Unverified Commit b3d4edd4 authored by Stuart Sides's avatar Stuart Sides Committed by GitHub
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Added text to description by Dave Humm. Closes #3860 (#4107) 

* Added text to description by Dave Humm. Closes #3860

* Forgot to add to the change log file

* Update after review

* Updated after review
parent 1cd4a2ad

CHANGELOG.md

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## [Unreleased]

 - Added documentation to lronaccal and lrowaccal to describe why there are negative DNs in I/F calibrated images.





- Fixed so required files are reported instead of continuing without them. [#4038](https://github.com/USGS-Astrogeology/ISIS3/issues/4038)

isis/src/lro/apps/lronaccal/lronaccal.xml

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  <description>

    <p>

      lronaccal performs radiometric corrections to images acquired by the Narrow Angle

    Camera aboard the Lunar Reconnaissance Orbiter spacecraft.  The LRO NAC camera

    will make observations simulteously with the HiRise camera.

      Camera aboard the Lunar Reconnaissance Orbiter spacecraft.

    </p>



    <p>
@@ -46,6 +45,52 @@ 
      image pixels.

    </p>



    <p>

      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.

    </p>



    <p>

      The calibration equation is:

      <pre>  reportedDN = ObservedDN - MeanBias - DarkCurrent </pre>



        Where:

        <pre>

   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.</pre>

     </p>



    <p>

      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.

    </p>



    <p>

      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.

    </p>



    <p>

      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.

    </p>

  </description>



  <history>

isis/src/lro/apps/lrowaccal/lrowaccal.xml

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      </pre>

      If TEMPRATUREFILE is not set, the constants are loaded from $lro/calibration/WAC_TempratureConstants.????.pvl

    </p>



    <p>

      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.

    </p>



    <p>

      The calibration equation is:

      <pre>  reportedDN = ObservedDN - MeanBias - DarkCurrent </pre>



        Where:

        <pre>

   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.</pre>

     </p>



    <p>

      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.

    </p>



    <p>

      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.

    </p>



    <p>

      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.

    </p>



  </description>



  <category>