在遥感定量反演的地面同步实测环节中，人为因素、环境变化、条件限制等测量不确定性因素会不可避免地引入数据噪声，致使水体悬浮物浓度反演精度降低。为此，提出一种顾及测量不确定性的水体悬浮物浓度遥感定量反演方法，即自适应抽样一致性极限学习机（ASAC-ELM）算法。该算法结合了极限学习机（ELM）、随机抽样一致性（RANSAC）和N邻近点抽样一致性（NAPSAC）方法的优势与特点，利用参数维度自适应地选取RANSAC或NAPSAC算法进行参数估计，避免了ELM算法易受非零均值正态分布数据噪声影响的缺陷。ASAC-ELM算法通过选取局内点（非噪声点）数据建立模型，可去除噪声数据的干扰，提升模型的精度与适应性。通过模拟多组不同数量级且服从非零均值正态分布的随机数，将加性噪声引入训练数据中，实现不同噪声比条件下对ASAC-ELM算法的检验，并与ELM算法、传统反向传播（BP）神经网络算法进行了对比。结果表明，不同噪声比条件下，ASAC-ELM算法的水质悬浮物浓度反演精度高于ELM算法和传统BP神经网络算法，且反演结果稳定性较高。

In the process of synchronous ground observation for quantitative remote sensing inversion, measurement uncertainty factors like human subjective factor, environmental change and condition restriction will induce data noise inevitably, which degrades the retrieval accuracy of the suspended matter concentration. Therefore, a measurement uncertainty-aware retrieval method named as the adaptive sample consensus extreme learning machine (ASAC-ELM) is proposed. ASAC-ELM integrates the merits of extreme learning machine (ELM), random sample consensus (RANSAC) and N adjacent points sample consensus (NAPSAC). The algorithm adaptively selects RANSAC or NAPSAC to estimate model parameters with the guidance of the parameter dimension, which avoids the problem that the ELM algorithm is sensitive to the non-zero normal distributed data noise. The ASAC-ELM algorithm selects inlying points (non-noise points) for model construction, thus can remove the interference from noise, and enhance the accuracy and flexibility of the model. In order to investigate the effectiveness of the proposed method under different noise conditions, a series of additive noise with non-zero mean normal distribution is introduced in the training data. The comparison among ASAC-ELM, ELM and traditional back propagation (BP) neural network algorithms is also conducted. The results show that for the retrieval of inland water suspended matter concentration under various noise conditions, the inversion accuracy and stability of ASAC-ELM is higher than those of ELM and the traditional BP neural network.