Bilinear Sparse Target Detection for Asbestos Identification in Hyperspectral PRISMA Data

Due to the side effects of asbestos on human health and environments, Italy has banned the use of asbestos-containing materials since 1992, but there are still illegal products with asbestos in daily life. In order to investigate the distributions of asbestos to facilitate its removal, this paper carries out asbestos identification with hyperspectral (HS) and panchromatic (PAN) data captured by the PRISMA satellite over Pavia, Italy. In this work, a pansharpening method with guided filter was used to inject more spatial details from 5m PAN to 30m HS. Then, the possible location of asbestos could be obtained by a bilinear sparse target detector (BSTD). Detection maps using BSTD are compared with that obtained by hierarchical constrained energy minimization (hCEM), ensuring the accuracy and reliability, also compared with the results using matched subspace detector with interaction effects (MSDinter) and adaptive MSD (AMSD) to verify the superiority of the bilinear sparse model.