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.
Time Delay and Integration (TDI) sensors scan the image in one dimensions using a rectangular sensor array that integrates multiple time-delayed exposures of the same object. Due to physical constraints the TDI sensor element may have a staggered structure, in which the odd and the even sensors are horizontally separated. TDI image acquisition systems are usually employed in low signal to noise situations such as low light conditions or thermal imaging, or when high-speed readout is required. This work deals with analysis and restoration of images acquired by thermal staggered TDI sensors in the presence of mechanical vibrations. Vibrations during such an image acquisition process cause space variant image distortions in the scanning direction. These distortions include geometric warps (such as interlace comb effects) and blur. This situation is different from common case where the image degradation caused by motion is modeled as space invariant and can be treated by de-convolution techniques. The relative motion at each location in the degraded image is identified from the image using a differential technique. This information is then used to reconstruct the image using projection onto convex stes (POCS) technique. A main novelty in this work is the implementation of such methods to scanned images (column-wise). Restorations are performed with simulated images and with real mechanically degraded thermal images.
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.