Novelty and Discovery Content Analysis Methods for the Planetary Data System Image Atlas
To meet these users’ needs, we implemented several image novelty and discovery tools. Novelty detectors analyze large data sets and highlight distinct imagery. These methods detect outliers by calculating how much an image’s content differs from the overall data set. We implemented algorithms ranging from spectrally (composition) sensitive models such as convolutional autoencoders to morphologically (shape) sensitive models such as Reed-Xiaoli detectors. Different models are trained for each instrument which allow users to investigate the most interesting images instead of the most recent.
We also implemented a discovery tool to quickly identify a diverse subset of interesting images. We employed a pre-trained convolutional neural network to extract content-based features from each image, then used the Discovery via Eigenbasis Modeling of Uninteresting Data (DEMUD) algorithm to rank images by unique or interesting content. To detect novel images, DEMUD iteratively builds a model to represent data that have already been displayed. The remaining items are ranked based on reconstruction error, and the item with the largest error is selected for the next iteration. This tool provides users with a highly diverse collection of images that is easy to browse without any mission-specific knowledge.
With the integration of these tools, the capabilities of the Atlas can expand to provide an overview of highly diverse images for each NASA mission, group images by similarity, and sort albums by their most interesting content.