Since the conditional random field (CRF) model can integrate spectral and spatial-contextual information of high spatial resolution (HSR) remote sensing images in a unified framework, it becomes an effective approach to optimize the classification results. However, the results of traditional classification methods based on the CRF are sensitive to the parameters. In this paper, an adaptive conditional random field (ACRF) model is designed to utilize the spatial information more flexibly and improve the accuracy. In the ACRF, the spatial homogeneity is employed to achieve adaptive parameters control, which can evaluate the effect of the unary potentials and pairwise potentials of different pixels. Two datasets are used in the experiments, and the results demonstrate that the proposed method can improve the classification accuracy, alleviate salt-and-pepper noises, and retain detailed information. Compared with other methods, ACRF shows a better performance for HSR image classification, integrating the spatial-contextual and spectral information.

由于条件随机场（CRF）模型可以在一个统一的框架中整合高分辨率（HSR）遥感图像的光谱信息和空间上下文信息，因此成为优化分类结果的有效方法。但是，基于CRF的传统分类方法的结果对参数敏感。本文设计了一种自适应条件随机场（ACRF）模型，以更灵活地利用空间信息并提高准确性。在ACRF中，采用空间均匀性来实现自适应参数控制，该参数控制可以评估不同像素的一元电势和成对电势的影响。实验中使用了两个数据集，结果表明该方法可以提高分类的准确性，减轻盐和胡椒粉的噪音，并保留详细信息。与其他方法相比，ACRF在融合空间上下文信息和光谱信息的情况下表现出更好的HSR图像分类性能。