The BasicPlatform plugin was created to provide backward compatibility in DIRSIG5 for the sensor collection capabilities in DIRSIG4.

The inputs to this plugin are the DIRSIG4 era platform, motion (either a GenericMotion or a FlexMotion description) and a tasks file. This allows the plugin to emulate the sensor collection capabilities in DIRSIG4. For more details, see the usage section at the end of this manual.

Implementation Details

Spatial and Temporal Sampling

This sensor plugin performs spatial and temporal sampling differently than DIRSIG4 did. Specifically, DIRSIG4 used discrete sampling approaches to separately sample the spatial and temporal dimensions of flux onto a given pixel and DIRSIG5 simultaneously samples the spatial and temporal dimensions with uniformly distributed samples.

In DIRSIG4, the amount of sub-pixel sampling was controlled on a per focal plane basis using rigid N x M sub-pixel sampling. Furthermore, delta sampling vs. adaptive sampling was a choice for the user. In contrast the BasicPlatform plugin uniformly samples the detector area and the number of samples used for each pixel is adaptive and driven by the convergence parameters. In general, the entire spatial response description in the input file is ignored by this plugin.

The DIRSIG4 temporal integration feature included an integration time and an integer number of samples to use across that time window. The temporal sampling approach brute-force resampled the entire detector for each sample, which resulted in a proportional increase in run-time. The DIRSIG5 BasicPlatform plugin ignores the number of temporal samples and instead uniformly distributes the ray times across the integration time window.

Advanced Features

This plugin includes a few advanced features that should become permanent features at some point in the future.

Important These advanced feature configurations are not supported by the graphical user interface (GUI) and will get deleted if the user loads and saves the .platform file in the Platform Editor.

Hypersampling

The DIRSIG5 radiance convergence algorithm powers the adaptive sampling approach to the simulation solution. It allows pixels that are less complex spatially, spectrally or temporally to use fewer samples (paths), and conversely more complex pixels to use more samples (paths). The algorithm initializes itself with an estimate of the pixel radiance using a minimum number of paths (Nmin). It then incrementally adds more paths to the radiance solution and compares the radiance at the reference wavelength from the previous N-1 sample solution to the current N sample solution. If the radiance difference is greater than the threshold, then another sample is added to the solution until the threshold is satisfied or the maximum number of samples (Nmax) is reached.

One of the failure corner cases for this algorithm is when there is a significant sub-pixel contribution that is missed by the minimum number of samples. For example, a small object within a pixel that is a major contribution to the pixel due to it having a high reflectance or a high temperature. If the object is small (in terms of area), it could easily be missed by a low number of minimum samples (Nmin). Hence, the solution could converge after only Nmin samples with all the samples having randomly sampled the portion of the pixel represented by something other than this small but important object. This corner case can also manifest with larger targets that contain a small but significant component. For example, a small chrome feature on a car that reflects a solar glint in the reflective region or a hot exhaust pipe in the thermal region. Increasing the minimum number of samples would increase the likelihood that such small but important contributors are not missed, but increasing the minimum number of samples for all the pixels can have a dramatic impact of run-time.

Ideally, we want a way to automatically identify specific pixels that contain these corner cases and utilize more samples in those pixels only. Although it is not automatic, this can be accomplished by using the hypersampling feature that allows the simulation to dynamically increase the Nmin and Nmax samples used in the convergence algorithm for select pixels. To utilize the feature, the user needs to label important targets and specify a sampling multiplier that increases the sample rates when any of the labeled targets are within a pixel. When a pixel IFOV overlaps these labeled targets, the Nmin and Nmax values will be increased by a user-defined multiplier.

Labeling Important Objects

The key step is to label instances that are "important". Instance labeling is only supported in the GLIST file and is accomplished by setting the name (or tags) attribute in either a static or dynamic instance to start with the specific string "::IMPORTANT::".

</geometrylist>
  ...
  <object>
    <basegeometry>
      <obj><filename>objects/example.obj</filename></obj>
    </basegeometry>
    <staticinstance name="::IMPORTANT::">
      ...
    </staticinstance>
    ...
  </object>
</geometrylist>

Instances of any type of base geometry (GDB, OBJ, GLIST or primitive) can be flagged as important. Not all instances of a given base geometry need to be labeled. For example, in some cases you might only want to label "fast moving" instances as important.

