Expanding the GEOINT Toolbox
Written by Jason Schwartz
GIF 2010 Volume: 8 Issue: 5 (July/August)
Both Active And Passive Sensors Need To Be
Considered In Meeting Different GEOint Mission Objectives.
Light detection and ranging (LiDAR) measurement systems are widely used throughout the military, as well as civilian engineering and industrial sectors. These extremely precise tools rely on fixed wavelengths of laser light to travel a round-trip from an emitter to a reflective target and return to a collector. The sensor system accurately measures time throughout this trip, and advanced calculations return the distance traveled by the light beam. Divide the round-trip distance by two and the distance to the reflected target is obtained. This is the method by which almost all target ranging systems operate.
Laser ranging systems are not without shortcomings. The systems are expensive, power-hungry, rely on highly complex proprietary technology, and require exacting tuning in order to deliver both high precision and accuracy. Often these shortcomings offset the benefits of millimeter precision and sub-centimeter accuracies. In order to select the most effective measurement technology to apply to meet GEOINT mission objectives, therefore, an assessment of various capabilities, benefits and shortcomings of electro-optic (EO) tools is warranted.
GEOINT has numerous simultaneous needs for imaging and measurement. The most frequent objectives are for capturing the geospatial “lay of the land,” measuring distances to targets or points of interest, and classifying material properties of objects in the distance to understand their composition.
Frequently these questions combine into a single mission-type order, such as, “What is that object in the distance and is it in weapons range?” Note that these objectives can be met by observations from ground-based or aerial sensor platforms, either manned or unmanned. Typically, a combination of imagery, EO/infrared, and laser ranging information from several sources is analyzed and “fused” together to derive GEOINT content used to answer the questions. Taking a closer look at the role each sensor plays in deriving GEOINT content plays a critical role in the above assessment.
ACTIVE SENSORS
Laser, radar, structured-light and infrared sensors are all classified as active sensors when they send out a source signal, which has to make a round-trip to a collector in order for “sensing” to occur. Obvious benefits are derived from controlling the EO source signal, including shaping or focusing the waveform to optimally cover a target area, narrowing the wavelength spectrum to isolate a certain characteristic such as vegetation or metallic material composition, or pulsing the emitter to add useful structure to the source that can be interpreted upon return to the sensor.
These flexible control capabilities are often cited as advances of active sensors over their passive counterparts. Other noteworthy capabilities come with emitter- based sensing. Autonomous vehicle navigation across unfamiliar terrain is most successfully accomplished through the use of structured light emitters projecting lines or circles onto the ground, then cameras picking up the projected images while fast-thinking field programmable gate arrays determine the terrain on-the-fly.
The same emitter pattern recognition is used in non-GEOINT solutions of machine vision for part-recognition during automated manufacturing. Multiplewavelength laser light is used successfully to penetrate foliage and the shallow water of coastal regions in order to identify “bare earth” elevations. Most recently, advances in time-of-flight laser sensors have even included full 3-D sensing capabilities.
These advances come with significant drawbacks, however, such as increased size, weight and power consumption, which makes them less agile for deployment into man-portable and small unmanned aerial systems. Perhaps the most significant risk to GEOINT mission success that comes with the requirement of an emitter is the potential for the sensor to be detected.
Once detected, adversaries can apply countermeasures or obscurants to defeat the sensor. In worst case scenarios, active sensors can be directly targeted and defeated/destroyed by inverse-sensing: An adversary can easily determine the sensor position by receiving the emitter signal and calculating geometries or trajectories.
PASSIVE SENSORS
By comparison with active sensors, passive systems use existing electro-optic spectra, typically from sunlight, to illuminate the target objective. They use traditional optics (lens) to focus the light and use analog or digital circuitry to acquire the signal just as a commercial camera does.
Avoiding the active sensor’s emitter has many benefits in terms of reducing cost, size, weight, power and complexity. Because reflected light can arrive at the sensor from numerous sources at the same instant, passive sensors can “sense” large areas much more rapidly than an active line-scanning laser can. The long history of scientific familiarity with the physics of optics enables a much broader base of technology support for passive sensor systems.
