Working with Mars Orbiter Camera Data
From Isis Workshop
[edit] About MGS MOC
The Mars Global Surveyor Mission
Mars Global Surveyor (MGS) was launched in 1996 with a successful Mars Orbit Insertion in September, 1997. As of January 2008, the spacecraft remains active and continues to orbit Mars. (See also: mars.jpl.nasa.gov)
The primary science goals of MGS are to determine whether life ever existed on Mars, to characterize the climate of Mars, to characterize the geology of Mars, and to prepare for human exploration. The instruments onboard the spacecraft are designed to help achieve these goals.
The instruments on the MGS spacecraft are:
- Mars Orbiter Camera (MOC)
- Mars Orbiter Laser Altimeter (MOLA)
- Thermal Emission Spectrometer (TES)
- Electron Reflectometer (MAGNETOMETER)
- Gravity Field Experiment (RADIO SCIENCE)
[edit] The Mars Orbiter Camera Instrument
The Mars Orbiter Camera (MOC) onboard MGS produces a daily wide-angle image of Mars similar to weather photographs of the Earth, and also takes narrow angle images. Malin Space Sciences is responsible for MOC operations.
The MOC consists of three cameras: a narrow angle camera greyscale, and red and blue wide angle cameras. Isis supports processing of the data acquired by all three cameras.
The MOC narrow angle camera (NA) data obtains high resolution greyscale images, ranging from 1.5 to 12 meters/pixel resolution. The red and blue wide angle cameras (WA) collect context images at approximately 240 meters/pixel resolution, and low-resolution global images at approximately 7.5 kilometers/pixel resolution.
The cameras are pushbroom scanners (also called along-track scanners), acquiring one line of data at a time as the spacecraft orbits the planet. For details about the MGS mission and the MOC cameras refer to the PDS MOC archive documents that are distributed with the image data. Information can be found on the Mars Global Surveyor Science Sampler Data Set Collection.
 Wide Angle, Red Band This image was collected using summing mode 27, and has a resolution of approximately 6.7 kilometers/pixel. This image is mirrored and will need to be flipped during processing. |
 Higher Resolution Wide Angle, Red Band This image was collected using summing mode 1, and has a resolution of approximately 250 meters/pixel. In summing mode 1, detector readings are not combined. This image is mirrored and will need to be flipped during processing. |
 Wide Angle, Blue Band This image was collected using summing mode 27, and has a resolution of approximately 6.7 kilometers/pixel. Summing modes are utilized on-board the spacecraft to average detector readings to combine them into a single output pixel value. |
 Narrow Angle This image has a resolution of approximately 1.5 meters/pixel. |
[edit] Related Resources
[edit] Processing MOC Data Overview
- Level 0 - Data Ingestion
- The MOC images are downloaded to a local workstation. The labels are updated with Isis keywords, and the filenames containing information about the geometry of the image is added to the labels.
- Level 1 – Radiometric Calibration and Noise Removal
- The noise and pixel spikes that are introduced during image acquisition are removed to create an ideal grayscale image representing reflectance values (ranging from 0 to 1 DN value).
- Level 2 - Projection
- Geodetic corrections are performed and the images projected to a map projection.
- Level 3 – Photometric Correction and Enhancement
- The effect of sun angle on the image is corrected, and the images are tone matched.
- Level 4 – Building a Mosaic
- A seamless mosaic is created.
[edit] Level 0 Processing - Data Ingestion
This is the starting point for the production of a MOC mosaic. The steps within the level zero processing provide the gateway into Isis processing. Running the following applications will ingest the MOC Standard Data Products (and MOC Standard Decompressed Data Products) and place necessary information into the labels of the image.
[edit] Acquiring MOC Data
There are numerous online resources and desktop tools for searching for and acquiring Mars Global Surveyor Mars Orbiter Camera image data.
[edit] Planetary Data System (PDS)
PDSPlanetary Image Atlas
The Planetary Data System (PDS) Planetary Image Atlas, hosted by the Jet Propulsion Laboratory and USGS Astrogeology Research Program, allows users to search through the MOC database and select images based on requirements entered by the user. There are both a basic and an advanced search feature.