Tip If the ::IMPORTANT:: string is added to the tags for the <object>, all the instances will inherit that tag and will get hyper-sampled.

Enabling Hypersampling

To enable hypersampling in sensor, the user must hand-edit the .platform file and add a new element. The hypersamplingmultiplier element specifies a multiplier that modifies the minimum and maximum number of samples used in each pixel when an important target overlaps the pixel IFOV. The use of this special element is shown within the <samplestrategy> element in the example below:

          <focalplane enabled="true" name="Example FPA">
            <capturemethod type="simple" name="Simple">
              <samplestrategy type="central">
                <hypersamplingmultiplier>50000</hypersamplingmultiplier>
              </samplestrategy>
              <imagefile>
                ...
              </imagefile>
              <spectralresponse>
                ...
              </spectralresponse>
              <spatialresponse>
                ...
              </spatialresponse>
            </capturemethod>
            ...
          </focalplane>

See the SubPixelObject1 demo for a working example.

Color Filter Array (CFA) Patterns

As an alternative to assigning a focal plane one (or more) channels that are modeled at every pixel, the option also exists to generate mosaiced output of a color filter array (CFA) that can be demosaiced as a post processing step. The currently supported CFA patterns include the Bayer and TrueSense patterns. These are configured by adding the <channelpattern> section to the <spectralresponse> for a given focal plane. Each channel pattern has a list of pre-defined "filters" that must be mapped to the names of channels defined in the <channellist>. Examples for each pattern are shown in the following sections.

Note The DIRSIG package does not come with a tool to demosaic images produced with CFA patterns. Standard algorithms are available in a variety of software packages including OpenCV (see the cv::cuda::demosaicing() function).

Example Bayer Pattern

A 3-color Bayer pattern must have channel names assigned to its red, green and blue filters:

              <spectralresponse>
                <bandpass spectralunits="microns">
                  ...
                </bandpass>
                <channellist>
                  <channel name="RedChannel" ... >
                    ...
                  </channel>
                  <channel name="GreenChannel" ... >
                    ...
                  </channel>
                  <channel name="BlueChannel" ... >
                    ...
                  </channel>
                </channellist>
                <channelpattern name="bayer">
                  <mapping filtername="red" channelname="RedChannel"/>
                  <mapping filtername="green" channelname="GreenChannel"/>
                  <mapping filtername="blue" channelname="BlueChannel"/>
                </channelpattern>
              </spectralresponse>

Example TrueSense Pattern

A 4-color TrueSense pattern must have channel names assigned to its pan, red, green and blue filters:

              <spectralresponse>
                <bandpass spectralunits="microns">
                  ...
                </bandpass>
                <channellist>
                  <channel name="PanChannel" ... >
                    ...
                  </channel>
                  <channel name="RedChannel" ... >
                    ...
                  </channel>
                  <channel name="GreenChannel" ... >
                    ...
                  </channel>
                  <channel name="BlueChannel" ... >
                    ...
                  </channel>
                </channellist>
                <channelpattern name="truesense">
                  <mapping filtername="pan" channelname="PanChannel"/>
                  <mapping filtername="red" channelname="RedChannel"/>
                  <mapping filtername="green" channelname="GreenChannel"/>
                  <mapping filtername="blue" channelname="BlueChannel"/>
                </channelpattern>
              </spectralresponse>

See the ColorFilterArray1 demo for a working example.

Active Area Mask

The user can supply an "active area mask" for all the pixels in the array via a gray scale image file. The brightness of the pixels are assumed to convey the relative sensitivity across the pixel area. A mask pixel value of 0 translates to zero sensitivity and a mask pixel value of 255 translated to a unity sensitivity. Hence, areas of the pixel can be masked off to model front-side readout electronics or to change the shape of the pixel (e.g. circular, diamond, etc.). The mask is used to override the uniform pixel sample with an importance based approach. Therefore, the active area image can contain gray values (values between 0 and 255) that indicate the pixel has reduced sensitivity in that specific area.

