Extremely thick haze caused by air pollution is observed in many satellite images of the earth, and in particular over eastern China. Standard image display software typically provides satisfactory visualization of the ground through automated or user-driven scaling to enhance contrast; however, it does not perform well with these highly polluted scenes, where the haze is spatially non-uniform. Furthermore, estimation of surface reflectance using standard atmospheric correction software is highly problematic under these conditions due to very low visible transmission of the haze coupled with lack of knowledge of its optical properties, which may not conform to the haze or aerosol models in the software. In this paper we show that a version of the empirical Quick Atmospheric Correction (QUAC) algorithm, adapted for spatially dependent scattering, produces visually satisfying imagery of the entire ground in multispectral satellite scenes containing thick haze, and that the output reflectance spectra appear to be realistic enough for performing basic surface classification. The QUAC algorithm is applicable to multispectral and hyperspectral imagery with any number of wavelength bands, including true color (RGB) imagery, and does not require radiometrically calibrated data.
A miniaturized, lightweight turn-key hyperspectral sensor package incorporating a single, monolithic spectrograph, telescope and navigation system is being built for airborne applications on small, Unmanned Aircraft Systems (UAS). The sensor is based on Corning’s existing MicroHSI 410 Vis/NIR Selectable Hyperspectral Airborne Remote sensing Kit (SHARK) currently used for airborne agricultural monitoring. Under DOE sponsorship, we are extending the approach to cover the full spectral range from 0.4-2.5 microns with a single spectrograph. This will enable rapid aerial surveys of vegetative mass, quality, and carbon sequestration. Other applications include mineralogy, agriculture, and intelligence/surveillance/reconnaissance (ISR). <p> </p>The sensor features an Offner-type spectrograph machined from a single transmissive block. The monolithic construction provides an unprecedented combination of high performance, low cost and low size, weight, and power. It has an f/1.4 aperture, 5 nm resolution, and measures only 46mm x 60mm x 76mm. The spectrograph block is coupled to a sterling-cooled, back-thinned, HgCdTe FPA covering 0.4-2.5 micron spectral range. The flight package, including spectrograph, camera, telescope, and navigation system weighs less than 2.4kg and can fit on group 1 UASs.<p> </p> In this paper, we present the design and optical performance of the sensor, and a detailed physical model of detection performance in standard, airborne hyperspectral sensing applications. At 100 Hz data rate, the sensor will achieve shotnoise limited performance with SNR > 250 from 0.4-1.7 microns and SNR<100 between 2-2.3 microns. Operating procedures for airborne monitoring of vegetative properties are also discussed. Initial test flights on a UAS are scheduled for next summer.
We present progress being made in the passive optical remote detection of ground surface vibration. With proper design, minute seismic surface waves may be captured using remote visible imagery. The utility of subband steerable filters to the detection of surface vibrations in the absence of inherent image contrast is demonstrated. Detections with the filters are shown with laboratory data and compared to Fourier transform results over a range of surface vibrational amplitudes. We present an analysis of the optical measurements of ground surfaces performed during the passing of nearby trains with discussion of the hardware, software, and detection clutter sources. Results from optical remote sensing are interpreted using additional accelerometer measurements and image processing.
Vibration waveforms in materials appear in video as a minuscule fluctuation in the light scattered into the camera. By inferring from processed video how vibrational energy propagates through an article to be inspected, we may detect local material anomalies. We report progress in developing measurement protocols and technologies to perform standoff nondestructive inspection of materials for defects using video image processing. In particular we show promising results from a protocol that conforms to relatively inexpensive hardware.