This Wednesday, Alina Zare presented a talk on Machine Learning and Applications in Remote Sensing in Global Centra Spring 2019 Webinars.

Most supervised machine learning algorithms assume that each training data point is paired with an accurate training label. However, obtaining accurate training label information is often time consuming and expensive, making it infeasible for large data sets, or may simply be impossible to provide given the physics of the problem. Furthermore, human annotators may be inconsistent when labeling a data set, providing inherently imprecise label information. Given this, in many applications, one has access only to inaccurately labeled training data. For example, consider the case of single-pixel or sub-pixel target detection within remotely sensed imagery, often only GPS coordinates for targets of interest are available with an accuracy ranging across several pixels. Thus, the specific pixels that correspond to target are unknown (even with the GPS ground-truth information). Training an accurate classifier or learning a representative target signature from this sort of uncertainly labeled training data is extremely difficult in practice. Similarly, consider the case of pixel-level fusion of mis-aligned hyperspectral and LiDAR data, given mis-alignment we may not know what specific pixels in each data set correspond to the same regions on the ground but we can more easily identify small sets of pixels in which these points are members. In both of these examples, accurately labeled training is unavailable and an approach that can learn from uncertain training labels, such as Multiple Instance Learning (MIL) methods, is required. Once we learn to spot it, we find this challenge of needing to learn from weakly labeled data or uncertain training labels plagues many potential machine learning and pattern recognition applications. In this talk, I will give an overview of our research efforts in machine learning while addressing imprecise data and labels.