LAUSR

Haze in remote sensing images (RSIs) usually causes serious radiance distortion and image quality degeneration, resulting in difficult remote sensing inversion and interpretation. Under the condition of dense haze, the existing dehazing methods still experience problems to be solved: 1) the texture details and spectral characteristics in RSIs cannot be restored well and 2) small-scale objects, such as cars and ships, which often consist of only a few pixels in RSIs, cannot be effectively highlighted in dehazed results. To solve these issues, we propose a novel dynamic collaborative inference learning (DCIL) framework that can significantly restore real surface information from dense hazy RSIs. First, we design a dynamic mutual enhancement (DME) mechanism to reinforce the low-level texture features by integrating primary information and semantic information at different levels. Second, we propose a spectrum-aware aggregation (SAA) strategy to mine the spectrum features among multiscale restored results, which can fully capture spectral characteristics. Third, we build a collaborative criterion by constructing a Siamese network structure in the training stage to improve the robustness and generalization performance of DCIL considering the diversity of the scale range and view change of RSIs. Finally, we propose a phased learning strategy (PLS) to deduce the implicit haze-relevant features by gradually increasing the concentration of haze, which can effectively address small-scale objects obscured by dense haze. To this end, we develop two synthetic remote sensing dehazing datasets to train our model, which can also alleviate the dilemma of hazy RSI datasets shortages. Experimental results on both synthetic datasets and real remote sensing hazy images demonstrate that the proposed DCIL can attain significant progress compared to competing methods. The two synthetic hazy datasets are available at https://github.com/Shan-rs/DCI-Net.