The active area mask is supplied via the <activearea> XML element within the <focalplane> element (see example below):

           <focalplane ... >
             <capturemethod type="simple">
               <spectralresponse ... >
                 ...
               </spectralresponse>
               <spatialresponse ... >
                 ...
               </spatialresponse>
               <activearea>
                 <image>pixel_mask.png</image>
               </activearea>
             </capturemethod>
             <detectorarray spatialunits="microns">
               ..
             </detectorarray>
           </focalplane ... >

The <image> element specifies the name of the 8-bit image file (JPG, PNG, TIFF, etc.) used to drive the pixel sampling.

Note that this pixel area sampling can be combined with the PSF feature described here.

Lens Distortion

The ideal pinhole camera approximation employed by this plugin can be enhanced via the use of the built-in lens distortion model. This common 5-parameter distortion model allows the user to introduce common distortions including barrel and pin cushion. The governing equation has 3 radial terms (k1, k2 and k3) and 2 tangential terms (p1 and p2) that define the transform of an undistorted point (x,y) on the focal plane into a distorted point (x',y').

The equations for the 5-parameter lens distortion model.

\begin{eqnarray} x' &=& x \cdot (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + 2p_1 xy + p_2(r^2 + 2x^2)\\ y' &=& y \cdot (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + 2p_2 xy + p_1(r^2 + 2y^2) \end{eqnarray}

Units for x and y are in focal lengths and centered about the principal point of the instrument.

Since DIRSIG is a reverse ray-tracing approach, the plugin is performing the inverse of this mapping to compute the appropriate rays to sample the correct regions of the object plane. It should be noted that these equations are not invertable, so the inversion is performed using an iterative solution to find the undistorted point (x,y) from a distorted point (x',y') on the focal plane.

The 5-parameter model is configured by introducing the <distortion> element insider the <properties> of a given instrument. At this time, this requires a manual edit of the XML .platform file.

<instrument type="generic" name="Name">
  <properties>
    <focallength>35</focallength>
    <distortion>
      <k1>5</k1>
      <k2>2.5</k2>
      <k3>-100</k3>
      <p1>0.1</p1>
      <p2>0.05</p2>
    </distortion>
  </properties>

The parameter set above produces a pronounced "pincushion" distortion when looking at a checkerboard pattern.

lens distortion
Figure 1. Output of the DIRSIG simulation using the above lens distortion parameters.

See the LensDistortion1 demo for a working example.

Time-Delayed Integration (TDI)

Time-delayed integration (TDI) is a strategy to increase signal-to-noise ratio (SNR) by effectively increasing the integration time for each pixel. Rather than using a single pixel with a long integration time, TDI uses a set of pixels with a short integration time that will image the same location over a period of time. This is usually utilized in a pushbroom collection system, where a 1D array is scanned across a scene using platform motion to construct the 2nd dimension of the image. TDI is typically accomplished by using a 2D array in pushbroom mode and using the 2nd dimension of that 2D array as TDI "stages" that will be used to re-image the same location as the system as the array is scanned by the platform in the along-track dimension. The figure below illustrates this concept [1].

fiete tdi diagram
Figure 2. Time-Delayed Integration Concept by Fiete.

In order to use the TDI feature, you need to have the following components setup in the <focalplane> configuration in the .platform file:

  1. The Y dimension of the detector array is assumed to be the TDI stages. Hence, the <yelementcount> in the <detectorarray> should be set to indicate the number of stages desired (greater than 1).

  2. The temporal integration time needs to be set. Hence, the <time> element in the <temporalintegration> should be set to a non-zero length.

  3. The TDI feature needs to be explicitly enabled. This is accomplished by setting the tdi attribute in the <temporalintegration> element to "true".

  4. To introduce detector noise, you need to enable and configure the built-in detector model.

Below is an example excerpt from a .platform file that has these components configured. In this case, the array has 12 stages of TDI (see the <yelementcount> element.

