Contamination Robust Learning: A Novel Task for Geospatial Scene Classification

Deep neural networks (DNNs) have shown dominance in many tasks such as image classification. Its effectiveness depends on a large number of high-quality training data, which are often manually selected with a high cost from the rough original dataset that suffer from a certain degree of contamination. Existing researches on dataset contamination mainly focuses on label noise. However, for geospatial scene imagery (remote/proximity sensing scene images) which are usually collected automatically, some images have been contaminated before manual annotation. In fact, label noise and data noise are essentially the same problem (mismatch between semantics and features), which should not be considered by either party alone. In this letter, the problem of data contamination in geospatial scene classification is re-emphasized and treated together with label contamination as sample contamination. On this basis, a novel learning task, contamination robust learning (CRL) is proposed. For this task, new datasets, corresponding baselines, and model evaluation methods are proposed. In addition, a new robust learning method named progressive steady updating learning (PSUL) is proposed, which achieves state-of-the-art results among existing methods and outperforms baseline methods by 3.6%. The code and new datasets are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hongdoubao123/CRL</uri>.