Novel Object Detection in Remote Sensing Imagery

Abstract

Novel object detection in remote sensing is challenging due to small objects, background clutter and open-set recognition. To discover and identify objects that are not within the set of known classes in training, we developed the extreme value theory-based novel object detection framework. Specifically, we first employed the state-of-the-art object detector to extract object detections. Then, the extreme value model (EVM) is trained based on the features of extracted detections. Thus our method can characterize the distribution of outliers in the known classes-based distributions to classify novel objects. If the novelty score is larger than the pre-set threshold, we assign this sample to novel classes; otherwise the sample is classified by the original object detector. To adapt to our task, we hold out a novel set of 18 of overall 60 classes in the xView dataset in satellite imagery. The experimental results on the xView dataset show the effectiveness of our proposed approach over traditional softmax thresholding.