Publications

Abstract not provided.

Abstract not provided.

Hyperspectral and multispectral imagers have been developed and deployed on satellite and manned aerial platforms for decades and have been used to produce spectrally resolved reflectance and other radiometric products. Similarly, light detection and ranging, or LIDAR, systems are regularly deployed from manned aerial platforms to produce a variety of products, including digital elevation models. While both types of systems have demonstrated impressive capabilities from these conventional platforms, for some applications it is desirable to have higher spatial resolution and more deployment flexibility than satellite or manned aerial platforms can offer. Commercially available unmanned aerial systems, or UAS, have recently emerged as an alternative platform for deploying optical imaging and detection systems, including spectral imagers and high resolution cameras. By enabling deployments in rugged terrain, collections at low altitudes, and flight durations of several hours, UAS offer the opportunity to obtain high spatial resolution products over multiple square kilometers in remote locations. Taking advantage of this emerging capability, our team recently deployed a commercial UAS to collect hyperspectral imagery, RGB imagery, and photogrammetry products at a legacy underground nuclear explosion test site and its surrounds. Ground based point spectrometer data collected over the same area serves as ground truth for the airborne results. The collected data is being used to map the site and evaluate the utility of optical remote sensing techniques for measuring signatures of interest, such as the mineralogy, anthropogenic objects, and vegetative health. This work will overview our test campaign, our results to date, and our plans for future work.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

All analysis was performed using data provided to SNL in June, 2016.

Detection of cultural artifacts from airborne remotely sensed data is an important task in the context of on-site inspections. Airborne artifact detection can reduce the size of the search area the ground based inspection team must visit, thereby improving the efficiency of the inspection process. This report details two algorithms for detection of cultural artifacts in aerial long wave infrared imagery. The first algorithm creates an explicit model for cultural artifacts, and finds data that fits the model. The second algorithm creates a model of the background and finds data that does not fit the model. Both algorithms are applied to orthomosaic imagery generated as part of the MSFE13 data collection campaign under the spectral technology evaluation project.

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimi-nation. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classiffier performance. Considering these ad-ditional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform fea-ture extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This thesis develops the supervised gamma process Poisson factorization (S- GPPF) framework, a novel supervised topic model for joint modeling of count matrices and document labels. S-GPPF is fully generative and nonparametric: document labels and count matrices are modeled under a uni ed probabilistic framework and the number of latent topics is controlled automatically via a gamma process prior. The framework provides for multi-class classification of documents using a generative max-margin classifier. Several recent data augmentation techniques are leveraged to provide for exact inference using a Gibbs sampling scheme. The first portion of this thesis reviews supervised topic modeling and several key mathematical devices used in the formulation of S-GPPF. The thesis then introduces the S-GPPF generative model and derives the conditional posterior distributions of the latent variables for posterior inference via Gibbs sampling. The S-GPPF is shown to exhibit state-of-the-art performance for joint topic modeling and document classification on a dataset of conference abstracts, beating out competing supervised topic models. The unique properties of S-GPPF along with its competitive performance make it a novel contribution to supervised topic modeling.

Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase signi cantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classfication steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment.