A popular strategy to reduce the spectral size of HyperSpectral Images (HSI) is to suppress all bands whose information content is largely duplicated in surviving bands. This is commonly termed Band Selection (BS). In this manuscript, a novel, unsupervised BS technique is proposed, consisting of three main steps: 1) Adjacent structural-similarity-based band partitioning, 2) for each partition, correlation-based, cluster-representative band selection, and 3) bad cluster removal using an Entropy measure. Initially, the adjacent structural similarity is measured, using Structural SIMilarity Index (SSIM); the next step is adjacency-partitioned cluster formation using a derivative-based approach. Then, a representative band with a high correlation is selected for each cluster. Finally, according to the Entropy measure, the cluster representatives are ranked to obtain the optimal subset of bands. The proposed Adjacency Clustering and Local Structural Correlation (ACLSC)-based BS is compared with other state-of-art approaches in terms of classification performance on benchmark HSI datasets. Overall Accuracy (OA), Average Accuracy (AA), and Kappa ((Formula presented.)) are computed on three datasets, namely, Indian Pines, Salinas, and Pavia University, to validate the proposed algorithm. Validation results support the use of adjacency clustering and incorporation of spatial-structural features for BS purposes.
A hyperspectral band selection strategy based on adjacency clustering and local structural correlation
Dell'acqua F.;
2024-01-01
Abstract
A popular strategy to reduce the spectral size of HyperSpectral Images (HSI) is to suppress all bands whose information content is largely duplicated in surviving bands. This is commonly termed Band Selection (BS). In this manuscript, a novel, unsupervised BS technique is proposed, consisting of three main steps: 1) Adjacent structural-similarity-based band partitioning, 2) for each partition, correlation-based, cluster-representative band selection, and 3) bad cluster removal using an Entropy measure. Initially, the adjacent structural similarity is measured, using Structural SIMilarity Index (SSIM); the next step is adjacency-partitioned cluster formation using a derivative-based approach. Then, a representative band with a high correlation is selected for each cluster. Finally, according to the Entropy measure, the cluster representatives are ranked to obtain the optimal subset of bands. The proposed Adjacency Clustering and Local Structural Correlation (ACLSC)-based BS is compared with other state-of-art approaches in terms of classification performance on benchmark HSI datasets. Overall Accuracy (OA), Average Accuracy (AA), and Kappa ((Formula presented.)) are computed on three datasets, namely, Indian Pines, Salinas, and Pavia University, to validate the proposed algorithm. Validation results support the use of adjacency clustering and incorporation of spatial-structural features for BS purposes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
