF, RDAC reduced A-RMSE by 80.45%, 89.25%, and 77.61% on the three datasets. The results indicate that structured residual diagnosis can support abundance correction under limited supervision, and the NMF experiment shows that the same correction framework can also be used with an alternative blind-unmixing backbone.