          <focalplane>
            <capturemethod>
              ...
              <temporalintegration tdi="true">
                <time>2.0e-05</time>
                <samples>1</samples>
              </temporalintegration>
              <detectormodel>
                <quantumefficiency>1.0</quantumefficiency>
                <readnoise>20</readnoise>
                <darkcurrentdensity>8.0e-07</darkcurrentdensity>
                <minelectrons>0</minelectrons>
                <maxelectrons>10e+02</maxelectrons>
                <bitdepth>12</bitdepth>
              </detectormodel>
              ...
            </capturemethod>
            <detectorarray spatialunits="microns">
              <clock type="independent" temporalunits="hertz">
                <rate>50000</rate>
                <offset>0</offset>
              </clock>
              <xelementcount>250</xelementcount>
              <yelementcount>12</yelementcount>
              <xelementsize>2.00000</xelementsize>
              <yelementsize>2.00000</yelementsize>
              <xelementspacing>2.00000</xelementspacing>
              <yelementspacing>2.00000</yelementspacing>
              <xarrayoffset>0.000000</xarrayoffset>
              <yarrayoffset>0.000000</yarrayoffset>
              <xflipaxis>0</xflipaxis>
              <yflipaxis>0</yflipaxis>
            </detectorarray>
          </focalplane>

See the TimeDelayedIntegration1 demo for a working example.

Important Although the stages are spatially and temporally sampled separately, the stages are solved as a single "problem" in the DIRSIG5 radiometry core. That means the paths per pixel is distributed across all the stages. Therefore, we recommend that you proportionally increase the convergence parameters as you increase the number of stages. For example, if you have N stages, you might consider increasing the minimum and maximum number of paths/pixel by N.

Experimental Features

This plugin includes a few experimental features that may become permanent features at some point in the future. Please utilize these features with caution since the feature maybe removed or replaced by a more mature version.

Important These experimental feature configurations are not supported by the graphical user interface (GUI) and will get deleted if the user loads and saves the .platform file in the Platform Editor.

Point Spread Function (PSF)

The user can currently describe the modulation transfer function (MTF) of the optical system via a single Point Spread Function (PSF). This function describes the spread of a point (zero area) in the object plane onto the focal plane after passing through the optical system.

psf diagram
Figure 3. The point spread function (PSF) of an optical system.

Note that a single PSF is provided and hence, this is currently not a position dependent PSF. It should also be noted that the PSF is wavelength constant for the wavelengths captured by the respective focal plane (different PSF configurations can be assigned to different focal planes).

This feature is enabled by manually editing a DIRSIG4 .platform file and injecting the <psf> XML element in the <focalplane> element. The PSF is used in a 2-step importance sampling scheme to emulate the convolution of the PSF with the pixel area. In general, more samples per pixel are required to faithfully reproduce the impacts of the PSF on the output. Not that for PSF that is highly structured and/or very wide (spans many pixels), the maximum number of paths per pixel might need to be increased (see the DIRSIG5 manual for details).

Using the built-in Gaussian Profile

For some simple, clear aperture optical systems a Gaussian approximation of the center lobe of the classic Airy disk diffraction pattern is a decent first order solution. For this scenario, the user simply has to specify the the number of pixels that 1 sigma corresponds to in the Gaussian shape. In the example below, a Gaussian PSF will be used that has the 1 sigma width scaled to span 3.8 pixels:

           <focalplane ... >
             <capturemethod type="simple">
               <spectralresponse ... >
                 ...
               </spectralresponse>
               <spatialresponse ... >
                 ...
               </spatialresponse>
               <psf>
                 <width>3.8</width>
               </psf>
             </capturemethod>
             <detectorarray spatialunits="microns">
               ..
             </detectorarray>
           </focalplane ... >

Using a User-Supplied Profile

In this case the PSF is supplied as a gray scale image file (JPG, PNG, TIFF, etc. via the <image> element) and the user must define the extent of the image (via the <scale> element) in terms of focal plane pixels. In the example below, the Airy disk pattern in the PNG file has an equivalent width of 10 pixels on the focal plane.

