Reliability factors in Markov random field (MRF) could be used to improve classification performance for synthetic aperture radar (SAR) and optical images; however, insufficient utilization of reliability factors based on characteristics of different sources leaves more room for classification improvement. To solve this problem, a Markov random field (MRF) with amendment reliability factors classification algorithm (MRF-ARF) is proposed. The ARF is constructed based on the coarse label field of urban region, and different controlling factors are utilized for different sensor data. Then, ARF is involved into the data energy of MRF, to classify the sand, vegetation, farmland, and urban regions, with the gray level co-occurrence matrix textures of Sentinel-1 imagery and the spectral values of the Landsat 8 imagery. In the experiments, Sentinel-1 and Landsat-8 images are used with overall accuracy and Kappa coefficient to evaluate the proposed algorithm with other algorithms. Results show that the overall accuracy of the proposed algorithm has the superiority of about 20% in overall precision and at least 0.2 in Kappa coefficient than the comparison algorithms. Thus, the problem of insufficient utilization of different sensors data could be solved.

马尔可夫随机场（MRF）中的可靠性因子可用于改善合成孔径雷达（SAR）和光学图像的分类性能；但是，基于不同来源的特征对可靠性因子的利用不足，为分类的改进留出了更多空间。为了解决这个问题，提出了一种具有修正可靠性因子分类算法（MRF-ARF）的马尔可夫随机场（MRF）。基于市区的粗略标签场构造ARF，并针对不同的传感器数据利用不同的控制因素。然后，将ARF纳入MRF的数据能量中，以Sentinel-1图像的灰度共生矩阵纹理和Landsat 8图像的光谱值对沙子，植被，农田和城市地区进行分类。在实验中 使用Sentinel-1和Landsat-8图像以整体准确性和Kappa系数与其他算法一起评估提出的算法。结果表明，与比较算法相比，所提算法的整体精度在整体精度上具有约20％的优势，在Kappa系数方面至少具有0.2的优势。因此，可以解决不同传感器数据利用不充分的问题。