Publications

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Forward modeling of optical spectra with absolute radiometric intensities requires knowledge of the individual transition probabilities for every transition in the spectrum. In many cases, these transition probabilities, or Einstein A-coefficients, quickly become practically impossible to obtain through either theoretical or experimental methods. Complicated electronic orbitals with higher order effects will reduce the accuracy of theoretical models. Experimental measurements can be prohibitively expensive and are rarely comprehensive due to physical constraints and sheer volume of required measurements. Due to these limitations, spectral predictions for many element transitions are not attainable. In this work, we investigate the efficacy of using machine learning models, specifically fully connected neural networks (FCNN), to predict Einstein A-coefficients using data from the NIST Atomic Spectra Database. For simple elements where closed form quantum calculations are possible, the data-driven modeling workflow performs well but can still have lower precision than theoretical calculations. For more complicated nuclei, deep learning emerged more comparable to theoretical predictions, such as Hartree–Fock. Unlike experiment or theory, the deep learning approach scales favorably with the number of transitions in a spectrum, especially if the transition probabilities are distributed across a wide range of values. It is also capable of being trained on both theoretical and experimental values simultaneously. In addition, the model performance improves when training on multiple elements prior to testing. The scalability of the machine learning approach makes it a potentially promising technique for estimating transition probabilities in previously inaccessible regions of the spectral and thermal domains on a significantly reduced timeline.

Deep learning (DL) has been widely proposed for target detection in hyperspectral image (HSI) data. Yet, standard DL models produce point estimates at inference time, with no associated measure of uncertainty, which is vital in high-consequence HSI applications. In this work, we develop an uncertainty quantification (UQ) framework using deep ensemble (DE) learning, which builds upon the successes of DL-based HSI target detection, while simultaneously providing UQ metrics. Specifically, we train an ensemble of convolutional deep learning detection models using one spectral prototype at a particular time of day and atmospheric condition. We find that our proposed framework is capable of accurate target detection in additional atmospheric conditions and times of day despite not being exposed to them during training. Furthermore, in comparison to Bayesian Neural Networks, another DL based UQ approach, we find that DEs provide increased target detection performance while achieving comparable probabilities of detection at constant false alarm rates.

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In this effort, random noise data augmentation is compared to phenomenologically-inspired data augmentation for a target detection task, evaluated on the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model "MegaScene" simulated hyperspectral dataset. Random data augmentation is commonly used in the machine learning literature to improve model generalization. While random perturbations of an input may work well in certain fields such as image classification, they can be unhelpful in other applications such as hyperspectral target detection. For instance, random noise augmentation may not be beneficial when the applied noise distribution does not match underlying physical signal processes or sensor noise. In the context of a low-noise sensor, augmentation mimicking material mixing and other practical spectral modulations is likely to be more effective when used to train a target detector. It is therefore important to utilize a data augmentation strategy that emulates the natural variability in observed spectra. To validate this claim, a small fully connected neural network architecture is trained using an ideal hemispheric reflectance materials dataset as a trivial baseline. That dataset is then augmented using Gaussian random noise and the model is retrained and again applied to MegaScene. Finally, augmentation is instead performed using phenomenological insight and used to retrain and reevaluate the model. In this work, the phenomenological augmentation implements only simple and commonly encountered spectral permutations, namely linear mixing and shadowing. Comparison is made between the augmented models and the baseline model in terms of low constant false alarm rate (CFAR) performance.

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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.

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Spatially unresolved targets, such as vehicles, reflect a radiance spectrum that is more complicated than the simple linear mixing of target and background material spectra. Although different materials in the target and background classes have Bi-Directional Reflectance Function (BRDF) dependent spectra, the unique geometry and orientation of a target object, in addition to the solar illumination and observation angles, define a more complex Tri-Directional Reflectance Function (TRDF) in which glints and shadows are important spectral contributors. For different observation scenarios, the apparent spectra of an unresolved target may vary significantly. However, since solar and observation angles are often known to operators of remote sensing instruments, well characterized TRDFs for specific targets allow for refinement in the estimation of the expected spectra of different unresolved targets. More accurately defined target classes may lead to improved performance in established subpixel target detection algorithms for remote sensing.