A Kernel-Based Hyperspectral Index for Mapping Plastics in Urban Areas

Accurate mapping of plastic distribution at large spatial scales is critical for mitigating pollution in urban built-up areas, but detection remains challenging due to the small size and sparse distribution of plastic objects. Spectral indices are commonly used for plastic mapping, but existing spectral indices rely on short-wave infrared (SWIR) absorption features with linear combinations, failing to account for plastic spectral variability and the nonlinear relationships between spectral bands. To overcome these limitations, this study proposes a novel kernel-based hyperspectral plastic index, termed the kernel plastic index (kPI), which jointly quantifies two stable SWIR absorption features (1600–1800 nm and 2000–2400 nm) and employs a Gaussian Radial Basis Function (RBF) kernel for nonlinear mapping to capture higher-order spectral interactions. The kPI was evaluated in three study areas in China using UAV and GF-5 hyperspectral images and compared with three existing plastic indices, a newly proposed combined Plastic Index (cPI), and a one-class classification method. The results demonstrated that kPI achieves the highest spectral separability between plastics and other land cover types. In plastic mapping, kPI consistently achieved the highest accuracies in all study areas, with F1-scores of 89.6%, 85.1%, and 92.4%, consistently improving upon comparative linear indices. The index exhibits enhanced separability for plastics and noticeable suppression of other land cover types with high computational efficiency. Thus, kPI provides an effective and robust approach for large-scale plastic pollution monitoring in urban environments.