USGS/PDS Mars Orbiter Camera Image Collection
Our PDS MGS MOC Image Collection offers a additional search tools, such as Web Graphical Access, which allows you to locate data by navigating an image map of Mars.
ASU JMARS Geographic Information System
The JMARS Geographic Information System (GIS), offered by the Arizona State University's Mars Spaceflight Facility is a useful desktop tool for searching for and viewing MOC images..
[edit] Resources
[edit] Data Acquisition Tool: Planetary Image Atlas
The Planetary Data System (PDS) Imaging Node houses data from several planetary missions, and offers a variety of methods for accessing their holdings. For now we will concentrate on acquiring Mars Global Surveyor Mars Orbiter Camera (MGS MOC) data.
[edit] Search
The PDS Planetary Image Atlas provides a Product Search search tool to interrogate the collection of MGS MOC imagery. This tool lets us query information about each image and ignores the data that we have no interest in. A good way to reduce the number of images to look at is by defining an area of interest with latitude and longitude ranges. You can also restrict the search by choosing a minimum and maximum resolution. Remember to keep in mind the coordinate system (areographic west for MGS MOC) and units (generally meters for distances) required by the search tool. Launch the MGS MOC Product Search to give it a try.
The table below lists the primary search parameters that can help you narrow down the number of images that are returned by a search of the PDS MGS MOC image collection. The images to the right show screen shots of the MGS MOC Product Search. Note there are two categories (the tabs above the search form) where these search parameters are found: Quick Search and Instrument.
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[edit] Browsing by Volume
You can also go to the online data and Browse Online Data Volumes, which offers FTP access to the CDs (compact discs)used to archive and distribute the data. This allows you to look at the image and text files on the CDs, where you can find more helpful information. To give it a try, launch the Planetary Image Atlas in a new browser window. Now click the Mars Global Surveyor Browse Online Data Volumes and choose a CD volume to look at. When you know the images that you would like to work with, you can go to this area with an FTP tool and download that image or download it using your browser.
[edit] Related Resources
[edit] Data Acquisition Tool: USGS/PDS MOC Image Collection
The Planetary Data System(PDS) Imaging Node houses all the released MOC data. The data can be accessed via the USGS/PDS Mars Global Surveyor Mars Orbiter Camera(MOC) Image Collection web site. There is a direct FTP link to all the data that is currently released to the public, and also Web Graphical Access that allows the user to select a region of interest, and evaluate the images by browsing through a list of images.
[edit] Web Graphical Access
This interactive page allows the user to select a 15 degree by 15 degree area of interest. The page displays the different types of data products as color coded square boxes overlain on a map of Mars. The browse images can be displayed from the list that is generated by clicking on Listing, or by clicking on a colored box.
To get started, lets launch the USGS/PDS Mars Global Surveyor Mars Orbiter Camera (MOC) Image Collection web site in a new browser window.
- Click on Web Graphical Access, a global map of Mars divided into 15 degree by 15 degree rectangular boxes will be displayed. If you click in one of the boxes, a zoomed-in view showing MOC footprints of available data will appear.
- Click on the grid in the box for Latitude 0° to 15°, Longitude 0° to 15°.
- Now, you have two choices:
- If you click on one of the colored boxes on the zoomed-in view, the browse image and image information will be displayed. If you click on a narrow angle MOC image, grayscale and color Viking context images may also be displayed. The Standard Compressed image can be downloaded by clicking Download. (Read the Download Note if problems are encountered.) Click on one of the red boxes (like m0704913 , closest to the upper left corner).
- If you click on Listing (on the top of the page, above the zoomed-in image with the colored boxes), a list of the MOC image number, center latitude, center longitude, resolution, and camera will be listed in tabular form. If you click on one the MOC image numbers the browse image and information page will be displayed.
[edit] Browsing via FTP
In order to use the ftp access, you must already know which image you will be working with, and which CD volume the image is archived on. For example, I would like to download m0103072.imq, which is archived on volume MGSC_1015. The data are divided into folders labeled using the first six digits of the MOC image numbers.
