Given the importance of accuracy in land cover (LC) maps, several methods have been adopted to predict per-pixel land cover accuracy (PLCA) of classified remote sensing images. Such a PLCA map provides spatially-explicit accuracy information and is of paramount importance for both producers and end-users of LC maps to thoroughly understand the spatial distribution of accuracy. In this study, we proposed a simple yet powerful random forest (RF) based approach for PLCA mapping with limited reference sample data. The main assumption of the proposed approach is that the LC’s misclassifications do not occur randomly, but rather exhibit some detectable characteristics which can be retrieved via the built model. With this approach, RF attempts to establish a nonlinear relationship between the accuracy and the same spectral bands used in LC classification. To confirm the proposed method as a consistent and practical approach for a variety of different settings, we evaluated it on five different classified remote sensing images derived from Landsat-8, Ikonos, and three Sentinel-2 images across different parts of Iran. In this manner, to validate the predictive capability of the RF-based method, we calculated the area under the receiver operating characteristic curve (AUROC) and several other statistical metrics, including sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy (ACC). Analysis of the average values of these metrics (AUROC = 0.88, SN = 95%, SP = 68%, PPV = 96%, NPV = 72%, and ACC = 95%) derived from the limited sample size datasets showed that the proposed model performs well in all case studies. The performance of the proposed model was further assessed through comparison against two benchmark methods, namely Gaussian kernel interpolation (GKI) and linear kernel interpolation (LKI). In conclusion, although our comprehensive evaluations revealed that RF, GKI, and LKI methods are promising approaches for PLCA mapping, RF outperformed both GKI and LKI in all of the experimental sites.

鉴于准确性在土地覆盖（LC）地图中的重要性，已采用了几种方法来预测分类遥感图像的每像素土地覆盖准确性（PLCA）。这样的PLCA地图可提供空间明晰的精度信息，对于LC地图的生产者和最终用户全面了解精度的空间分布至关重要。在这项研究中，我们提出了一种简单而强大的基于随机森林（RF）的方法，用于有限参考样本数据的PLCA映射。提出的方法的主要假设是LC的错误分类不会随机发生，而是表现出一些可检测到的特征，这些特征可以通过构建的模型进行检索。通过这种方法，RF尝试在精度和LC分类中使用的相同光谱带之间建立非线性关系。为了确认所提出的方法是针对各种不同设置的一致且实用的方法，我们在源自Landsat-8，Ikonos的五幅不同的分类遥感图像以及伊朗不同地区的三张Sentinel-2图像中对其进行了评估。通过这种方式，为了验证基于RF的方法的预测能力，我们计算了接收器工作特性曲线（AUROC）和其他几个统计指标（包括灵敏度（SN），特异性（SP），阳性预测值（ PPV），阴性预测值（NPV）和准确性（ACC）。从有限样本量数据集中得出的这些指标的平均值（AUROC = 0.88，SN = 95％，SP = 68％，PPV = 96％，NPV = 72％，ACC = 95％）的平均值表明，建议的该模型在所有案例研究中均表现良好。通过与两种基准方法（即高斯核内插（GKI）和线性核内插（LKI））进行比较，进一步评估了所提出模型的性能。总之，尽管我们的综合评估表明RF，GKI和LKI方法是PLCA映射的有前途的方法，但RF在所有实验站点中均优于GKI和LKI。