           <focalplane ... >
             <capturemethod type="simple">
               <spectralresponse ... >
                 ...
               </spectralresponse>
               <spatialresponse ... >
                 ...
               </spatialresponse>
               <psf>
                 <image>airy_psf.png</image>
                 <scale>10</scale>
               </psf>
             </capturemethod>
             <detectorarray spatialunits="microns">
               ..
             </detectorarray>
           </focalplane ... >
Note The example above mirrors the setup illustrated in the PSF diagram at the start of this section. The image file contains the Airy disk pattern shown as projected onto the focal plane. The size of that spread pattern captured in the image spans approximately 10 pixels. Hence, the scale is 10. The scale is not related to the number of pixels in the PSF image but rather to the effective size of the pattern in focal plane pixel units.
Important Because the PSF effectively increases the sampling area of the pixel, you might need to consider increasing the convergence parameters. The easiest way to explore if this is required for your simulation is to turn on the sample count truth and see if you are regularly hitting the maximum number of paths/pixel (samples/pixel).

Internal Detector Modeling

By default, the BasicPlatform plugin is producing an at aperture radiometric data product that is by default in spectrally integrated radiance units of watts/(cm2 sr). Depending on the various options set, these units can change. For example:

  • The flux term can be switched from watts to milliwatts, microwatts, or photons/second.

  • The area term can be switched from per cm2 to per m2.

  • Enabling "temporal integration" will integrate the seconds in the flux term resulting in joules, millijoules, microjoules or photons (depending on the flux term settings).

The default output radiometric data products are provided so that users can externally perform modeling of how specific optical systems, detectors and read-out electronics would capture the inbound photons and produce a measured digital count.

For users that do not wish to perform this modeling externally, the internal detector model described here provides the user with the option to produce output data that is in digital counts and features Shot (arrival) noise, dark current noise, read noise and quantization. The general flow of the calculations is as follows:

  1. The at aperture spectral radiance [photons/(s cm2 sr)] is propagated through a user-defined, clear aperture to compute the at pixel spectral irradiance [photons/(s cm2)].

  2. The area of the pixel is used to compute the spectral photon rate onto the pixel [photons/s].

  3. The integration time of the pixel is used to temporally integrate how many photons arrive onto the pixel [photons].

  4. The channel relative spectral response (RSR) and a user-define scalar quantum efficiency (QE) is used to convert the spectral photon arrival rate into the number of electrons generated in the pixel [electrons].

  5. Shot noise is added using a Poisson distribution with a mean of the number of electrons [electrons].

  6. Dark current noise is added using a Poisson distribution with a mean of the number of electrons [electrons].

  7. Read noise is added using a Poisson distribution with a mean of the number of electrons [electrons].

  8. The number of electrons is scaled using a user-defined analog-to-digital converter (ADC) to produce a digital count (DC).

  9. The final digital count value is written to the output.

detection diagram
Figure 4. The flow diagram of the internal detector model.

The internal detector model must be configured by manually editing the .platform file to add the <detectormodel> element and few other elements. The following .platform file excerpt an be used as a template:

        <instrument type="generic" ... >
          <properties>
            <focallength>125</focallength>
            <aperturediameter>0.017857</aperturediameter>
            <aperturethroughput>0.50</aperturethroughput>
          </properties>
          <focalplane ... >
            <capturemethod type="simple">
              <imagefile areaunits="cm2" fluxunits="watts">
                <basename>vendor1</basename>
                <extension>img</extension>
                <schedule>simulation</schedule>
                <datatype>12</datatype>
              </imagefile>
              <spectralresponse ... >
                ...
              </spectralresponse>
              <spatialresponse ... >
                ...
              </spatialresponse>
              <detectormodel>
                <quantumefficiency>0.80</quantumefficiency>
                <readnoise>60</readnoise>
                <darkcurrentdensity>1e-05</darkcurrentdensity>
                <minelectrons>0</minelectrons>
                <maxelectrons>100e03</maxelectrons>
                <bitdepth>12</bitdepth>
              </detectormodel>
              <temporalintegration>
                <time>0.002</time>
                <samples>10</samples>
              </temporalintegration>
            </capturemethod>
          </focalplane>
        </instrument>

Requirements

  • The capture method type must be simple. This internal model is not available with the "raw" capture method, which is used to produce spatially and spectrally oversampled data for external detector modeling.

  • The aperture size must be provided to compute the acceptance angle of the system in order to compute the at focal plane irradiance from the at aperture radiance. This is specified using the <aperturediameter> element in the <instrument><properties>. The units are meters.

  • The focal plane must have temporal integration enabled (a non-zero integration time) in order to compute the total number of photons arriving onto the detectors.

