Processing Multi-Spectral UAS imagery For Value Added Data Analysis

Introduction:
Due to the advancement of technology UAS imagery can now be used to do things that could never be done before.  The advancement of camera technology allows for a very accurate assessment of vegetation health to take place.  This lab goes through processing and creating images taken with the MicaSense RedEdge sensor for further data analysis.  Imagery taken with a normal RBG sensor can be manipulated to see vegetation health, this is done by creating a false color infrared image.  The RedEdge sensor has an additional band between the red and near infrared, they call it the RedEdge, this allows for some of the most accurate vegetation health analysis.  In this lab a series of images and maps will be created to show the differences between a normal false color IR and a false color RedEdge. The images were taken at a house in Fall Creek Wisconsin.

Methods:
The first step of the process was to load the images into Pix4D to create an Orthomosaic Geotiff of the study area.  This was done similarly as previous lab with the exception of using the Ag-Multispectral Processing template. The second step was to create a composite image of all the bands together.  This was done in ArcMap by using the Composite Bands Tool.  The resulting image can be viewed with a variety of different band combinations. The final step to creating maps that can be used for value added analysis was to create a permeable/impermeable surface map using ArcGIS Pro.

Results:
The first thing to be noted is that only 69% of the images were calibrated successfully when processing the imagery in Pix4D, this is due to pilot error.  The camera was turned on when the UAS was still climbing to the proper altitude.  This resulted in images that were not successfully tied into the image.  If this was a real world application the site would be flown again to get better data.

The .tif files were then brought into ArcMap and combined together to create a composite image.  The bands can be combined in a number of ways to create a number of different images.  This first map is in RGB and is how we see through our eyes. The band combination for this image is 3,2,1.

The second map uses bands 5,3,2 to create a false color infrared image.  This uses the infrared, red and green bands to show vegetation.  Vegetation in the image show up as red because the IR band reflects off the vegetation at a cellular level.  The darker the red the vegetation is the healthier it is.

The final composite map was created with the RedEdge 

Conclusion:
The RedEdge sensor gives a more detailed analysis of vegetation health by providing an extra band.  From these images a Pervious/Impervious surface map was created.  The map is not perfect but it shows where the house and road are. The shadow on the upper left hand corner of the house is mistakenly labeled as pervious.  This map would have to be fixed to be used in a real world application.

Processing Pix4D imagery with GCPs

Introduction:
This project introduces the user to manually tying Ground Control Points (GCP's) to UAS imagery in Pix4D.  It is virtually the same as the previous 'Building maps with Pix4D' lab with the exception of manually adding ground control points.  The ground control points in this lab were physically places at the site of the imagery by the previous class before they took the flight data.

Methods:
The method to add pictures and process them is the same process as the 'Building maps with Pix4D' lab except for one step before the processing is actually started.  When the user gets to the main Pix4D screen, they want to go to Project - GCP/MTP Manager- and import their GCP coordinates from a text file.

 The next step is to run the initial processing.  After that is complete the it time to manually edit the GCP's to tie them to each individual image.  This can be done by clicking basic editor.  The user will then click on a GCP from the list and a list of images that are near that point will pop up.  The user will then click on a image and select the center of the point.  This is done for 5-10 GCP's in the data set and the program will automatically tie the rest down.

When the user finishes tying GCP's to images, it is time to reoptimize.  This ties the images down to the GCP's in the system.  If this is done correctly it is time to run the rest of the processing to get a point cloud and a DSM.

GCP's and Data Quality:
In the previous lab, the same imagery was processed without using GCP's.  This resulted in a good looking point cloud and DSM.  Pix4D automatically generates GCP's from the image data to tie the images to a location on earth.  To the untrained professional this may be enough.  Manually placing GCP's at a project site and taking their location with a highly accurate GPS allows for the user to input these into Pix4D and tie them to the imagery.  This creates maps that are highly accurate to the real world.  Highly accurate imagery and maps is highly valuable in many situations.

Maps:

The resulting map is the exact same as when processing it without adding GCP's.  The real benefit is with the spatial accuracy of the files.  This can be seen in the report that Pix4d creates when it finishes processing.

 No GCP

GCP's

The X variance when using GCP's is almost 7% higher than when done without GCP's.  This is saying that each point is within one meter of the actual location on earth.  The reason the Y variance is higher with the GCP's is because two flights were used and the data with two flights had more variance in the Y direction than the data with a single flight.  That is why it is lower than the first data set that was just one flight.

Conclusions:
In order to get highly accurate positional data from UAS data, GCP's must be incorporated especially if a local datum is not available.  If taking flights in a remote country or location GCP's may be the only option to tying the images to the earth.  The use of GCP's create more accurate data that is highly beneficial to the user in the long run.

Using ArcGIS Pro to engage in Value Added Data Analysis

Introduction:
The data collected by a UAS goes well beyond a pretty picture of what your house looks like from above. If gathered correctly, with the right sensor for the right job, your UAS data is a bountiful harvest waiting for you to exploit the value.  This lab goes through the steps of classifying an aerial image for different surface types using ArcGIS Pro.  The instructions for this lab were provided by ESRI Tutorial "Segment the Imagery"