To get m0103072.imq from the site, lets launch the USGS/PDS Mars Global Surveyor Mars Orbiter Camera (MOC) Image Collection web site in a new browser window.
- Click on FTP access
- Select MGSC_1015
- Select the m01030 folder
- Right click on the filename (m0103072.imq) and select Save link as... (your browser may also call it "Save target as" or "copy to folder"). A pop-up window will appear to allow you to select or create a folder where the file is to be saved on your local workstation.
[edit] Resources
[edit] Data Acquisition Tool: JMARS
JMARS is a useful Geographic Information System (GIS) tool to evaluate MOC images that cover an area of interest before the images are downloaded. The tool displays MOC footprints and a variety of other Mars data. JMARS can be used to:
- Query the database of MOC images
- Select browse images to display on-screen and download via the web
- Generate a list of MOC images (great for creating scripts)
- Save the displayed map as an image
[edit] Give it a Whirl!
If you have JMARS installed, launch it and log in. We'll run through a quick and easy exercise in displaying MOC Narrow Angle footprints.
- In the Main tab of the Layer Manger, hit the Add new layer button, which opens a menu.
- Select Moc from the Stamp menu.
- In the Add Moc stamp layer window, just hit the Okay button, leaving all the fields blank.
- A new tab named MOC-NA stamps will appear in the Layer Manager. When it's done loading the footprints, its drawing status indicator will turn from red to green,the footprints will be displayed on the map and the images shown on the map are list in the Layer Manager.
- Try right-clicking on a listing the image list and on the footprints displayed on the screen -- there's lots of options for working with and accessing information and data. For example, Render and Load Selected Stamps will download and display the MOC images for the selected footprints on the map, giving you the ability to preview the data. Web Browse will launch an image's web page in your browser so you can access the information and data.
Of course, there's many other options in JMARS to help you search for MOC data, such as the tools for narrowing your search and modifying your display in the Settings, Query, and Render tabs in the Layer Manager, using the various tools in the main menus, and adding other useful data layers to the display.
[edit] Resources
- Download JMARS (free, registration required to use JMARS' full functionality)
- JMARS User Manual
[edit] Importing MOC Data
The Standard Decompressed Data Products, which have an .img extension and are in PDS image format, can be ingested directly into ISIS. The Standard Data Products, which have an .imq extension, are compressed PDS format images and are decompressed before ingesting into ISIS.
[edit] Using moc2isis to ingest MOC images into Isis
The following examples show the use of moc2isis to ingest (or import) a MOC image into Isis. The resulting output file will be an Isis cube.
Example: Ingesting an Uncompressed Standard Decompressed Data Products MOC image
moc2isis from=r0700563.img to=r0700563.lev0.cub
Example: Ingesting a Compressed Standard Data Products MOC image
moc2isis from=r0700563.imq to=r0700563.lev0.cub
The decompression software for the .imq images is included within moc2isis, you don't need to worry about the extra step of decompressing the image.
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[edit] Decompressing MOC Images
If you'd like to decompress the MOC image outside of the ISIS moc2isis application, use the mocuncompress application. mocuncompress is not an Isis application, and you must enter this command exactly as shown below. There is no GUI available for this application. (This application is supplied by Planetary Data System).
mocuncompress Syntax:
mocuncompress [input file] [output file]
mocuncompress Example: .img is the recommended file extension for the uncompressed output file.
mocuncompress e2000929.imq e2000929.img
If you'd like to import the resulting uncompressed image into Isis, simply run moc2isis on the uncompressed file. For example:
moc2isis from=e2000929.img to=e2000929.lev0.cub
[edit] MOC Problem Data
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Many of the problems with the MGS MOC data sets are due to either transmission errors or environmental conditions that existed when the image was acquired. To find these poor quality images, you must visually inspect the images. While many predicted problems are easily handled through standard MOC processing procedures, missing data, corrupted data, and other random data acquisition and transmission issues may require special processing, manual editing, or simply cannot be corrected. Clouds and airborne dust are two elements, caused by environmental conditions, that will degrade the quality of your images. The amount of image degradation will vary. In either case, whether or not you use the image is a judgment based on how much information will be gained verses how much the image will degrade the final product. |
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[edit] Adding SPICE
An important capability of Isis is the ability to geometrically and photometrically characterize pixels in raw planetary instrument images. Information such as latitude, longitude, phase angle, incidence angle, emission angle, local solar time, sun azimuth, and a many other pixel characteristics can be computed.
