Supervised classification methods, especially recent deep learning approaches, have achieved significant success in commercial applications in Natural Language Processing (NLP) and Computer Vision (CV) domain, where large training data is available. Supervised machine learning algorithms, e.g., advanced deep neural networks, require sufficient labeled training instances which are representative of the test data. Such training data is often scarce in land cover applications given high manual labor and material cost required in manual labeling (e.g., visual inspection) and field study. This is further exacerbated by the high-dimensional nature of spatio-temporal remote sensing data. Moreover, land covers commonly show much variability across space and time, e.g., the same crop can look different in different years and in different regions due to variability in weather conditions and farming practice. Additionally, the availability of multiple RS data sources acquired at different spatial and temporal resolutions, and other heterogeneous data, e.g., elevation, thermal anomalies, and night-time light intensity, provides unique algorithmic challenges that need to be addressed.

Multi-label image classification has generated significant interest in recent years and the performance of such systems often suffers from the not so infrequent occurrence of incorrect or missing labels in the training data. In this paper, we extend the state-of the-art of training classifiers to jointly deal with both forms of errorful data. We accomplish this by modeling noisy and missing labels in multi-label images with a new Noise Modeling Network (NMN) that follows our convolutional neural network (CNN), integrates with it, forming an end-to-end deep learning system, which can jointly learn the noise distribution and CNN parameters. The NMN learns the distribution of noise patterns directly from the noisy data without the need for any clean training data. The NMN can model label noise that depends only on the true label or is also dependent on the image features. We show that the integrated NMN/CNN learning system consistently improves the classification performance, for different levels of label noise, on the MSR-COCO dataset and MSR-VTT dataset. We also show that noise performance improvements are obtained when multiple instance learning methods are used.