Pollution level mapping of heavy metal in soil for ground-airborne hyperspectral data with support vector machine and deep neural network: A case study of Southwestern Xiong'an, China

Abstract

Heavy metal in soil is a significant issue with the urban development in China, and traditional ground spectra are difficult to satisfy the demands for heavy metal monitoring and assessment in large-scale areas. In the paper, ground-airborne hyperspectral data is utilized to analyze the pollution level of heavy metal, 423 soil samples and corresponding ground spectra are collected synchronously with airborne hyperspectral image acquisition in Southwestern Xiong'an, China. Among them, support vector machine (SVM) is utilized to predict the concentration of independent samples, deep neural network (DNN) is aimed to estimate the spatial distribution of concentration with airborne image scenes. Finally, the pollution level is generated by the Softmax function, and it is defined by the risk control standard of heavy metals. The ground spectra and airborne image are closely integrated by the proposed method, the pollution situation is directly evaluated by ground-airborne hyperspectral data and indirectly evaluated by the concentration of local space, and the mapping results are believed to provide constructive advices about environmental protection.

Keywords: Deep neural network; Heavy metal; Hyperspectral remote sensing; Pollution level mapping; Support vector machine.