In system performance analysis, most often Signal to Noise Ratio (SNR) and system resolution (via
MTF) are analyzed separately. In this paper we advocate the use of a joint measure, namely, the Noise
Equivalent Reflectance Difference (NERD) as a function of the Spatial Resolution (SR). We
demonstrate that the NERD vs. SR captures most of the essential properties of the system's
performances and is therefore a useful tool in system evaluation. We demonstrate how various
tradeoffs affect the NERD vs. SR curve in some not so trivial way.
Aiming at night time spaceborne imaging, we compare the expected performances of a low-light-level visible sensor
with a conventional IR sensor. The low-light-level visible sensor, an electron multiplier CCD (EMCCD), is a close to
ideal photon counting device, with possibly negligible dark current noise and negligible readout noise. This fact, along
with the significant improvement of diffraction (about an order of magnitude), suggests an interesting competition
between the two technologies. In essence, this is a tradeoff between noise and optical performances (favoring the visible
channel) and basic target radiance (favoring IR). Other factors such as reliability and cost can also play an important role.
While we consider two different spectral ranges with different imaging content, we are able to conduct a cautious
theoretical comparison based on standard targets in various lighting conditions. We show that for a given set of system
parameters, even when lighting conditions are favorable, i.e. a night with a full moon, the low-light-level visible channel
performances are inferior to those of an IR channel. We also comment on the significance of the system working point
regarding performances under varying condition.
KEYWORDS: Modulation transfer functions, Minimum resolvable temperature difference, Signal to noise ratio, Spatial frequencies, Thermography, Contrast transfer function, Imaging systems, Fourier transforms, CCD cameras, Systems modeling
The edge response is one of many techniques used to calculate the MTF (Modulation Transfer Function) of an imaging system. The MTF can be used to calculate the MRTD function (Minimal Resolvable Temperature Difference) for thermal imagers or the MRC (Minimal Resolvable Contrast) function for visible image systems. Most of the conventional techniques used to calculate the MRTD or MRC functions are time consuming and can be influenced by the subjectivity of the operator. A comparison of conventional MRTD measurements for more than 300 systems is compared with the MRTD function derived using the edge response technique.
High-resolution IR scanning systems able to scan large areas quickly require linear detector arrays with more than 1000 elements and high sensitivity, achieved by TDI. ELOP initiated the development of such a long detector array in the 3-5μm spectral region. The architecture of the detector is based on several sub-segments butted together in a staggered configuration to achieve the desired detector length. One problem is the large non-uniformity of the detector, which is exacerbated by the cos4α optical effect. With the entrance pupil imaged on the cold shield aperture to enhance efficiency, the angle a becomes large. This imposes significant additional non-uniformity that has to be compensated and affects the dynamic range of the electronics. A way to overcome this problem is suggested, based on de-selecting specific pixels in any TDI channel.
Another problem is that while higher TDI levels increase the SNR, they increase the smear (blur) due to vibrations, drift etc. The optimal TDI level depends on the specific conditions of the system, namely: signal level and vibrations. Using superfluous pixels in the overlap between segments, several TDI levels can be operated simultaneously, allowing a decision to be made automatically as to the optimal TDI level for operation.
KEYWORDS: Visible radiation, Visibility, Modulation transfer functions, Atmospheric sensing, Vegetation, Interference (communication), Imaging systems, Sun, Sensors, Signal to noise ratio
Multi-spectral imaging systems are required for global monitoring of land and ocean. In order to design a new multi-spectral spaceborne system developed in Elop, and optimize physical parameters, theoretical analyses were performed. The system consists of twelve narrow spectral bands in the visible spectrum. Each spectral band was selected according to the information required for agriculture and water monitoring. NE(delta) (rho) is the principal driver for system design. NE(delta) (rho) refers to the change in target spectral reflectance, which produces a signal in the sensor equal to the noise level in that sensor. This paper describes the NE(delta) (rho) sensitivity to different kinds of scenarios such as vegetation, water and soils. Sensitivity to spectral bands in the 390-965nm spectrum, sun elevation angles and different atmospheric conditions are also presented. The system performance calculations are based on a new simulation tool developed in-house and on the Modtran code (by ALF - USA) for radiance calculations. Along with Ne(delta) (rho) , other performance parameters are presented such as Signal to Noise Ratio and NE(delta) L. From the analyses presented in this paper, it can be shown that the system design of multi-spectral imager has to take into account both scenario and physical parameters. The performance of the Multi-spectral imager is strongly dependent on the scenario and the atmospheric conditions during photography.
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