Remote sensing (RS) image dehazing is an effective means to enhance the quality of hazy RS images. However, existing dehazing methods are ineffective in dealing with nonhomogeneous RS haze scenes. To tackle this deficiency, we design a multi-model joint estimation (M2JE) module and a self-correcting (SC) module to construct a unified end-to-end network for RS image dehazing, termed the multi-model SC network (M2SCN). Specifically, the M2JE module regards the dehazing process as a multi-model ensemble problem, so as to improve the generalization ability of the model. The SC module can gradually correct the error in the intermedia features extracted by the network, thus enabling the network to deal with nonhomogeneous hazy images. Extensive experiments are conducted to demonstrate that our proposed M2SCN performs favorably against state-of-the-art methods on popular RS image dehazing benchmark datasets.