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Remote Sens."},{"key":"ref60","article-title":"Contrastive learning based on multiscale hard features for remote-sensing image scene classification","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref61","article-title":"Triplet contrastive learning framework with adversarial hard-negative sample generation for multimodal remote sensing images","volume":"62","author":"Chen","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref62","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014","journal-title":"arXiv:1412.6572"},{"key":"ref63","first-page":"1695","article-title":"Semantic adversarial examples","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. 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