{'fraction_non_alphanumeric': 0.02634245187436677, 'fraction_numerical': 0.0, 'mean_word_length': 5.292993630573249, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Robust Deep Metric Learning for Remote Sensing Images with Noisy Annotations

Manual and automatic annotation of Remote Sensing (RS) scenes are rather complex tasks which may unavoidably introduce some degree of mislabeled data in large-scale archives. In this regard, noisy annotations become an important constraint for deep metric learning-based RS characterization methods since most of them are trained in a supervised way. To address this problem, here we investigate the use of deep metric learning for characterizing RS scenes with noisy labels. Specifically, we consider the Normalized Softmax Loss and develop a robust extension, i.e., the Robust Normalized Softmax Loss (RNSL), in order to effectively capture the semantic relationships among RS scenes with mislabeled ground-truth information. The conducted experiments, using the K-NN classifier and two benchmark RS image archives, show the potential of the proposed approach with respect to other state-of-the-art methods.