For mapping forest above-ground biomass (AGB), a novel feature evaluation method, named integrating multiple metrics with collinearity considered (IMC), is proposed by fusing different types of metrics with considering information redundancy among features. The optimal feature set was obtained using IMC metric from various types of features extracted from L-band ALOS-2 PALSAR data in planted Chinese fir forest. To evaluate the effectiveness of the IMC metric, three common feature evaluation metrics, Pearson correlation coefficient, importance derived from random forest (RF), and the sensitivity index (SI), were also used to obtain the optimal feature set, respectively. Moreover, the combination effect among features was also analyzed to filter the global best feature set in mapping for forest AGB. The results showed that the accuracy of forest AGB estimation was obviously improved using the proposed IMC metric [0.45–0.67 for $R^{2}$ and from 24.51% to 32.16% for relative root mean square error (rRMSE)]. Furthermore, it is also implied that the combination effect among features has the great potential to improve the capability of forest AGB estimation.

中文翻译：

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一种利用 PolSAR 数据融合多种评估指标绘制森林 AGB 的新特征评估方法

为了绘制森林地上生物量（AGB），通过融合不同类型的指标并考虑特征之间的信息冗余，提出了一种新的特征评估方法，即考虑共线性的多指标集成（IMC）。从人工杉木林L波段ALOS-2 PALSAR数据中提取的各类特征，利用IMC度量获得最优特征集。为了评估 IMC 指标的有效性，还分别使用三个常见的特征评估指标：皮尔逊相关系数、随机森林重要性（RF）和敏感性指数（SI）来获得最佳特征集。此外，还分析了特征之间的组合效果，以筛选森林AGB映射中的全局最佳特征集。结果表明，使用所提出的 IMC 指标，森林 AGB 估计的准确性明显提高 [0.45–0.67 for $R^{2}$相对均方根误差 (rRMSE) 从 24.51% 到 32.16%]。此外，这也意味着特征之间的组合效应对于提高森林AGB估计的能力具有巨大的潜力。