lronaccal

lronaccal performs radiometric corrections to images acquired by the Narrow Angle Camera aboard the Lunar Reconnaissance Orbiter spacecraft.

The LRO NAC detector has a total of 5064 pixels, divided among an A channel and a B channel. The pixels alternate between the two channels: ABABABAB, etc. Images from LROC NAC may or may not include all pixels in the acquired image. There are special summing modes that are utilized on-board the spacecraft to average detector pixels to combine them into a single output pixel value. The value of the ISIS label keyord, SpatialSumming, indicates the number of samples that were summed and averaged to result in the pixel values stored in the file. Note that this will reduce the number of samples in the output image by a factor of at most the SpatialSumming mode value.

The LROC NAC camera has the ability to acquire images of differing sizes in both line and sample. The starting hardware detector pixel for the acquired image is specified by the ISIS label keyword, SampleFirstPixel. The first pixel in the detector is indicated by a value of 0.

Dark current pixels are taken for each line from the masked pixels that lie along each edge of the image.

If SpatialSumming is 1 the dark current pixels are averaged together then this average is subtracted from all image pixels. If SpatialSumming is 2, the dark current pixels for the A and B channel are averaged separately, then the A channel dark average is subtracted from the A channel image pixels and the B channel dark average is subtracted from the B channel image pixels.

The DN level in an uncalibrated image is the sum of the true signal from the scene, the bias, the dark current, and random noise in all 3 components. The random noise in the true signal and dark current is called shot noise and the random noise in the bias is called read noise. The true signal, bias, and dark current are defined as mean values so that if the random noise were averaged down to insignificance by taking a very large number of images and averaging them, the resulting image would be the true scene, bias, and dark current with no systematic error. That implies the statistical distribution of the random noise has an average of zero, and therefore the random noise has both positive and negative values, except for the trivial case of zero random noise.

The calibration equation is:

  reportedDN = ObservedDN - MeanBias - DarkCurrent 

Where:

   ObservedDN = TrueDN + E
   E is a randomly sampled value from (mu, sigma^2) and mu=0
   TrueDN is the signal that would be reported in an idealized case of an instrument with zero noise.

Let's look at the case of a calibrated image for which the true signal is zero, a dark image. In calibration the mean bias and dark current are subtracted. The random noise term is then randomly sampled from a known distribution with a mean of zero. Since the distribution has a mean of zero, values for the random noise can be positive or negative. Therefore, the addition of random noise to a pixel with true signal near zero can result in negative DN values.

Negative reported DNs are possible when E < -1 * TrueDN. These are pixels in a very dark image that happen to have a strongly negative random noise value.

Note: ObservedDN and TrueDN both must be greater than or equal to zero. For ObservedDN, it's because the hardware is not able to report negative DN values . For TrueDN, it's because radiance and reflectivity cannot be negative. The dimmest target is one that is completely dark, and for that target TrueDN = 0.

If run on a non-spiceinited cube, this program requires access to local mission-specific SPICE kernels, in order to find the distance between the sun and the target body. When run on a spiceinited cube, this can be determined using the camera model. Using a spiceinited cube as input has the advantage of not requiring that local mission-specific kernels be available. (See spiceinit web=true.)