Processing thermal images

   

Introduction to thermal mapping

Infrared imaging is increasingly used for obtaining thermal maps, in particular of industrial installations, to quickly detect anomalies in plants, and to better target maintenance efforts and improve the efficiency of operation.

Thermal cameras are rather different from normal RGB cameras. First of all, thermal cameras tend to have much lower resolution than current RGB cameras. They also need special optics, not to block longwave infrared wavelengths. Then, even if thermal cameras normally carry a shutter, this is usually not used for taking pictures, but only for internal calibration of the sensor.

The time over which an image is acquired is rather determined by a “response time” of the camera sensor, which is generally longer than typical exposure times for RGB cameras. In addition, the response of thermal cameras tends to change in time (drift), and be non-uniform over the sensor.

Which images can be used for thermal projects?

Pix4Dmapper can process thermal images that have been captured following the recommendations described in this section.

Format Description
RJPG An RJPG (radiometric JPG) image is a .jpg image with radiometric data embedded in the image's metadata. This is a proprietary image format that is supported by Pix4Dmapper. RJPG is the recommended image format for thermal images.
.tiff .tiff grayscale images are supported by Pix4Dmapper but can lack important radiometric data.
.jpg Pix4Dmapper supports .jpg thermal images, but this image format is not recommended. The .jpg images are colored-mapped temperatures and contain only a visual representation of the temperature instead of the raw values.
 
Information: You can process thermal images extracted from a video but it is not recommended for the same reasons why .tiff images are preferable to .jpg images. Moreover, a movie is less likely to contain image geolocation and is likely to be more compressed than a still image. A thermal video may also capture excessive amounts of image overlap, which can introduce noise into your reconstruction.

Which thermal cameras are supported and which temperature they provide?

Sensor recommendations

In order to have enough visual content in the images for Pix4Dmapper to reconstruct the scene, we recommend:

  • A minimum sensor resolution of 640x480. Smaller sensors are not supported and typically do not calibrate. 
  • Using a lens with a smaller focal length (9mm) increases the image's footprint, though it is possible to use longer lens focal lengths.

Supported thermal cameras

Recommended integrated solutions that are supported out-of-the-box include the following cameras.

Camera model Absolute temperature Relative temperature
DJI Zenmuse XT    x
DJI Zenmuse XTR x  
FLIR Vue Pro   x
FLIR Vue Pro R x  
senseFly ThermoMAP x  
Aeryon Labs FLIR board   x
Workswell WIRIS 2nd Gen 640   x
Micasense Altum (Multispectral+Thermal)  x  
senseFly Duet T (RGB+Thermal)   x

Other custom camera integrations based on FLIR's Vue Pro or Tau2 sensors are also supported. Learn more about custom camera integrations: Processing thermal images.

 
Important: When creating a project with one of these supported cameras, verify that the Pixel Size and the Focal Length values are correct. Contact your camera manufacturer for details about your camera's specifications. Learn how to modify camera model parameters: How to use the Editing Camera Model Options.

Radiometric thermal cameras

Cameras labeled "R" are radiometrically calibrated. Using such cameras enables the capture of absolute temperature in every pixel of an image. FLIR Vue Pro R and Zenmuse XTR are both radiometric versions that do record absolute temperature. They save their images in RJPG (radiometric JPG) format: a .jpg image with temperature data embedded in every pixel.

 
Important: Even when using radiometrically calibrated cameras, please note that the differences in illumination, as well as the properties of the surface (material, roughness,...) in the scene, also affect the thermal emissivity in such a way that only similar surfaces can be reliably compared within one project. How far the absolute value of temperature is from the locally measured one depends on the details of the camera. Any such bias cannot be compensated by the software alone, but should rather be addressed at the hardware level. In practice, using as reference a local temperature measurement on the ground is the simplest and most effective solution.

How to capture thermal images?

For a better reconstruction of the captured scene in a thermal project, some recommendations should be followed during the image acquisition:

  • Have very high overlap: 90% front and side image overlap.
  • The images have been taken at a resolution of at least 640x480.
  • The images do not suffer from motion blur. An increased flight speed may cause a blurred image.

How to process thermal datasets?

Process a thermal project

1. Create a new project. For more information: New project in Pix4Dmapper.
For nadir datasets with accurate image geolocation, select the processing template Thermal Camera. For more information: Processing Options Default Templates

 
Important: You can apply a different processing template in the following cases:

2. Ensure that the Pixel Size and the Focal Length values are correctly set: on the menu bar, click Project > Image Properties Editor... and in the section Selected Camera Model, click Edit... For step-by-step instructions about how to modify the camera model: How to use the Editing Camera Model Options.
3. On the Processing bar, click Start to start the processing. The thermal index map will be generated during step 3. DSM, Orthomosaic, and Index.