To compute this information, the SPICE (Spacecraft and Planetary ephemeredes, Instrument C-matrix and Event kernel) kernels must first be determined for the particular raw instrument image. These kernels maintain the spacecraft position and orientation over time as well as the target position and instrument specific operating modes.
To add SPICE information to your cube, run spiceinit application on the image so that camera/instrument specific applications (e.g., cam2map, campt, qview) will have the information they need to work properly. Generally, you can simply run spiceinit with your input filename and no other parameters:
spiceinit FROM=my.cub
[edit] Related Isis Applications
See the following Isis documentation for information about the applications you will need to use to perform this procedure:
- spiceinit: adds SPICE information to the input cube
[edit] Level 1 Processing - Noise Removal and Radiometric Calibration
To create a Level 1 MOC image, we'll clean up noise and other problems and radiometrically correct the data so we have an image representing the reflectance of the surface. We'll start by removing image defects caused by malfunctioning detectors, dust specks, transmission noise, and so forth. We'll finish up our Level 1 image with radiometric calibration in order to create an image that is representative of an ideal image acquired by a camera system with perfect radiometric properties.
[edit] Overview of Radiometric Calibration
[edit] Why perform radiometric calibration?
Both vidicon cameras (such as those carried on-board the Viking and Voyager missions), and charge coupled device (CCD) cameras (such as on the Clementine, Mars Reconnaissance Orbiter, and other contemporary missions) produce digital images with the inherent artifact known as camera shading. Camera shading results from the non-uniform sensitivity across the field-of-view of the imaging instrument.
Perhaps the best way to illustrate camera shading is to imagine acquiring a digital image of a target of uniform brightness, say a screen that has been painted a uniform shade of gray. If the camera sensitivity across the fields-of-view were ideal (and the flat-field target exactly the same brightness everywhere), then the acquired digital image would have the same DN value for all the pixels in the image. However, because of the non-uniform sensitivity of the camera, the DN values of the resulting image will vary throughout the image array (see the example to the right). A typical camera may have as much as 20% variation across the field-of-view. Camera shading corrections are applied to an image that correct for the non-uniform sensitivity so that, in our flat-field observation example, the radiometrically corrected image would contain pixels of identical value.
[edit] What is radiometric calibration?
Radiometric calibration recalculates the DNs in an image based on numerous factors, such as the exposure time, known values for the camera shading based on flat-field observations, dark current (output current of a detector when no energy is incident on the detector, such as when the shutter is closed), and other factors describing the unique electronics design and characteristics of an imaging system. Camera sensitivity may be time dependent because of the drift of the camera sensitivity throughout the course of the mission. The camera sensitivity is also dependent on the filter, operating modes of the instrument, and temperature of the cameras. Additionally, the camera response may be non-linear at various brightness levels.
[edit] How is the image changed by radiometric calibration?
A radiometrically calibrated image has DNs in radiometric units that are proportional to the brightness of a scene. Radiometric calibration applications in Isis produce output values that represent either:
- Radiance - The amount of electromagnetic energy emitted or reflected from an area of a planet, in units of µw/(cm2*sr)
- Reflectance - The ratio of reflected energy to incoming energy (i.e. irradiance/solar flux, often simply called I/F). A reflectance would be 1.0 for an ideal 100% reflector where the sun and camera orientations are perpendicular to the reflecting surface.
