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Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection
Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generatedoi:10.3390/rs13101894 fatcat:ayc7rez7wzg67pdbg4ctfieo74