Process dataset from Micasense Altum

The Micasense Altum is a camera with 6 bands: Blue, green, red, red edge, near-infrared (NIR) and thermal infrared (LWIR). Though the thermal sensor is 160x120, the images process successfully as a rig due to the high-resolution multispectral sensors.

1. Upload the images and use the ag multispectral template.
2. To convert the LWIR pixel values to degree C, use the formula: Thermal_ir=(lwir/100)-273.15

There is a demo dataset of this camera in the micasense website.

Process dataset with both thermal and RGB imagery (A better 3D mesh/ model)

Thermal cameras usually have much lower resolution than RGB cameras, and thus the 3D model is of much lower quality. The idea is to use the higher resolution RGB images to compute a detailed 3D model (mesh) and to project the thermal texture on top of it. This greatly improves the final thermal 3D model. To process a dataset with both thermal and RGB imagery:

1. Run step 1. Initial Processing for the thermal dataset following the instructions above.
2. Run step 1. Initial Processing for the RGB dataset in a separate Pix4D project.
3. Merge the RGB and the thermal projects. For more information about merging project: Merging projects.
4. On the menu bar, click Process > ProcessingOptions. Select 2. Point Cloud and Mesh and the tab Advanced. Ensure that for the Point Cloud and Mesh Geometry image groups, Thermal IR is unchecked and that group1 is checked. Ensure that for the Mesh Texture image group, Thermal IR is checked and group1 is unchecked. For more information: Menu Process > Processing Options... > 2. Point Cloud and Mesh > Advanced.

 
Information: This last step ensures that the point cloud will be generated from the RGB images, the mesh geometry will be created from this RGB point cloud and that the texturing will be done using the thermal imagery. However, the merging benefits only the mesh and not any other output.
  

How to visualize thermal outputs?

Visualize the 3D Point Cloud in the rayCloud

1. Click View > rayCloud, to open the rayCloud and load the 3D Point Cloud by ticking the Point Clouds box in the Layers section of the left sidebar. For more information: Menu View > rayCloud > Left sidebar > Layers > Point Clouds .
2. Display: (optional) In the Point Clouds layer of the left sidebar, select Display Properties and change the Shader to either Screen Aligned Quads, Thermal or Spherical Points, Thermal.

Visualize the 3D Textured Mesh in the rayCloud

1. If you use the Thermal Camera processing template and that the 3D Textured Mesh output is desired, on the menu bar, click Process > Generate 3D Textured Mesh. For more information: Menu Process > Generate 3D Textured Mesh.
2. Click View > rayCloud, to open the rayCloud and load the 3D Textured Mesh by ticking the Triangle Mesh box in the Layers section of the left sidebar. For more information: Menu View > rayCloud > Left sidebar > Layers > Triangle Meshes .
3. Display: (optional) In the Triangle Meshes layer of the left sidebar, select Display Properties and change the Shader to Thermal.

Visualize thermal Index Map in the Index Calculator

1. Click View > Index Calculator to open the Index Calculator.
2. In the section Index Map of the sidebar, select the band containing thermal data.

 
Note: Depending on the camera used to capture images, the band containing thermal data will differ.
 
Zenmuse XTR and FLIR Vue Pro R For projects created with Zenmuse XTR or FLIR Vue Pro R radiometric cameras and if RJPG (radiometric .jpg) imagery is used, the absolute temperature is obtained directly from the band. For more information on RJPG, see this section.
senseFly ThermoMAP Records absolute temperature. For projects created using the senseFly ThermoMAP camera, the temperature [°C or °F] index should be used to obtain absolute temperature index maps. This index is loaded automatically for Thermomap projects and computed using the following formula: 0.01*thermal_ir - 100
Zenmuse XT and FLIR Vue Pro Relative temperature is computed.
Workswell WIRIS 2nd Gen 640 The newer Wiris camera records relative temperature. It is recommended to do the processing using grayscale .tiff images and create the following index to view absolute temperature: 0.04*thermal_ir - 273.15
Micasense Altum LWIR .tiff images are used to generate the thermal reflectance map. Create the following index to view absolute temperature: Thermal_ir=(lwir/100)-273.15

3. Display: (optional) In the section Color Map and Prescription of the sidebar, increase the number of classes to 32 and from the drop-down list choose Equal SpacingMenu View > Index Calculator > Sidebar > 4. Color Maps and Prescription.