Generally, Isis radiometric calibration applications that offer both reflectance and radiance output will output reflectance by default. Due to the fact that radiometric calibration is mission dependent, each mission supported by Isis has its own radiometric calibration application. A few examples of radiometric calibration applications in Isis are:
[edit] Radiometric Calibration of MOC Data
The radiometric calibration application moccal corrects an image so the output resembles an "ideal" grayscale image where the digital numbers (DNs) are proportional to scene brightness. The images are corrected for:
- Global gain and offset instrument operating modes
- Variable sensitivity of each detector in push broom array
- Even/odd detector offset
- Normalize sun-target distance
- Low-high-low spike at 50 pixel intervals caused by power supply synchronization pulse
Occasionally, corrupted pixels need to be set to null pixel values.
moccal is intended to run on MOC Narrow Angle and Wide Angle images. The default output image is a 32-bit cube where the pixels have been adjusted to represent reflectance (i.e. albedo), with valid pixel values between 0 and 1. (Alternatively, you can create a radiance image, with units in DN/msec, by setting the IOF parameter to "NO").
Default coefficients for the MOC calibration correction can be found in $ISIS3DATA/mgs/calibration/moccal.ker.# (#=version number, moccal will refer to the highest version number as most recent).
Example Command Line: This will perform radiometric corrections to our image (r0700563.lev0.cub) and create an output cube (r0700563_cal.cub) where the DNs represent reflectance (I/F, ratio of reflected radiation and the amount of incident radiation ).
moccal from=r0700563.lev0.cub to=r0700563_cal.cub
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[edit] Related Isis Applications
See the following Isis documentation for information about the applications you will need to use to perform this procedure:
- moccal: performs radiometric corrections to MOC images
[edit] Overview of Noise And Artifacts
Noise and artifact are terms used to describe speckles, spikes, reseaus, missing data, and other marks, blemishes, defects, and abnormalities in image data created during the acquisition, transmission, and processing of image data. The line between the definitions of artifact and noise is fuzzy (and often subject to opinion), and often the terms are used interchangeably. Some noise and artifacts are expected, even purposefully added, and can be removed during the radiometric calibration process.
[edit] What is noise?
In image processing, noise is a type of flaw or blemish in the image caused by:
- Telemetry data dropouts or transmission errors
- Malfunctioning or dead detectors
- Read noise native to the CCD system
- Coherent noise caused by spurious electronic signals from the operation of instruments onboard the spacecraft
Noise can take the appearance of speckling, missing data, random or orderly patterns, and other variations that cause the image to have a muddled appearance, or visually distracting blemishes or patterns. There are three categories of noise:
- Fixed-location noise always exist at the same location in the image array, with predictable positions. Fixed location noise can be cosmetically corrected by replacing the bad pixels with the weighted average of the unaffected neighborhood pixels. Fixed-location noise can result from malfunctioning or dead detectors.
- Randomly occurring noise results from data transmission errors causing data bits to be altered at random intervals in the image. The random noise produces discrete, isolated pixel variations or "spikes" and gives an image a "salt-and-pepper" appearance. Additionally, telemetry drop-outs can cause portions of an image to be completely missing. This type of noise is generally corrected using filtering techniques that recognize missing or anomalous data pixels and replaces these data points with a weighted average of the unaffected neighborhood pixels.
- Coherent noise can be introduced by spurious electronic signals produced by the operation of instruments onboard the spacecraft during image observations. The spurious signals interfere with the electronics of the imaging system causing coherent noise patterns to be electronically "added" to the images. For example, the shuttering electronics of the Viking cameras introduced a spurious "herring-bone" pattern at the top and bottom of the image. Noise-removal algorithms are designed to correct specific coherent noise problems such as this one.
[edit] What are artifacts?
Generally, image artifacts are a type of flaw or blemish in the image introduced during processing, intentionally introduced due to the design of system, or unintentional introduction of debris or energy external to the system. Examples of artifacts include:
- reseaus etched on the camera lens
- reseaus exposed on photographic film during pre-flight preparations for a mission
- minute dust specks located in the optical path or on the focal plane array
- cosmic rays and other charged particles impacting the sensor (particularly CCDs)
- fringe, ring, or visible patterns created during filtering, ratio analysis, and other enhancement processes
- quantization, checkerboarding, and other artifacts introduced by image compression algorithms during conversion from Isis cube format to a lossy image format or bit-type reductions that reduce the tonal resolution of the data
Most artifacts fit neatly into the categories of noise listed earlier and are corrected using many of the same processes. For example, dust specks create fixed-location blemishes, and cosmic rays cause random spikes. Reseaus are useful blemishes that are removed once they are analyzed for their locations within an image and the information saved for later processing.