Note The areaunits and fluxunits for the <imagefile> are ignored when the detector model is enabled. These unit options are automatically set to the appropriate values to support the calculation of photons onto the detectors.

Parameters

The following parameters are specific to the detector model and are defined within the <detectormodel> element of the focal plane’s capture method.

quantumefficiency

The average spectral quantum efficiency (QE) of the detectors. This spectrally constant parameter works in conjunction with the relative spectral responses (RSR) of the channel(s) applied to the detectors. The channel RSR might be normalized by area or peak response, hence the name relative responses. This QE term allows the RSR to be scaled to a spectral quantum efficiency. If the RSR curve you provide is already a spectral QE, then set this term to 1.0.

Important Don’t forget to account for the gain or bias values that are defined for each channel. In practice, we suggest not introducing an additional gain transform and leave the channel gain and bias values at the default values of 1.0 and 0.0, respectively.
readnoise

The mean read noise for all pixels in electrons (units are electrons).

darkcurrentdensity

The dark current noise as an area density. The use of density is common since it is independent of the pixel area and will appropriately scale as the pixel area is increased or decreased (units are Amps/m2).

<minelectrons>, <maxelectrons> and <bitdepth>

The analog-to-digital (A/D) converter (ADC) translates the electrons coming off the array into digital counts. Electron counts below the minimum or above the maximum will be clipped to these values. The ADC setup is used to linearly scale the electrons within the specified min/max range into the counts into the unsigned integer range defined by the <bitdepth>. For the example above, a 12-bit ADC will produced digital counts ranging from 04095.

Note Electron counts below the minimum or above the maximum values defined for the ADC will be clipped to the respective upper/lower value.

Options

  • Once the A/D converter scales the electrons into counts, the pixel values are now integer digital counts rather then floating-point, absolute radiometric quantities. Although those integer values can continue to be written to the standard output ENVI image file as double-precision floating-point values (the default), it is usually desirable to write to an integer format. The <datatype> element in the <imagefile> can be included to change the output data type to an integer type (see the the data type tag in the ENVI image header file description).

Note The example above has the A/D producing a 12-bit output value and the output image data type was selected to be 16-bit unsigned. In this case, the 12-bit values will be written on 16-bit boundaries. Similarly selecting 32-bit unsigned would write the 12-bit values on 32-bit boundaries. Selecting an 8-bit output would cause the upper 4-bits to be clipped from the 12-bit values.

Inline Processing

There is also an ability to have this plugin launch an external process to perform operations on the output image. The <processing> element can be added to each focal plane (inside the <capturemethod> element) and can include one or more "tasks" to be performed on the supplied schedule.

Note Currently these processing tasks are applied to the radiometric image product and not the truth image product.

The schedule options include:

collection_started

A processing step that is performed on the radiometric image file at the start of the collection.

task_started

A processing step that can be performed on the radiometric image file at the start of each task.

capture_started

A processing step that can be performed on the radiometric image file at the end of each capture.

capture_completed

A processing step that can be performed on the radiometric image file at the end of each capture.

task_completed

A processing step that can be performed on the radiometric image file at the end of each task.

collection_completed

A processing step that can be performed on the radiometric image file at the end of the collection.

Note The processing schedule is most likely related to the output file schedule for the focal plane. For example, if the focal plane produces a unique file per capture, then the processing schedule would most likely be capture_completed so that it can perform post-processing on the file just produced by the capture event.

The example below is meant to demonstrate how a (fictitious) program called demosaic can be run at the end of each capture. The options (provided in order by the <argument> elements) are specific to the program being executed:

           <focalplane ... >
             <capturemethod type="simple">
              <spectralresponse ... >
                ...
              </spectralresponse>
              <spatialresponse ... >
                ...
              </spatialresponse>
              <imagefile areaunits="cm2" fluxunits="watts">
                <basename>truesense</basename>
                <extension>img</extension>
                <schedule>capture</schedule>
              </imagefile>
              <processing>
                <task schedule="capture_completed">
                  <message>Running demosaic algorithm</message>
                  <program>demosaic</program>
                  <argument>--pattern=truesense</argument>
                  <argument>--input_filename=$$IMG_BASENAME$$.img</argument>
                  <argument>--output_filename=$$IMG_BASENAME$$.jpg</argument>
                  <removefile>true</removefile>
                </task>
              </processing>
             </capturemethod>
           </focalplane>

