Parallelized Nonlinear Target Detection for Asbestos Identification in Large-Scale Remote Sensing Data

Due to the side effects of asbestos on human health and environments, many countries have banned the use of asbestos-containing materials, but there are still illegal products with asbestos in daily life. In order to investigate the distributions of asbestos to facilitate its removal, this letter studies the feasibility of asbestos identification with hyperspectral (HS) and panchromatic (PAN) data, taking images captured by the PRISMA and ZY1E 2-D satellites over Pavia, Italy, as examples. In this work, a pansharpening method with guided filter was used to improve the HS image quality in terms of spectral fidelity and spatial details. Then, the possible location of asbestos could be obtained by a nonlinear target detector named bilinear sparse target detector (BSTD). Considering high computational cost for large-scale remote sensing data processing, we further develop BSTD to its parallelized version (denoted as PBSTD). Given the ground truth of asbestos over Pavia by the Regional Environmental Protection Agency-ARPA, Lombardia, our PBSTD and several popular methods are evaluated from both qualitative and quantitative perspectives, showing that most algorithms could correctly detect large-size asbestos roofs, and the nonlinear PBSTD and MSDinter perform better in small-size asbestos identification than other linear detectors. However, the detection accuracy on small-size asbestos is insufficient in practical applications, which indicates that there are still issues to achieve accurate small-size asbestos identification using coarse-spatial-resolution spaceborne remote sensing.