 Reseaus, dropped data, and salt-and-pepper This example shows a number of noise and artifact types. The large, regularly spaced black dots across the image are caused by the reseaus on the camera lens. The pattern of vertical black lines across the bottom of the image was caused by transmission data loss. The black and white speckles, called salt-and-pepper, are random noise. |
 Instrument error The black streak running down the right side of this image may have been caused by an unusual error in the line-scanner camera that acquired this image. |
 Transmission error A glitch during the transmission of this image caused the data to become garbled (upper right) and some data was completely lost (the black area across the middle) |
 Image compression Exporting the image to a lossy, compressed image format (using very high compression), the compression algorithm introduced a tiled pattern across the image. |
[edit] Noise Removal: MOC Wide Angle Versus Narrow Angle
So far, we've been handling MOC Wide Angle and MOC Narrow Angle images the same. Now, we need to diverge and do some special processing to some of our images, and images from both cameras will be handled differently.
The different MOC camera characteristics resulted in images that have varying noise patterns. There are different noise issues among The MOC Wide Angle and MOC Narrow Angle images, and processing depends on the cameras' acquisition mode, and whether an image is the blue or red band for MOC Wide Angle. These common noise issues are straightforward to correct, and some images not have noise problems, and processing can continue with projecting the image to a map projection.
[edit] 50-pixel Noise Spike (MOC Narrow Angle)
The images acquired by MOC Narrow Angle (NA) when the camera was in crosstrack summing mode 1 contain a noise spike pattern every 50 pixels.
To find out if your image has this issue, extract the CrosstrackSumming and InstrumentId keywords from your MOC image to determine camera (Narrow Angle or Wide Angle) and camera crosstrack summing mode (modes range from 1 to 8 for MOC NA):
getkey from=r0700563_cal.cub grpn=Instrument keyword=CrosstrackSumming getkey from=r0700563_cal.cub grpn=Instrument keyword=InstrumentId
If the values returned are CrosstrackSumming=1, and InstrumentId=MOC_NA, run the mocnoise50 application to remove the noise spikes. Note: mocnoise50 will check the InstrumentId and CrosstrackSumming keywords for you and only run on images that have the correct values.
mocnoise50 from=r0700563_cal.cub to=r0700563_noise.cub
The images below have been cropped and magnified to show the results of mocnoise50 program.
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[edit] Related Isis Applications
See the following Isis documentation for information about the applications you will need to use to perform this procedure:
- mocnoise50: removes diagonal spike noise from MOC Narrow Angle images with Crosstrack Summing mode of 1
- gekey: outputs the value of a requested keyword from a cube label
[edit] About Crosstrack Summing Modes
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. Both MOC NA and WA detectors can utilize crosstrack (sample) and downtrack (line) summing modes. The value of these modes indicate the number of samples and lines, respectively, that were summed and averaged to result in the pixel values stored in the raw file.
[edit] Even-Odd Noise Removal (MOC Narrow and Wide Angle)
The Narrow Angle (NA) and Wide Angle (WA) images contain a noise pattern across even and odd pixels whenever the cameras were in crosstrack summing mode 1. The application mocevenodd gathers an average of the pixels to remove the noise pattern. The changes are very subtle, so it is very hard to see the differences visually. The pixel values are different if viewed with an interactive display program like qview.
To find out if your image has this issue, extract the CrosstrackSumming keyword from your MOC image to determine the camera crosstrack summing mode (modes range from 1 to 8 for MOC NA and 1 to 127 for MOC WA):
getkey from=e2000929_cal.cub grpn=Instrument keyword=CrosstrackSumming
If the crosstrack summing mode is 1 (i.e. CrosstrackSumming=1), run the mocevenodd application. Note: mocevenodd will check the CrosstrackSumming keyword for you and only run on images that have the correct value.