How to fix the discontinuities of thermal intensity between consecutive images?

If the temperature seems to drift with time, this is due to the characteristics of the camera (usually uncooled cameras exhibit this behavior) and this cannot be corrected by software. The camera provides an automated way to recalibrate the intensity, usually by taking a picture with the shutter closed. What happens is that the thermal image of a surface having uniform temperature is not itself uniform: it might rather show patterns, peculiar of a specific camera, and highly variable in time.

Check with the camera manufacturer for more details.

How to reduce very long processing time?

There are two main factors that affect the speed of step 1. Initial Processing:

  • Too much overlap: if some images in the project are taken from the same location, this will increase the processing time exponentially. It is advised to use a flight planning app (such as Pix4Dcapture) that triggers the camera based on distance instead of time. Alternatively, it is recommended to manually remove images if the drone was hovering at the same location for an extended period of time.
  • Camera model optimization: if the camera’s initial values are too different from the optimized ones, it may slow down processing. Ensure that the pixel size and focal length are entered correctly: How to use the Editing Camera Model Options.

What to do in presence of entirely white or black images in the rayCloud and low calibration rate?

In case the rayCloud presents either entirely black or entirely white images and the project shows a very low calibration rate, it means that the thermal camera used is not registered in our database. In these situations two actions are possible:

  • The preferred way is to send us a sample of the dataset such that we can include it in our database.
  • Another way is, before starting the process, to close the project and open the .p4d file with a text editor and below the <tangentialT2> and above the <cameraModelSource> lines, add the following line
    <pixelValue pixelType="uint16" min="-1" max="-1"/>

The "pixelType" must match the datatype of your input image. For example, if you use float or 8-bit data, the above line will not work.

How to use a custom integration of a thermal sensor?

When using a custom integration, it is necessary to integrate the metadata Pix4Dmapper requires in the image EXIF tags. Ensure to follow this document listing all the EXIF tags read by Pix4Dmapper: EXIF and XMP tag information read by Pix4D Desktop.

Guidelines for troubleshooting

When the processing of your thermal dataset is not successful or does not calibrate, please ensure to check the following points:

If after verifying the points above, the project still does not calibrate or is still very distorted, please try applying successively the following processing options:

  • Apply All Prior to the internal camera parameter optimization method. For more information on All Prior processing option: Menu Process > Processing Options... > 1. Initial Processing > Calibration.
  • Set the Camera model with distortion parameters to zero (Radials R1, R2, R3, and Tangentials T1 and T2). For more information on camera model options: How to use the Editing Camera Model Options.
  • If it still does not calibrate, try to run it with other calibration methods (Standard, Alternative). For more information: Menu Process > Processing Options... > 1. Initial Processing > Calibration. Thermal and Thermomap template uses alternative calibration pipeline. This pipeline assumes that the dataset does not contain oblique images, the terrain is flat and homogenous. Due to this assumption during the calibration, the images that have  >35 deg orientation will not get calibrated. Try processing with standard calibration.
  • A good thermal data set would be of very high overlap of around 95%. Still, it is important that the images are not captured from the same point of view, but the centers of the images are different points. For cameras like Tau2 (video camera), the fps might be too high and there might be many images in the same location, especially if the drone is hovering. In this case, you should manually remove some frames of the data set.
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18 comments

  • KES GIS

    having trouble with the prcoesing of Flir vue pro images,temperature difference is present in images.How to solve it?

  • Holden (Pix4D)

    Hey there. I would suggest opening a support request. Click contact support at the top of the page to open a ticket and include quality reports and a description of the issue, please.

  • RIJUN RAJ K

    Hi, 

    I'm planning to do Thermal Image mapping with my DJI Phantom 4 Pro using FLIR VUE PRO Camera. I've few doubts for using this.

     

    From YouTube videos, I understood that both DJI Camera and Flir Vue Pro can be mount at same time and Pix4D is supporting Vue Pro Camera.

     

    Doubt: Can I set Automatic Triggering for Flir Vue Pro in Pix4D Application??

    Is that Vue Pro Camera work like DJI Camera without Flir Application??

    My purpose is to take Thermal Images using Vue Pro and making 3D Models for Mapping. 

  • Gaël (Pix4D)

    Hi Rijun,

    Your questions have been answered in the following post
    Don't hesitate to post your questions on our community in a new post or in an already existing post with a similar topic to interact with others users.

