A New Remote Sensing Change Detection Data Augmentation Method Based on Mosaic Simulation and Haze Image Simulation

The quality of optical remote sensing images is largely affected by clouds and haze. In addition, the mosaicking image of multiple remote sensing images, due to objective factors such as acquiring time or climate conditions, will lead to large spectral differences in the area around the seamline. The aforementioned scenarios will seriously affect the accuracy of change detection models based on deep learning. However, there is still a lack of methods to address such issues. To solve these problems, from the perspective of training samples, this article proposed a simple but effective data augmentation method to improve the generalization ability of the deep change detection model in the region of haze cover and the seamline. First, from the characteristics of the optical remote sensing image itself, two image simulation methods are designed to conduct data augmentation, named mosaic simulation and haze image simulation. Then, the newly augmented training samples are mixed with the original training samples and then input into a deep learning model for model training. Finally, the change detection results indicate that the proposed data augmentation method can effectively improve the generalization ability of the change detection model in the region of haze cover and seamline, which has high practical value for improving the deep learning model's performance in real-world scenarios and also provides a simple but effective algorithm reference for other intelligent interpretation tasks from the perspective of training data.

Automated summary

This article proposes a simple but effective data augmentation method to improve the generalization ability of the deep change detection model in the region of haze cover and the seamline. Experimental results indicate that this data augmentation method can effectively enhance the adaptability of the change detection model in the areas with haze cover and seamline, which is of great significance for improving the performance of deep learning models in real-world scenarios and providing a simple but effective algorithm reference for other intelligent interpretation tasks from the perspective of training data.