Remote sensing image generation model based on generative adversarial network

Abstract

In recent years, object recognition methods of satellite remote sensing images based on deep learning have developed rapidly. The deep learning method, which needs a lot of labeled data to train the network, can achieve higher performance than the traditional method. However, it is extremely time-consuming and costly to obtain a large amount of labeled remote sensing target image data. Therefore, how to get high performance remote sensing target classifier by using only a few labeled target images training is an urgent problem to be solved. Based on WGAN-GP, this paper optimizes the constraint conditions of neural networks, and proposes a depth generation model, namely CCWGAN-GP, and applies it to remote sensing image generation. The experimental results show that the image generated by CCWGAN-GP has a high similarity to the real image, and can significantly improve the performance of the classifier under the training condition with only a few tags.