MAPIR Camera Control (MCC)

Interface Tabs:

This document explains how to use our MAPIR Camera Control software

Process Tab

Process images captured by MAPIR cameras.

Analyze

To get started, press the INPUT button, and select a folder of MAPIR camera images.

We automatically select the same folder for the OUTPUT folder. We'll create a new folder named "Processed_1" in

 the INPUT folder to store your processed images. If you prefer, you can select a different output location. 

If there is already a folder named "Processed_1" we'll create "Processed_2", etc.

Now press the ANALYZE button. The program will display some feedback messages in the log window while it

 analyzes your input images.

You may be warned that the images containing our targets are over or under exposed.

Over-exposed means they are too bright. Lower the exposure. Increase N in shutter speed 1/N.

Under-exposed means they are too dark. Increase exposure. Decrease N in shutter speed 1/N.

You can proceed with calibration regardless of WARNINGS, but the results may not be as good.

Processing Settings

Clicking on the gear icon in the bottom right corner of the Process tab opens the Processing settings window

 with the following options:

Recalibration interval (seconds）

During the reflectance calibration process, the calibration formulas will be re-calculated when a new calibration

target image equal to or greater than the interval time is found. Interval time is the number of seconds since the 

last calibration target image timestamp. Setting to 0 will update the calibration formulas for every target image

found. This allows you to account for changing ambient light more easily. If non-target images are found prior in

time to the first target image, then all images prior will be corrected with the first found target image.

Minimum calibration sample area (pixels)

When assessing the calibration target images we reject any that are too far away from the camera. We limit this 

distance by setting a minimum sample area for the pixels of each reflectance target. The smaller the minimum 

sample area the further the target can be, but the less pixels used for the average. The more pixels of each target

used the better the average will be, and thus the better the results should be. This setting is useful if you are 

capturing multiple calibration images from various distances (such as when a drone takes off). We are sampling a 

square region, so the side length of the square controls the total area. Area = length x width. So an area value of

25 is a 5 x 5 pixel square minimum.

Processing Options

Vignette (Flat Field) Correction

By default we will select "Vignette (Flat Field) Correction" if it is supported by the input camera model(s). This is

 ideal to do whenever possible. It will brighten up the outer perimeter of pixels in the images, which are darker due+

to the lens optics.

White Balancing

For RGB filter camera models we support white balancing the images using a "gray world" method. At this time 

we do not support calibrating the RGB filter models for reflectance using the target.

Reflectance Calibration

If you provided a photo of our reflectance targets then the "Reflectance Calibration / White Balance" option will 

be enabled. See here for more about our calibration targets.

If you have a sub-folder in your INPUT folder titled "target", we will search there only for target photos. 

This helps reduce the ANALYSIS time, since we search each photo for the target fiducial pattern

"Image Format" drop-down allows you to select the type of output images you require.

Image Format Options

TIFF (16-bit)

16-bit (pixel digital number 0 - 65535) TIFF (.tif) format output.

TIFF (32-bit, Percent %)

32-bit (pixel float 0.0 - 0.1) TIFF (.tif) format output.

JPG (8-bit)

8-bit (pixel digital number 0 -255) JPG (.jpg) format output.

JPG (8-bit, Percent %)

32-bit (pixel float 0.0 - 0.1) JPG (.jpg) format output.

Depending on your inputs, certain outputs may not be enabled.

If you have a RGB-sensor camera with a single channel/band filter installed (i.e. Survey3 RE) or have a

mono-sensor camera (i.e. Kernel2 M3M), the resulting output images will be single channel/band,

mono, grayscale.

Percent Image Output

Our cameras can be calibrated to provide pixel level reflectance measurements. Reflectance is commonly

provided as a percent value, so in addition to the typical integer pixel value output our software also provides

percent pixel value output.

This means that each pixel will range from a value of 0.0 to 1.0, meaning 0% to 100%. A pixel value of 0.83 would

mean 83.0%.

Percent images are large in size, so confirm you have enough storage before processing.

To begin processing press the PROCESS button.

The log on the right side will provide feedback during processing. Processing times can be long depending on

your computer's capabilities and the number of images. When processing is complete the log can be saved to a

text file using the SAVE LOG button.

After processing you can analyze the photos on the  Analyze tab.

Control Tab

Coming soon...

Analyze Tab

Analyze MAPIR camera images

Open Image to Analyze

To start analyzing an image press the BROWSE button and select an image.

The image will open in the image view box:

Our MAPIR Survey3W RGN example image shows our MAPIR Camera Reflectance Calibration Ground Target 

Package (V2) on top of some grass. The Survey3 camera was handheld during image capture, from about 1 m 

distance. There are regions in the grass ranging from dirt, to lower health grass, to higher health grass. A dead

leaf is also in the foreground of the image. This 16-bit TIFF image was previously converted from RAW and 

calibrated for reflectance using the Process tab in MCC.

Raster Index

To apply a raster equation, such as the NDVI index to the image, press the EDIT INDEX button to open the 'Raster

 Index Calculator" pop-up window:

Selecting an index from the "Index:" drop-down will display the index formula photo:

Notice the left-hand side shows the spectrum of light that is typically used in that index formula variable location.

For the NDVI index it typically uses near infrared (NIR) light and red (RED) light. The right-hand side shows the

variables X, Y, Z that correspond to the X, Y, Z drop-downs below the index formula photo.

MCC automatically labels the image channel/band names if a Survey3 image is open. In our example, this is the

RGN filter model Survey3, so the bands are labeled accordingly. For all our filter models, the name of the filter

corresponds with the band order, so filter R,G,N = bands 1,2,3.

For NDVI we need to choose the "X: Band 1: Red" and "Y: Band 3: NIR", as seen below:

Press the APPLY button to see the opened image update without closing the Raster window. Press the OK button

to keep the changes and close the Raster window.

