James D. Leonard

Modeling, development, and testing of a shortwave infrared supercontinuum laser source for use in active hyperspectral imaging

A fundamental limitation of current visible through shortwave infrared hyperspectral imaging systems is the dependence on solar illumination. This reliance limits the operability of such systems to small windows during which the sun provides enough solar radiation to achieve adequate signal levels. Similarly, nighttime collection is infeasible. This work discusses the development and testing of a high-powered super-continuum laser for potential use as an on-board illumination source coupled with a hyperspectral receiver to allow for day/night operability. A 5-watt shortwave infrared supercontinuum laser was developed, characterized in the lab, and tower-tested along a 1.6km slant path to demonstrate propagation capability as a spectral light source.

An approach for evaluating assisted target detection technology

The literature is replete with assisted target recognition (ATR) techniques, including methods for ATR evaluation. Yet, relatively few methods find their way to use in practice. Part of the problem is that the evaluation of an ATR may not go far enough in characterizing its optimal use in practice. For example, a thorough understanding of a methods operating conditions is crucial, e.g., performance across different sensor capabilities, scene context, target occlusions, etc. This paper describes a process for a rigorous evaluation of ATR performance, including a sensitivity analysis. Ultimately, an ATR algorithm is deemed valuable if it is actually utilized in practice by users. Thus, quantitative analysis alone is not necessarily sufficient. Qualitative user assessment derived from user testing, surveys, and questionnaires is often needed to provide a more complete interpretation of an evaluation for a particular method. We demonstrate our ATR evaluation process using methods that perform target detection of civilian vehicles.

Field tests for round-trip imaging at a 1.4 km distance with change detection and ranging using a short-wave infrared super-continuum laser

Field tests have been conducted of a broadband illuminator for active hyperspectral imaging (HSI) using a short-wave infrared supercontinuum laser (SWIR-SCL). We demonstrated irradiance comparable to the sun for two-way measurements at a 1.4 km distance between laser and target, and performed change detection and ranging. The experimental results suggest that the range resolution of our method is ∼1.5  cm even at the 1.4 km distance. Hence, we demonstrated the possibility to perform HSI with active broadband illumination using the SWIR-SCL. To our knowledge, this experiment is the first-ever to test two-way propagation of the active HSI illumination over a long distance. The 64 W SWIR-SCL provides near sunlight-equivalent illumination over multiple square meters, and the laser could enable HSI 24 h a day, even under a cloud cover, as well as enhanced capabilities such as change detection and ranging.

Proactive sensor fusion for urban (SASO) operations

Current urban operations require intelligent methods for integrating data and transmitting fused information to users. In this paper, we evaluate the capability to deliver accurate and timely data to both a commander and the user on the ground. The ground user requires data on immediate threats for rapid reaction, whereas the commander has time to reason over information on potential threats for preventative action. Using predicted data and information affords proactive decision making on anticipated threats. Proactive action includes gathering new information, relocating for safety, and hindering the opposition from action. Complexities abound with urban operations and sensor fusion strategies, which revolve around delivering quality information (i.e. timely, accurate, confident, high throughput, and minimal cost). New strategies are needed to account for high density targets, sensor obscurations, and rapid response to meet Sustainable and Security Operations (SASO). The purpose of this paper is to evaluate the inherent responsibility of the fusion system to deliver a consistent and succinct set of information over the appropriate time window. This paper with highlight (1) proactive use of sensor resources, (2) integration of users with fielded system, and (3) communication and decision making modeling to meet operational timeliness needs.

AdaptSAPS utility for adaptive ATR development and assessment

The ATR community has a strong and growing interest in ATR systems that adapt to changing circumstances and is developing means to solve these dynamic and difficult ATR problems. To facilitate this research, the AFRL COMPASE and SDMS organizations have developed an AdaptSAPS framework for developing and assessing such adaptive ATR systems. This framework, in the form of AdaptSAPS Version 1.0, provides MATLAB code, organized procedures, and an organized database for adaptive ATR systems. SAIC is applying their Ellipse Detector (ED) to this framework to validate the AdaptSAPS procedures and to test the AdaptSAPS database. The ED previously has shown utility on a variety of sensors and ATR problems. Although computationally efficient, the ED is more complex and much more powerful than simpler detectors such as a two parameter CFAR. However, the ED is not currently implemented as an adaptive ATR. In this paper, we show the utility of the AdaptSAPS framework for developing and assessing a non-trivial adaptive ATR by embedding the SAIC ED in the AdaptSAPS framework. We point out the strong points and weak points of AdaptSAPS Version 1.0 and recommend enhancements for future versions. In particular, we comment on AdaptSAPS as delivered, the current missions and data bases in AdaptSAPS, and the current performance measures in AdaptSAPS.

Neural network ATR indexing system

We have developed a novel neural network based automatic target recognition (ATR) indexing system. This system utilizes regularization edge detection, adaptive vector quantization (AVQ) clustering, model driven feedback, and backpropagation trained networks. It can be designed to be invariant to either translation, or translation and rotation. The system incorporates both top-down and bottom-up processing to suppress background clutter.

Using model-driven feedback in neural network object recognition

In this paper we deal with the problem of edge extraction for the purpose of matching to a known model or set of models. We describe an approach to using geometric model based information within a feedback system, without the requirement for prior pose estimation by a matching process. We call this process model driven feedback (MDF). The feedback system uses a chord based transform of the image edges that is invariant either to translation or both translation and rotation, depending on its form. By representing both the data and model information using a geometrically invariant transform, and iteratively minimizing a function of the differences between the model and data transforms, the system is able to eliminate background edges while retaining object edges that are similar in shape to the model.

Automatic FLIR target recognition using a hierarchical neural system

This paper describes an approach to areas of FLIR target recognition: (1) target isolation, and (2) target classification. The method utilized for the isolation of potential target regions is based on localized texture information. The modality of the local gradient histogram is used to define both target regions and to segment these regions into subcomponents corresponding to the vehicle morphology (wheels, engine, armor, etc.). After the target regions are isolated, each region is fit with a metric (parallelogram). Each subcomponent in this region is then classified based on its shape and location within this metric. The classification is made using several neural networks with each corresponding to a specific vehicular subcomponent. The classifications of these neural networks are then used as input to another network responsible for vehicle type classification. This construct allows for azimuth and depression angle robustness of the target region, the limitations of which are discussed.