Narrow Angle: If you're working with a MOC NA image, run mocevenodd on the output of mocnoise50:
mocevenodd from=r0700563_noise.cub to=r0700563.lev1.cub
Wide Angle:If you're working with a MOC WA image, run mocevenodd on the output of moccal:
mocevenodd from=e2000929_cal.cub to=e2000929.lev1.cub
The images below have been cropped and magnified to show the results of mocevenodd program.
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[edit] Related Isis Applications
See the following Isis documentation for information about the applications you will need to use to perform this procedure:
- mocevenodd: removes even-odd sample noise from MOC images with Crosstrack Summing mode of 1
- gekey: outputs the value of a requested keyword from a cube label
[edit] Gap Noise Removal (MOC Wide Angle)
Currently, the Isis 3 application to remove the image gap at detector 371 in MOC Wide Angle images. mocgap is under development. When the application is released, we will develop a page here in this lesson providing you with examples of gap noise and tips for running mocgap to remove this type of noise from your MOC Wide Angle images.
The Wide Angle camera that acquired images in the blue filter contains a gap at detector 371. This gap can be filled in with the average of surrounding pixels (five pixels on a line centered on detector 371). Referring to the extracted label information, if InstrumentId = MOC_WA, then determine if it is a blue filter:
getkey from=file_mocevenodd.cub grpn=Archive keyword=FilterName
If FilterName = BLUE then apply mocgap to the appropriate output from the steps above:
If MOC_WA / CrosstrackSumming = 1
mocgap from=file_mocevenodd.cub to=file.lev1.cub
Or… if MOC_WA / CrosstrackSumming not equal 1:
mocgap from=file_cal.cub to=file.lev1.cub
[edit] Level 2 Processing - Geometry
Producing a mosaic requires geometric processing on the individual images that make up the desired mosaic. The individual images are geometrically transformed from spacecraft camera orientation to a common map coordinate system. Isis has generic software applications that are applied to all supported mission data. Based on the information in the image labels that was added in our earlier steps, the software recognizes the instrument and performs accordingly.
Within Isis, the geometric software includes correcting camera distortions for each supported instrument. The camera distortion correction and geometric transformation are performed simultaneously so that an image is resampled only once and resolution loss is minimal. The transformation is based on the original viewing geometry of the observation, relative position of the target and the definition of the map projection.
[edit] Overview of Map Projecting Images
Converting a raw instrument image to a map projected image is a fundamental capability of Isis. The target definition, ground range, pixel resolution, map projection, and projection dependent parameters are all needed in order to produce a map projected image.
[edit] Projecting the Image
The cam2map application converts a camera (instrument) image to a map projected image. cam2map will automatically compute defaults for most of the mapping parameters, so you only need to define a subset of the parameters in your map template (e.g. ProjectionName).
- Your cube must be data from a Isis-supported mission -- cam2map depends on camera/instrument information in the Isis system to perform map projections.
- If you are projecting several images with the same projection parameters, you can reuse the same map template for all your images simply by removing the latitude longitude range parameters (MinimumLatitude, MaximumLatitude, MinimumLongitude, and MaximumLongitude) from your map template.
- cam2map will automatically calculate parameter values for you -- all you really need is the projection name in your map template.
- If you're planning on mosaicking your projected images, make sure the PixelResolution is the same for all images. Some projections also require the CenterLongitude and CenterLatitude to be the same when creating a mosaic.
[edit] Related Isis Applications
See the following Isis documentation for information about the applications you will need to use to perform this procedure:
- maptemplate: set up a map projection parameter template for map projecting cubes
- cam2map: project a cube to a map projection
[edit] Level 3 Processing - Photometric Correction
Currently, Isis 3 photometric correction capabilities are under development. When the applications are released, we will develop a page here in this lesson providing you with examples and tips for using Isis photometric correction tools.
Photometric normalization is applied to all images that make up a mosaic in order to adjust and balance the brightness levels among the images that were acquired under the different lighting conditions.
