Optimized clustering sample selection for spectral reflectance recovery

The accuracy of spectral recovery depends heavily on the selection of an appropriate sample set, so the optimized sample selection by clustering strategy can improve the spectral recovery results. This paper presents a sample optimization method that combines hierarchical clustering and K-mean angle similar clustering to achieve this process. The proposed method employs the hierarchical clustering to divide the training sample dataset into 15 subspaces and obtain 15 subspace centroids. The similarity distance is then calculated between the testing sample and each subspace samples, and the subspace with the sample having the smallest distance is selected. The testing sample is utilized as a priori centroid, which clusters the optimal subspace by competition with the centroid of the subspace selected. This iterative process continues until the centroid of the subspace remains unaltered. Finally, the training samples within the optimal subspace use to recover spectral reflectance through Euclidean distance weighting. Experimental results demonstrate that the proposed method outperforms existing methods in terms of spectral and colorimetric accuracy, as well as stability and robustness. This research provides a solution to the problem of data redundancy in the spectral recovery process and enhances the accuracy and efficiency of spectral recovery.

Automated summary

This paper presents a sample optimization method that combines hierarchical clustering and K-mean angle similar clustering to improve the accuracy of spectral recovery. Experimental results demonstrate that the proposed method outperforms existing methods in terms of spectral and colorimetric accuracy, as well as stability and robustness.