The special string $$IMG_BASENAME$$ represents the radiometric image file being generated. In this example, the <imagefile> indicates that a file with the basename of truesense and a file extension of .img will be produced for each capture, resulting in filenames such as truesense_t0000-c0000.img, truesense_t0000-c0001.img, etc. When this processing task is triggered after the first capture, the $$IMG_BASENAME$$ string will be replaced by the string truesense_t0000-c0001. Subsequent processing calls will be automatically called with the appropriate filename that is changing from capture to capture.

The <removefile> option allows you to request that the current file produced by DIRSIG be removed after the processing task is complete. In the case of the example above, after the DIRSIG image has been demosaiced, the original mosaiced image could be removed (to save disk space, etc.). The default for this option is false.

Tip These processing steps can also be used to perform tasks unrelated to image data processing. For example, to append the current time into a log file, to perform file housekeeping tasks, etc. The intention is for any program that can be executed from the command-line to be triggerable as part of the simulation event schedule.

For a more complex example, consider wanting to automatically generate a video file from a simulation. In this example, we will use the ffmpeg utility to encode a sequence of individual frames into a video. To achieve that, we will use the "file per capture" output file schedule and we will define two processing tasks:

  1. At the end of each capture, the output file will be converted to a PNG image. In this case we will use the DIRSIG supplied image_tool utility.

  2. At the end of the data collection (simulation), all the individual frames will be encoded by the ffmpeg utility (note that the details of the ffmpeg options are beyond the scope of this discussion).

Example processing tasks to produce a video file.
              <processing>
                <task schedule="capture_completed">
                  <message>Converting radiance image to PNG</message>
                  <program>image_tool</program>
                  <argument>convert</argument>
                  <argument>--autoscale=gamma</argument>
                  <argument>--format=png</argument>
                  <argument>--output_filename=$$IMG_BASENAME$$.jpg</argument>
                  <argument>$$IMG_BASENAME$$.img</argument>
                </task>
                <task schedule="collection_completed">
                  <message>Encoding frames into video</message>
                  <program>ffmpeg</program>
                  <argument>-y</argument>
                  <argument>-framerate</argument>
                  <argument>15</argument>
                  <argument>-i</argument>
                  <argument>demo-t0000-c%04d.png</argument>
                  <argument>video.mp4</argument>
                </task>
              </processing>

While the simulation is running, the <message> will be displayed when each processing task is executed:

Output of DIRSIG featuring the task messages.
Running the simulation
Solar state update notification from 'SpiceEphemeris'
    66.237 [degrees, declination], 175.141 [degrees, East of North]
Lunar state update notification from 'SpiceEphemeris'
    70.153 [degrees, declination], 137.883 [degrees, East of North]
    0.089 [phase fraction]
Starting capture: Task #1, Capture #1, Event #1 of 21
    Date/Time = 2008-12-30T12:00:00.0000-05:00, relative time = 0.000000e+00
    Instrument -> Focal Plane
        Bandpass = 0.400000 - 0.800000 @ 0.010000 microns (41 samples)
        Array size = 320 x 240
Scaling frame to PNG ... done.

...

Starting capture: Task #1, Capture #21, Event #21 of 21
    Date/Time = 2008-12-30T12:00:05.0000-05:00, relative time = 5.000000e+00
    Instrument -> Focal Plane
        Bandpass = 0.400000 - 0.800000 @ 0.010000 microns (41 samples)
        Array size = 320 x 240
Scaling frame to PNG ... done.

Encoding frames into video ... done.

An example of this video encoding scenario is included in the TrackingMount1 demo (see the encode.platform and encode.sim files).