    Best,

  • Haimanote Bayabil

    Hello,

    Is the Zenmuse XT2 19mm camera supported by Pix4D? thanks

    Edited by Haimanote Bayabil
  • Daniel (Pix4D)

    Hello,

    The camera is indeed supported.

    If you go to the Which thermal cameras are supported section of this article, there is a list including all of the supported cameras and the Zenmuse XT is listed.

    Regards.

     

  • Fredrik Nerdal

    Indeed the Zenmuse XT is listed, but what about the XT2? 

  • Momtanu (Pix4D)

    Hi Fredrik,

    Currently, the camera Zenmuse XT2 is not included in the camera database of Pix4D mapper. But you can process images from xt2 in Pix4D. It will read the camera parameters from the exif of the images.

  • Fredrik Nerdal

     

    Do you have a possible sulotion for this problem? We have tried with both jpeg and rjpg, but get the same error about no calibrated cameras. The overlap should be enough (90%)

  • André Rück

    Hello,

    we have exact the same problem by monitoring fields. There are too many uncalibrated cameras. Overlap was also 90%.

    XT2 640@30 HZ on a M210RTK

  • Momtanu (Pix4D)

    @Andre @Fredrik, this might be due to oblique images. I am pasting this the article here, guidelines for troubleshooting:

    • Thermal and Thermomap template uses alternative calibration pipeline. This pipeline assumes that the dataset does not contain oblique images, the terrain is flat and homogenous. Due to this assumption during the calibration, the images that have  >35 deg orientation will not get calibrated. Try processing with standard calibration.

    @Fredrik, Your overlap does not seem to be uniform. The orbital and the grid flight path does not have any overlap or common points, as thermal images are of very low resolution. Can you try processing the grid only by removing the images of the orbit? Also, change the calibration to standard.

  • André Rück

    By changing the calibration method there are more uncalibrated cameras then before....

    In the previously calculated model, the images were just not recognized in a "plane", but went into the "depth":

     

    Any ideas ?

    Best regards!

  • Momtanu (Pix4D)

    Hi Andre,

    Since alternative pipeline is optimized for thermal projects, that works the best. The software gives some weight to the geolocation data in this pipeline and since thermal has low resolution and also for agriculture projects or homogeneous areas, using the geolocation data works the best (alternative calibration). Standard pipeline extracts key points and does not use geolocation data while calibration, so that might always be an issue if the area is homogeneous. If you are using standard calibration, if there are oblique images, the project will not fail completely. However, there might be other issues.

    It would be best if you write us a support ticket so that we can look into depth through your quality reports, logfile, p4d file and images and work together to solve the issue.

  • Andre Daccache

    Hi, I wonder if it is possible to modify/adjust the value of the overlapped pixels? For example, I would like to calibrate pixels of the second image based on the pixels of the first during the image stitching process. I assume by default the pixel values on overlapped thermal images in Pix4D are averaged?    

  • Momtanu (Pix4D)

    Andre, unfortunately, that is not possible. You might try changing the pixels values of the raw images programmatically before processing in Pix4D if you have resources. 

  • Alex Alex

    Hello, I have a few questions:

     

    Is the DJI Zenmuse XT2 compatible for processing (we want to obtain a thermal map)?

    Does the final map have the thermal data and also the geo-reference?

    What is the optimal flight height for sensors that have a resolution of 640? What about those that have a resolution of 320?

     

    Thank you.

     

  • Momtanu (Pix4D)

    Hi Alex, yes you can use DJI's Zenmuse XT2 for getting a thermal map. The final map will have temperature values in the pixels and will also be georeferenced if you are not missing the geolocation data for some reason.

    The sensor has a resolution of 640x512. Since the resolution is low, we recommend a higher overlap and flight height. You can have a look at this: https://support.pix4d.com/hc/en-us/community/posts/360001400266-500-acre-thermal-orthomosaic and https://support.pix4d.com/hc/en-us/articles/115002442323-Image-acquisition

     

  • Andrea Gonzalez

    Contribution to the question: What to do in presence of entirely white or black images in the rayCloud and low calibration rate?

    In case the rayCloud presents either entirely black or entirely white images and the project shows a very low calibration rate, it means that the thermal camera used is not registered in our database.

    Before starting the process, to close the project and open the .p4d file with a text editor and below the <tangentialT2> and above the <cameraModelSource> lines, add the following line <pixelValue pixelType="uint16" min="-1" max="-1"/>

    The folders "1_initial" and "temp" (in the proyect) must be deleted before running step 1 "Initial processing"

    Bye

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