**4. Conclusions**

Deep semantic segmentation neural networks are powerful end-to-end remote sensing image classification tools that have achieved successes in many applications. However, it is difficult to construct a training set that thoroughly exhausts all the pixel segmentation possibilities for a specific ground area; in addition, spatial information loss occurs during the network training and inference stages. Consequently, the classification results of DSSNNs are usually not perfect, which introduces a need to correct the results.

Our experiments demonstrate that when faced with complicated remote sensing images, the CRF algorithm often has difficulty achieving a substantially improved correction effect; without restricting the mechanism by using additional samples, the CRF may overcorrect, leading to a decrease in the classification accuracy. Our approach improves on the traditional CRF global processing effects by offering two advantages:

(1) End-to-end: ELP identifies which locations of the result image are highly suspected of containing errors without requiring samples; this characteristic allows ELP to be used in an end-to-end classification process.

(2) Localization: Based on the suspect areas, ELP limits the CRF analysis and update area within a small range and controls the iteration termination condition; these characteristics avoid the overcorrections caused by the global processing of the CRF.

The experimental results also show that the ELP achieves a better correction result, is more stable, and does not require training samples to restrict the iterations. The above advantages ensure that ELP is better able to adapt to correct the classification results of remote sensing images and provides it with a higher degree of automation.

The typical limitation of ELP is that, in comparison with the traditional CRF, the additional iterations, the localization process, and the update mechanism of the result image will introduce an additional computational burden. Consequently, ELP is much slower than the traditional CRF method. Fortunately, the localization process also ensures that different parts of areas do not affect each other, which makes ELP easier to parallelize. In further research, we will adjust the processing structure of ELP, facilitating a GPU implementation that enables ELP to execute faster. For semantic segmentation neural networks, differences in the training set size can cause various degrees of errors in the resulting image, which has an apparent influence on the post-processing task. In future research, to construct a more adaptive post-processing method, we will study the relationship between training/testing dataset size and the post-processing methods used and consider the problems faced by post-processing methods in more complex application scenarios.

**Author Contributions:** Conceptualization, X.P. and J.X.; data curation, J.Z.; methodology, X.P.; supervision, J.X.; visualization, J.Z.; writing—original draft, X.P. All authors have read and agree to the published version of the manuscript.

**Funding:** This research was jointly supported by the National Natural Science Foundation of China (41871236; 41971193) and the Natural Science Foundation of Jilin Province (20180101020JC).

**Conflicts of Interest:** The authors declare no conflict of interest.