Additionally, many of the sensing capabilities are supported openly through standards, communities and source-code packages. This degree of support further increases the attractiveness of passive sensing as a preference in meeting GEOINT mission objectives.
While active-sensor capabilities can be adjusted by manipulating both the emitter and receiver, the passive sensor’s capabilities are managed on the receiver alone. Careful attention to lens response, receptor type, size and sensitivity, as well as sensor positioning and motion, all directly contribute to effectiveness and “fitness for duty” of any passive sensor system.
Equally important to the success of the passive sensor are the analytical solutions for the geometry problem of reflected light. These solutions, based on the way all eyes “see,” are the basis for photogrammetry. For these reasons, panchromatic and multispectral passive sensors have been a mainstay of military and commercial remote sensing, surveillance and GEOINT missions for several decades.
Obviously, passive sensor systems have drawbacks as well. As the source of light is uncontrolled, much back-end processing goes into passive sensor systems to account for the variability of light sources or the almost complete lack of a light source.
In restricted conditions such as underground tunnels, inside unlit buildings, or deep underwater, these passive sensors lack the ability to self-illuminate, and are therefore inoperable. Passive systems are also subject to countermeasures and obscurants across the spectrum they observe. Bright lights cause blooms that obscure the sensor while it auto-adjusts, and colored camouflage adversely effects simpler passive sensor systems.
BEST OF BOTH WORLDS
But what if a passive sensor had the flexibility of control or the precision and accuracy of an active system such as LiDAR? What are the obstacles for LiDAR systems that include passive imaging components to produce “fused” point clouds with image context? Why can’t high-resolution hyperspectral imaging systems run at full motion video frame-rates or beyond?
These are exactly the questions being asked by advanced imaging solution providers. These developing solutions are based on passive sensor systems. The results have been tested to verify repeatable accuracies, reliable precision and simplified analytics all based on photogrammetric principles, militarized COTS hardware, and community- based open-standards solutions such as OPENCV. The objective is to fill the gap by leveraging analytic solutions for shortrange photogrammetry, object tracking and computer vision in order to deliver 40 or 80 megapixel natural-color and CIR images as geo-registered texture maps and point clouds.
Both tripod-secured “stationary” and mobile, vehicle-mounted LiDAR area-scan solutions are including digital single-lens reflex cameras as an option to capture natural-color images co-registered with laser scan cloud. These solutions collect multi-megapixel digital photos, and then register and fuse them into the sensor scene by applying the pixel values as an attribute of the LiDAR points. By comparison, the imager-only solution derives both the geo-registered cloud points and the photographic images from a single collection event, through the same lens, at the same resolution and timing. This simplified approach yields efficiencies in analysis, scalability and cost.
The technique has gained favor with NASA, which used it to measure large imaging mirrors, and with researchers at the Geomatics Engineering Research Group of the University of Leon, France, who investigated the theoretical accuracies and measured repeatable precision on the order of 1:10000 or better. This means that a linear measurement of 10 kilometers may be repeatably off by a meter or so, in the worst case.
For an initial use-case applicable across many mission objectives, Follow- Me selected urban terrain acquisition, both because of the urgent need to develop sound, reliable urban terrain acquisition tools and ubiquity of terrain as a basis for GEOINT missions. The company’s objective capabilities are to collect the equivalent of 1 foot terrain elevations out to 1,000 feet. However, because of the flexibility in configuration and variations in the COTS hardware selections, the system can easily migrate into a stand-off analytical surveillance or a targeting solution.
Just as a mechanic’s toolbox is filled with various tools, each with a specialized purpose, so too should be the deployment of sensor systems to meet the needs of the GEOINT community. Some sensors favor poor lighting, others broad daylight; some perform best from orbital or airborne platforms, others from stationary mounts such as mast-tops. No single sensor solution has become a panacea delivering impressive results on every GEOINT mission objective.
Recent advances in both sensors and analytical processing capabilities have delivered on their promise with exciting solutions that leverage 3-D, stereoscopic vision and point cloud creation. Now is an excellent time to re-evaluate the GEOINT sensor “tool box” in order to identify the critical mission objectives, the missing tools from the tool box, and look into emerging capabilities for solutions to the latest GEOINT challenges. ♦
Jason Schwartz is technology director for Follow-Me Systems.