KEYWORDS: Clouds, Light sources and illumination, Short wave infrared radiation, Near infrared, Cameras, Sun, Atmospheric modeling, Multiple scattering, Signal to noise ratio, Sensors
Daytime low light conditions such as overcast, dawn, and dusk pose a challenge for object discrimination in the reflective bands, where the majority of illumination comes from reflected solar light. In reduced illumination conditions, sensor signal-to-noise ratio can suffer, inhibiting range performance for recognizing and identifying objects of interest. This performance reduction is more apparent in the longer wavelengths where there is less solar light. Range performance models show a strong dependence on cloud type, thickness, and time of day across all wavebands. Through an experimental and theoretical analysis of a passive sensitivity and resolution matched testbed, we compare Vis (0.4-0.7μm), NIR (0.7-1μm), SWIR (1-1.7μm), and eSWIR (2-2.5μm) to assess the limiting cases in which reduced illumination inhibits range performance.
KEYWORDS: Clouds, Mid-IR, Long wavelength infrared, Temperature metrology, Data modeling, Atmospheric modeling, Infrared search and track, Target detection, Humidity, Emissivity
The presence of clouds affects the detection of small airborne targets for infrared imaging. Clouds increase the signal of the background and create nonuniformity behind a desired target. This results in low and varying contrast. Clear sky conditions provide a low noise, uniform background that gives a better chance of detection. Understanding key variables of the clouds nonuniform structure allows for better detection and for accurate infrared search and track (IRST) models. Atmospheric modeling software, such as moderate resolution atmospheric transmission (MODTRAN), provides background path radiance in the emissive midwave infrared and longwave infrared bands. These modeled skies have been matched with measured skies in various conditions with low error. MODTRAN clouds, however, assume total cloud cover of uniform thickness and no varying transmission. MODTRAN clouds do not consider the spatially and radiometrically varying structures that make clouds unique. Studied spatial and radiometric characteristics of clouds are used in an empirical approach to predict cloud radiometric temperatures and structures with four simple equations. These cloud properties are measured at night to avoid solar contributions and focus on their emissive characteristics. The empirically modeled clouds are projections from measured or MODTRAN modeled clear skies. This method of modeling clouds allows for easy implementation of a nonclear sky background into IRST models. The range in which a target is first detected from its background can now be compared between clear and cloudy skies.
Drawing from techniques used to dehaze visible, three-channel RGB images, we propose an approach to separate emissive and reflected radiance in images at short distances with a microbolometer-based longwave infrared (LWIR) camera system. The best case for optimal contrast and with the most descriptive information about the scene in an LWIR image would be where no external sources are radiating toward the scene. We introduce the concept of a blackbody channel prior (BCP) to multiband LWIR imaging to describe pixels that represent objects that behave most similarly to perfect blackbodies with an emissivity near unity. Most LWIR images of outdoor scenes are degraded largely in part by reflected sky path radiance. We can estimate scene radiance with a minimized reflective component producing images with enhanced contrast. Experiments on a number of multiband images are present to demonstrate this spectral-based BCP technique and show its potential for preserving scene information while achieving contrast enhancement.
KEYWORDS: Clouds, Target detection, Mid-IR, Long wavelength infrared, Infrared radiation, Infrared detectors, Signal to noise ratio, Infrared signatures, Black bodies
Clouds can increase the signal of the background and create non-uniformity behind an airborne target which results in low and varying contrast. Clear sky conditions provide a low noise, uniform background that gives a better chance of detection. In comparison, clouds in the immediate vicinity of a target can decrease the signal to noise ratio (SNR). Understanding key variables of this non-uniform structure can allow for better detection of small UAVs. The presented radiometric and spatial characteristics for both the midwave and longwave bands are the maximum and minimum blackbody equivalent temperature and the distributions of the cloud temperatures. The spatial metrics of measurements are a one-dimensional power spectrum to understand the random spatial structure of the clouds. These cloud properties are measured at night to avoid any solar contributions and obtain their emissive characteristics. An Empirical Model is created to predict cloud radiances in any atmosphere.
Optimal longwave infrared (LWIR) scene contrast occurs when reflections from other sources are minimized, leaving only thermal emission. Applying contrast enhancement to LWIR imagery based on pixel values' spatial distribution without regard to underlying temperature and emissivity is a non-physics-based approach. For a physics-based approach, something must be known about the temperature or the objects' emissivity under observation. In remote sensing applications, atmospheric conditions are measured allowing for calculated values for downwelling and path radiance to be obtained. Then, an iterative process can be performed using well-established TES algorithms to determine temperature and emissivity within specific bands. In this paper, we propose a method using a three-band LWIR imaging system with a partial sky view to collect in-scene data to apply contrast enhancement based on spectral differences between bands. Unlike traditional contrast enhancement methods, temperature variations between each band are considered and implemented using relatively inexpensive uncooled microbolometer cameras. We detail the process used for calibrating and determining brightness temperatures with sub-band LWIR filtered cameras. Using absolute sky radiance correlated to MODTRAN6 models, we estimate objects' emissivity profiles in a scene and propose an algorithm for applying contrast enhancement.
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