Usage

The BasicPlatform is implicitly used when the user launches DIRSIG5 with a DIRSIG4 era XML simulation file (.sim). To explicitly use the BasicPlatform plugin in DIRSIG5, the user must use the newer JSON formatted simulation input file (referred to a JSIM file with a .jsim file extension). At this time, these files are hand-crafted (no graphical editor is available). An example is shown below:

[{
    "scene_list" : [
        { "inputs" : "./demo.scene" }
    ],
    "plugin_list" : [
        {
            "name" : "BasicAtmosphere",
            "inputs" : {
                "atmosphere_filename" : "mls_dis4_rural_23k.atm"
            }
        },
        {
            "name" : "BasicPlatform",
            "inputs" : {
                "platform_filename" : "./demo.platform",
                "motion_filename" : "./demo.ppd",
                "tasks_filename" : "./demo.tasks",
                "output_folder" : "output",
                "output_prefix" : "test1_",
                "split_channels" : false
            }
        }
    ]
}]

Options

Output Folder and Prefix

DIRSIG4 and DIRSIG5 support a common pair of options to specify the output folder and/or prefix of all files generated by the sensor plugins. The optional output_folder and output_prefix variables serve the same purpose as (and take precedence over) the respective command-line --output_folder and --output_prefix options. The value with providing these options in the JSIM file is that each BasicPlatform plugin instance can get a unique value for these options, where as the command-line options will provided each plugin instance with the same value.

For example, consider a simulation with multiple instances of the exact same sensor flying in different orbits (e.g., a constellation of satellites). The constellation can be modeled in a single simulation using multiple instances of the BasicPlatform plugin, each being assigned the same .platform file but a different .motion file with the unique orbit description. Since each instance is using the same .platform, each instance will write to the same output files. Using the output_prefix (or the output_folder) option in the JSIM allows each instance to generate a unique set of output files while sharing the same platform file.

Supplying a unique "output prefix" to each instance of the plugin.
[{
    "scene_list" : [
        ...
    ],
    "plugin_list" : [
        ...
        {
            "name" : "BasicPlatform",
            "inputs" : {
                "platform_filename" : "./my_sat.platform",
                "motion_filename" : "./sat1.motion",
                "tasks_filename" : "./sat1.tasks",
                "output_prefix" : "sat1_"
            }
        },
        {
            "name" : "BasicPlatform",
            "inputs" : {
                "platform_filename" : "./my_sat.platform",
                "motion_filename" : "./sat2.motion",
                "tasks_filename" : "./sat2.tasks",
                "output_prefix" : "sat2_"
            }
        }
    ]
}]

Split Channels into States

By default, this plugin submits the bandpass for each focal plane as a separate spectral state to the DIRSIG5 core. That means that a single wavelength will be used to geometrically follow light paths through the scene. This approach is fine unless spectral dispersion (e.g., refraction separating different wavelengths) is expected or desired. For example, if a single focal plane is assigned red, green and blue channels and the user expects atmospheric refraction to create spectral separation, then the single spectral state approach will not work because a single, common wavelength will be used to compute the refraction for red, green and blue light. The first alternative is to create multiple focal planes, which will result in each submitting a unique spectral state and as a result different wavelengths will be used to compute the refraction for each channel.

Note On average, atmospheric refraction can be significant, but the relative differences between channels in a single camera is usually minimal except at severe slant angles (low elevation) an long path lengths. The Refraction1 demo contains an example this effect in practice.

The second alternative is the "split channels into states" option, which forces the plugin to submit a unique spectral state for each channel in each focal plane. The optional split_channels variable in the JSIM input serves the same purpose as (and takes precedence over) the respective command-line --split_channels option. The default is false.

Scale Resolution

In some situations, the user may want to modify a focal plane to get a higher resolution (more pixels) or lower resolution (fewer pixels) image of the same field-of-view (FOV). This can be manually achieved by modifying the geometric camera model via the pixel sizes, number of pixels, etc. An easier alternative is the "scale resolution" option, which allows the user to quickly change the resolution. The command-line --scale_resolution option is assigned a resolution scaling factor (e.g., --scale_resolution=2.0). The default value is 1 but providing a factor > 1 will increase the resolution (more pixels) and a factor < 1 will decrease the resolution (fewer pixels). This factor will be applied to all the focal planes in all the instruments modeled by this plugin.


1. Robert Fiete, "Modeling the imaging chain of digital cameras"', Tutorial Texts in Optical Engineering, TT92, SPIE Press, 2010