The Markov random field (MRF) model has attracted great attention in the field of image segmentation. However, most MRF-based methods fail to resolve segmentation misclassification problems for high spatial resolution remote sensing images due to insufficiently using the hierarchical semantic information. In order to solve such a problem, this paper proposes an object-based MRF model with auxiliary label fields that can capture more macro and detailed information and apply it to the semantic segmentation of high spatial resolution remote sensing images. Specifically, apart from the label field, two auxiliary label fields are first introduced into the proposed model for interpreting remote sensing images from different perspectives, which are implemented by setting a different number of auxiliary classes. Then, the multilevel logistic model is used to describe the interactions within each label field, and a conditional probability distribution is developed to model the interactions between label fields. A net context structure is established among them to model the interactions of classes within and between label fields. A principled probabilistic inference is suggested to solve the proposed model by iteratively renewing the label field and auxiliary label fields, in which different information of auxiliary label fields can be integrated into the label field during iterations. Experiments on different remote sensing images demonstrate that our model produces more accurate segmentation than the state-of-the-art MRF-based methods. If some prior information is added, the proposed model can produce accurate results even in complex areas.