Process:
The tutorial began with downloading the data and starting the ArcGIS Pro project file. The first step was to create a new layer by extracting bands 4, 1, and 3 from the imagery.  This created a false red image that shows vegitation as red and manmade objects like roads as grey.  This allows for easier classification later. The next step was to classify different segments of the image into groups based on spectral characteristics. To do this a Segment Mean Shift raster function was used. This new layer was saved as Lousiville_Segmented.  The next step of this process is to classify the different groups by specifying spectral signatures.  This is done with the image classification toolbar in ArcMap.  The user selects spectral signatures by drawing shapes in that signature then grouping like groupings together.  Then back in ArcGIS Pro, the groupings just created are imported into the classifier.  A few classifier tools are ran until a tif is created with all impervious and pervious objects on the image seperated  into two separate classes.  The next step is to determine if the new image accurately categorized objects into the proper classes, impervious or pervious.  The first step in this process is to create points then opening their attribute table and manually checking to make sure they were categorized correctly.  If they are not the user must make the appropriate adjustments. Then a confusion matrix was created to see the accuracy of the points added.  This resulted in a 92% overall confidence.  This allows the user to proceed in confidence. The next step was creating a graduated colors map to symbolize the areas with a high amount of impervious surface.  This was done by using a series of tools provided in the tutorial.

Final Map:

The Parcels with the highest area of impervious surfaces appear to be the ones that correspond to the location of the roads.  These parcels are large and are almost completely impervious.  The parcels with houses on are often an orange color due to the impervious surface like the house and the driveway mixed with an area of pervious surface like grass or ground.

Conclusions:
ArcGIS Pro makes data processing and analysis very streamlined.  It lays the process out for doing a task in a manner most people can easily understand.  The process for creating a map of impervious surfaces in the Louisville area was a simple procedure that resulted in a high quality map that could be useful in many situations.  The application of ArcGIS Pro with UAS Data is growing everyday.  It is growing increasingly easier to use UAS data in many everyday mapping platforms.  

Building Maps with Pix4d

Overview of Pix4d:
Pix4d is an image processing software that is based on automatically finding thousands of common points between images.  When the same common points (keypoints) are found on multiple images the, program generates a 3D point.  In order to create highly accurate 3D images, images must maintain high amounts of overlap to create as many 3D points as possible. It is recommended by Pix4d that at least 75% frontal and 60% side overlap is maintained.

Pix4dmapper 3.1 Usermanual 

When mapping terrain such as forest and dense vegetation, flat terrain with agriculture fields, and areas of snow and sand a minimum of 85% frontal and 70% side overlap is recommended for quality map construction in Pix4d.  In many cases camera settings, such as exposure, must be adjusted to receive quality images.  Pix4d can process multiple flights by overlapping images from both flights.  Both flights must be taken at a similar altitude.  Pix4d can also process oblique images.  For the user to get high quality maps from oblique images, the user must take images with their camera at multiple different angles.  Pix4d does not require Ground Control points to create accurate georeferenced maps.  The program will automatically do this with the coordinates that are tied to the images.  It is recommended that GCP's are taken of the study area and put into Pix4d to create the most accurate map possible but is not required.  After inputting and processing the images a quality report is created that includes details about the processing such as, summaries, quality checks, calibrations, maps, and information of the data.

How to Use the Software:

To begin processing in Pix4d the user must connect the program to where they want to save and what to call the new project.

The user must then import their images

Once the images are imported into the user must confirm or change,  image coordinate system, geolocation and camera settings. Camera setting are important to have right to allow for proper image processing.

In this case the camera shutter model was set to a Global Default and was changed to a Linear Rolling Shutter. 

The next step is to confirm the output coordinate system and GCP coordinate system and select the type of processing that needs to be done.  In this case we selected for Pix4d to create 3D Maps. 

When finished inputting the data the user gets to a screen that allows them to start the processing.  The user then sets his processing options and selects to only do the initial process.  If the user leaves, Initial Processing, Point Cloud and Mesh, and DMS, Orthomosaic and Index selected it will process all three parts while doing an initial process for each part.  This wastes time so it is advised to run the initial process, then run #2 and #3 together.

After processing the user will get reports for each process as well as the map.  All the user needs to do to view the map is turn cameras off in the layers section and turn on triangle meshes.  This will result in a map similar to below.

Maps/Data:

After the processing, Pix4d creates a report with lots of useful information.  The first page of the quality reports shows a summary, quality check of processing results, and a preview of the maps that are created.

 In the report is a section about overlap.  It is important that the images contained a high amount of overlap to create as many keypoints as possible.  This will ensure the highest quality 3d image. It can be seen that the bottom row of images have less overlap than anywhere else.  This could be due to a gust of wind pushing the UAS slightly off its path during the last sweep.

The .tif files created in Pix4d can be imported into a geodatabase in ArcMap and created into visually appealing maps.  The map below is a Digital Surface model and a Mosaicked image of the mine the data was taken from.  The map on the left shows the height of different objects in a color scale model and the map on the right shows the visable band image of the area.

A useful tool in Pix4d is creating videos of maps using the in software fly over video recording tool.  This allows the user to create visually appealing flyover videos of the area by simply setting way points and rendering the video.

First Impressions of Pix4d:
At first impression this software is easy to use and can be very beneficial for many applications in the UAS world.  Taking drone imagery then turning it into a georeferenced 3D map without Pix4d would require extensive knowledge of different programs and algorithms.  Pix4d allows for a user to quickly create high quality 3D maps in  a short period of time.  To create an image that is more spatially accurate Ground Control Points should have been manually put in and taken of the area then tied to the imagery.  Pix4d automatically creats GCP's from the locations on the images but this is not completely accurate.  This first look at Pix4d was a great introduction to 3D mapping from UAS data.  There is much more to learn about image processing in Pix4d.



Conclusions:
Overall, Pix4d is a very useful tool in importing and processing large amounts of images from a UAS platform.  It streamlines the workflow allowing the user more time for analyzing the data to gain contextual insight and less time processing and creating maps and images.