He Ping, Cheng Xianfeng, Wen Xingping, Cao Yi, Chen Yu
Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
School of Fine Art and Design, Kunming University, Kunming 650214, China.
Sensors (Basel). 2025 Jan 23;25(3):684. doi: 10.3390/s25030684.
Hyperspectral technology has become increasingly important in monitoring soil heavy metal pollution, yet hyperspectral data often contain substantial band redundancy, and band selection methods are typically limited to single algorithms or simple combinations. Multi-algorithm combinations for band selection remain underutilized. To address this gap, this study, conducted in Gejiu, Yunnan Province, China, proposes a multi-algorithm band selection method to enable the rapid prediction of lead (Pb) contamination levels in soil. To construct a preliminary Pb content prediction model, the initial selection of spectral bands utilized methods including CARS (Competitive Adaptive Reweighted Sampling), GA (Genetic Algorithm), MI (mutual information), SPA (Successive Projections Algorithm), and WOA (Whale Optimization Algorithm). The results indicated that WOA achieved the highest modeling accuracy. Building on this, a combined WOA-based band selection method was developed, including combinations such as WOA-CARS, WOA-GA, WOA-MI, and WOA-SPA, with multi-level band optimization further refined by MI (e.g., WOA-GA-MI, WOA-CARS-MI, WOA-SPA-MI). The results showed that the WOA-GA-MI model exhibited optimal performance, achieving an average R of 0.75, with improvements of 0.32, 0.11, and 0.02 over the full-spectrum model, the WOA-selected spectral model, and the WOA-GA model, respectively. Additionally, spectral response analysis identified 22 common bands essential for Pb content inversion. The proposed multi-level combined model not only significantly enhances prediction accuracy but also provides new insights into optimizing hyperspectral band selection, serving as a valuable scientific foundation for assessing soil heavy metal contamination.
高光谱技术在监测土壤重金属污染方面变得越来越重要，但高光谱数据通常包含大量的波段冗余，并且波段选择方法通常局限于单一算法或简单组合。多算法组合用于波段选择仍未得到充分利用。为了弥补这一差距，本研究在中国云南省个旧市进行，提出了一种多算法波段选择方法，以实现对土壤中铅（Pb）污染水平的快速预测。为构建初步的Pb含量预测模型，光谱波段的初始选择采用了包括竞争性自适应重加权采样（CARS）、遗传算法（GA）、互信息（MI）、连续投影算法（SPA）和鲸鱼优化算法（WOA）等方法。结果表明，WOA实现了最高的建模精度。在此基础上，开发了一种基于WOA的组合波段选择方法，包括WOA - CARS、WOA - GA、WOA - MI和WOA - SPA等组合，并通过MI进一步细化进行多级波段优化（例如WOA - GA - MI、WOA - CARS - MI、WOA - SPA - MI）。结果表明，WOA - GA - MI模型表现出最优性能，平均R值达到0.75，分别比全光谱模型、WOA选择的光谱模型和WOA - GA模型提高了0.32、0.11和0.02。此外，光谱响应分析确定了22个对Pb含量反演至关重要的共同波段。所提出的多级组合模型不仅显著提高了预测精度，还为优化高光谱波段选择提供了新的见解，为评估土壤重金属污染提供了有价值的科学基础。
