Abstract: Systems and methods for preprocessing images are provided. One example computer-implemented method includes accessing multiple images including a first object, a second object, and/or a third object; ranking the first object, second object, and third object based on rareness of the first object, the second object, and the third object; grouping ones of the multiple images for which the first object is labeled and the second object is unlabeled; identifying the second object as a threshold object, based on the second object ranking being a rarest unlabeled object in said group; for each of the images in the group, based on the first object being more rare than the threshold object: computing a convex hull, which surrounds the first object(s) included in the image; and modifying the image to omit a part of the image outside of the convex hull.

Abstract: An automated system is provided for classifying materials in remotely-sensed imagery based on automated construction of a dynamic classifier—namely, a classifier that is automatically trained on the same image to which it is then subsequently applied. A first automated process identifies high confidence exemplars of each class using tailored classification techniques. This data is then used to train a supervised classification model (e.g., discriminant analysis), and the resultant classifier is applied to other pixels in the image that are unclassified or uncertain. Dynamic classification is automatically customized to the current image and can yield a more accurate and efficient material classification versus a static (image-independent) or manually trained classifier.

Abstract: An automated system is provided for classifying materials in remotely-sensed imagery based on automated construction of a dynamic classifier—namely, a classifier that is automatically trained on the same image to which it is then subsequently applied. A first automated process identifies high confidence exemplars of each class using tailored classification techniques. This data is then used to train a supervised classification model (e.g., discriminant analysis), and the resultant classifier is applied to other pixels in the image that are unclassified or uncertain. Dynamic classification is automatically customized to the current image and can yield a more accurate and efficient material classification versus a static (image-independent) or manually trained classifier.