On the right side you can see the pixel map legend. The name at the top of the legend shows the index name that

was used (NDVI), and the numbers represent the pixel value range (minimum to maximum) of the image. In our

example the pixels range from -1 to 1, though the range shows -0.99 to -0.99. These are the minimum and

maximum pixel end-points. 

The grayscale gradient image next to the pixel scale shows the color associated with the corresponding pixel

value. This is commonly called a LUT, or "Look Up Table". The minimum value is set to black color and the

maximum is set to white pixel. A gradient map is then applied to the pixels in between, with a medium gray

representing a value 50% (half-way) between the minimum and the maximum.

Looking at the NDVI index image you can see the surrounding grass is mostly composed of white pixels, since the

 NDVI index correlates a higher NDVI value with healthy vegetation that is reflecting a lot of near infrared (NIR)

 light

LUT Color Gradient

Let's apply a new color LUT (Look Up Table) to the pixels so it is easier to see the contrast in the image. Contrast

is the difference between the black and white pixels.

Press the EDIT LUT button to open the "Color Map (LUT)" pop-up window:

Click the APPLY button to apply the default color LUT settings to the opened image::

 Understanding how to properly adjust the LUT minimum and maximum clipping points may take some time. It

 helps to know a little about the raster index being used and the index's typical pixel ranges for certain materials

.We'll go over it now briefly to help you understand the various options.

Please feel free to CONTACT US if you have questions about our products and their many uses. Some 

of the below text may be a bit technical, so please let us know if you have questions.

The NDVI index has a maximum pixel range of -1 to 1, but most often it is clipped to 0 to 1. We will explain clipping 

in a moment below. The reason the NDVI range is commonly set from 0 to 1 is that the NDVI index is typically

used to analyze vegetation "health", with higher NDVI values correlating to more photosynthesis activity going on 

(reflecting more NIR light). So a higher NDVI value typically represents a healthier plant. Vegetation typically

ranges from dead or low health around 0.2-0.3 to most healthy around 0.8-0.9.

Notice in the previous image that the surrounding grass white pixels are now mostly colored medium green (~0.6)

to dark green (~0.9), showing that the majority of the grass is "healthy". Since our LUT minimum and maximum

 clipping points are still set at the defaults, they are at the image's actual pixel minimum and maximum. We have

 set our contrast for the LUT to cover the entire pixel range (-1 to 1).

It is common to adjust the contrast of the LUT, meaning to adjust the values for the minimum and maximum 

clipping points. Making the contrast range less condenses the color ramp/gradient, meaning that the LUT+

contrast increases within the clipped pixel range.

Let's adjust the clipping range of the opened image. Open the "Color Map (LUT)" pop-up window (EDIT LUT

button). Change the minimum "Minimum:" value to 0, as shown below. Click Apply/Ok.

The opened image has now been updated.

Now you can better see the vegetation health in the image. The lower health grass has been colored light green

(~0.6) and the healthiest grass is colored dark green (~0.9). Exposed soil in the foreground and perimeter is red

(0.0) to orange (~0.3).

The dead leaf in the foreground, seen cropped and centered below, is below 0.4, as expected:

Unchecking the Index and LUT checkboxes reveals the original calibrated image reflectance pixels.

Notice (below) how the shaded leaf and shaded grass underneath the leaf have high NDVI values (green). This is 

typical for NDVI pixels of shaded vegetation.  We have marked 2 shaded vegetation regions below with blue arrows:

There are other index equations that may do better to account for shaded vegetation. For example here is the 

same cropped MAPIR Survey3W RGN image but with the OSAVI index applied. Notice the shaded leaf is now 

correctly showing lower NDVI values than the surrounding healthy grass. The grass region itself is also likely

 better represented now with the OSAVI index, as there is shaded grass within the grass region as well.

Classes Options

Opening up the "Color Map (LUT)" pop-up window (EDIT LUT) we can choose a "Classes:" option::

Classes define how many defined color ranges the pixels are scaled between. The more classes the higher the

 contrast, the less classes the lower the contrast between pixel regions. You've seen the highest "7 Colors" class

 option, here is the opened photo below in "3 Colors" for comparison:

Clip Options

Opening up the "Color Map (LUT)" pop-up window (EDIT LUT) we can choose a "Clip:" option:

When the minimum value is increased, or the maximum value is decreased, you are clipping/cutting off the pixels 

outside that inner clipped range. The pixels that are clipped/cut off can then have their value changed to 

something else. Those are the clip options explained below:

LUT Minimum/Maximum

Pixels outside the clipping range will be set to the LUT default minimum and maximum pixel color. For the Red-

Yellow-Green LUT, lower valued pixels are all set to red, and higher valued to green.

Transparent

Pixels outside the clipping range will be set to transparent (see-through). This is useful to overlay the output

image in other image applications, such as as a raster layer in a GIS application.

Background Index

Pixels outside the clipping range will be set to the pixels from the raster index image. Basically setting the LUT

back to the default grayscale for only those 

Background Original

Pixels outside the clipping range will be set to the pixels from the original image. This is a common option for us

to use ourselves, as it tends to easily highlight the contrast we're looking to show.

Here is the opened image with the "Clip" option set to "Background Original":

And the original opened (calibrated) image for comparison:

After reflectance calibration the healthy vegetation is blue, because on this RGN filter model camera the near 

infrared (NIR) light is captured in the sensor's blue channel.

You could also choose the GNDVI index and process that with this RGN filter model MAPIR camera:

You could also open up an OCN filter model image of a similar scene and process for NDVI:

Navigation Buttons

Above right of the image viewer you will find the navigation buttons (from left ot right):

Zoom In     -     Zoom Out    -    Fit Image

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