Generally, radiometrically calibrated spacecraft images measure the brightness of a scene under specific angles of illumination, emission, and phase. For a planetary body that doesn't have a significant atmosphere, this brightness is controlled by two basic classes of information: the intrinsic properties of the surface materials, (including composition, grain size, roughness, and porosity), and local topography of the surface.
[edit] Level 4 Processing - Mosaicking
Currently, Isis 3 photometric correction capabilities are under development. When the applications are released, we will finish developing this lesson and provide you with tips for using Isis to create your final, seamless mosaic using mapmos and tone matching procedures and applications.
The final steps in our processing will produce a seamless mosaic of all the images in our region of interest. In spite of best efforts at radiometric calibration and photometric normalization, small residual discrepancies in image brightnesses are likely to remain. These brightness differences appear as seams in a mosaic. There are a couple of methods that will minimize the seams resulting in an improved aesthetic result for a final mosaic. The accuracy and quality of the radiometric calibration and photometric normalization effects how well the seams can be minimized.
[edit] Exporting Isis Data
Naturally, once you're finished processing your image data and you have your final product, you'll want to use it in reports, papers, posters, web pages, your favorite Geographic Information System (GIS) or image analysis package, or simply share it with others. Since many other software packages can't read Isis cube format, you'll want to export your cube to a file type appropriate to your needs.
[edit] General Purpose: Desktop Applications and Web
isis2std (Isis to Standard formats) exports your cubes to a wide variety of popular image formats, such as PNG (Portable Network Graphics) and TIFF (Tagged Image File Format). No matter what desktop application you're using (Word Perfect, Illustrator, or GIMP, just to name a few), or what you're creating (posters, web pages, or a pretty background for your desktop), isis2std will handle it.
[edit] But There's More! GIS and Image Analysis Applications
But the icing on the cake is that isis2std also exports the necessary files for taking your image into GIS! If your cube has Mapping labels, isis2std will write a world file that provides GIS software, and other software which can take advantage of a geographically registered image, with the necessary information to properly display your image. Most applications designed to work with remotely sensed and map data (such as Arc products, Envi, and Global Mapper) can use TIFF, JPEG, and other images with world files. Several geographic software applications even have support for planetary data.
[edit] Astronomy Anyone?
isis2fits (Isis to FITS format) exports your cubes to Flexible Image Transport System (FITS) format, a standard in the astronomical community. There's lots of software designed to work with FITS (even a few common applications and software libraries , like ImageMagick and GIMP). In general, FITS format is useful for mission team members who are working with cruise data to analyze instrument calibration, camera pointing, and other factors. Since cruise image data frequently contain stars, astronomy software comes in quite useful for this type of work.
[edit] Just the Data, Ma'am
isis2raw (Isis to Raw format) exports an individual band in your cube to raw image format, a minimal format that contains just the data in the bit-type of your choosing. isis2raw is a good choice when none of the other formats Isis can export to will work for you. In order to import a raw image into another application, you will need to know the width (samples), height (rows), bit-type, and endianess of your raw file in order to import it correctly, so remember to keep track of the parameters you used to export your cube. Related Isis Applications
See the following Isis documentation for information about the applications you will need to use to perform this procedure:
- isis2std: Export a cube to popular image formats, such as TIFF and PNG, with GIS world files
- isis2fits: Export a cube to FITS format, the standard image format of the astronomical community
- isis2raw: Export a cube to raw format
[edit] Staying Organized
The recommended filename convention at various phases of processing for level0, and level1 file are as follows:
The final output from moc2isis is considered a level0 image and should have .lev0.cub as the file extension.
Example: moc2isis from= r700563.imq to=r700563.lev0.cub
For the level1 file it depends on whether only moccal will be run or not. If only moccal needs to be run, the output should have the .lev1.cub extension.
Example: moccal from=r700563.lev0.cub to=r700563.lev1.cub
If mocevenodd needs to be run, the final output from mocevenodd should have the .lev1.cub extension.
Example: mocevenodd from=r700563.noise.cub to=r700563.lev1.cub
If mocgap needs to be run for MOC-WA images, then the final output from mocgap should have the .lev1.cub extension.
Example: mocgap from=r700563_cal.cub to=r700563.lev1.cub
