Use of GEMs Processing Software

Introduction

Get familiar with the product (Questions in italics)

What does GEMs stand for?

GEMs is an acronym for Geo-localization and Mosaicing System.

Name what the GSD and pixel resolution are for the sensor. Why is that important for engaging in geospatial analysis. How does this compare to other sensors?

(Fig. 1)  GEMs sensor from Sentek Systems.

Ground sampling distance (GSD) for the GEMs is 5.1 cm @ 400ft and 2.5 cm @ 200ft.  The pixel resolution for the sensor is 1.3 mega pixels (MP) for both the RGB and the Mono. Knowing the GSD and pixel resolution help you determine the quality of data therefore helping you select the correct sensor for the task.  The pixel resolution is very low by today's standards.  The majority of every camera out there including your cell phone has a pixel resolution of 10 MP or higher.

How does the GEMs store its data?

The data collected by the GEMs is stored on a USB jump drive which is mounted on the sensor during the flight.

What should the user be concerned with when mounting the GEMs on the UAS?

The following is a list of concerns when mounting the GEMs sensor on a UAS platform.

• The GEMs sensor is designed to for the cameras to be point downwards towards the ground.
• The GEMs sensor should be attached to the flattest portion of the underside of the UAS platform.
• You should not place and magnetic material withing 4" of the GEMs sensor.
• Minimize vibrations to the sensor the best as possible.
• Reduce the electromagnetic interference (EMI) to prevent jamming of the GPS unit.

Examine Figures 17-19 in the hardware manual and relate that to mission planning. Why is this of concern in planning out missions?o

• Figure 17 is displaying as the elevation of the sensor rises the GSD also rises.  
• Figure 18 is displaying the correlation to elevation of the sensor and the speed of platform to the quality of imagery the sensor can capture.
• Figure 19 is displaying the correlation to elevation of the sensor and the row spacing of the flight path to the quality of imagery the sensor can capture.

These figures are of great concern for planning out UAS missions.  You have to set the parameters which include the altitude, speed, and the row spacing when programming the mission plan.  You want to set the parameters to collect the highest quality data you can but you must also be concerned with flight time of the UAS.  You would also take into consideration the goal of the project to determine the quality of data you need.  Setting these parameters incorrectly will give you erroneous results and you will have to adjust the parameters and refly the mission.

Write down the parameters for flight planning software (page 25 of hardware manual). Compare those with other sensors such as the  Cannon SX260, Cannon S110, Nex 7, DJI phantom sensor, and Go Pro.

When comparing the GEMs with the listed sensors you can see the GEMs sensor has the lowest megapixels of any sensor on the list (Fig 3).  I was unable to obtain all the information for all the sensors.  With the information I was able to obtain you can see the GEMs sensor fall behind in every category.

Read the 1.1 Overview section. Then do a bit of online research and answer what the difference between orthomosaic and mosaic for imagery (orthorectified imagery vs. georeferenced imagery). Is Sentek making a false claim? Why or why not?

The orthomosaic process is a combination of orthorectification and mosaicking.  Orthorectification is the term to focus on here.  "Ortho-rectification is the process of correcting imagery for distortion using elevation data..." (ImStrat)  The GEMs software does not utilize any elevation data in the calculations when creating the mosaic images.  So it is with this information I believe Sentek is make a false claim of creating "Orthomosaiced RGB, NIR, and NDVI imagery." (Sentek)

What forms of data are generated by the software?

The software generates the following data:

• RGB (Red, Green, Blue additive color model)
• NIR (Near-Infrared)
• NDVI (Normalized Difference Vegetation Index)
• GPS Coordiantes for previous images

How is data structured and labeled following a GEMs flight? What is the label structure, and what do the different numbers represent?

The images are in the file by type and numerical order.  Only images in the file after the flight are Mono0 images and RGB0 images.  There is also a number of text files/folders along with the .bin file located in the folder.

The labeling scheme for flight date and time on the folder is GPS time.  Week=# and that #= (a specific date). TOW=H-M-S (Hours-Minuets-Seconds).  The GPS time is automatically generated by the GPS unit and the script with in the GEMs unit automatically attaches it to the file for the flight.


What is the file extension of the file the user is looking to run in the folder?

The user is looking for a .bin file to open in the Sentek software program.

Methods

What is the basis of this naming scheme? Why do you suppose it is done this way? Is this a good method. Provide a critique.

The labeling scheme for flight date and time on the folder is GPS time.  Week=# and #= (a specific date). TOW=H-M-S (Hours-Minuets-Seconds).  The GPS time is automatically generated by the GPS unit and the script with in the GEMs unit automatically attaches it to the file for the flight. To convert the time to conventional dates and time you must find a conversion calculator.

The GPS time is a good theory but leaves a bit to be desired when the operator doesn't understand and cannot quickly convert the time to their own conventional time.  Trying to find the conversion calculator I linked above took a bit of searching to locate.  Additionally, this conversion calculator was not created by the company, it is just a random person who created the tool.  How do I know the conversion calculation they are using is correct?  I feel it would be the best if they incorporated an automatic conversion with in the script or a converter with in the software which comes with the GEMs unit.

Explain how the vegetation relates to the FC1 colors and to the FC2 colors. Which makes more sense to you? Now look at the Mono and compare that to the vegetation.

(Fig. 1) Display bar scales for FC1 (Left) and FC2 (Right).

The display scales for FC1 and FC2 are representing healthy vegetation.  With FC1 red is the healthiest color which will be displayed.  With FC2 green is the healthiest color which will be displayed.

I feel FC2 makes more sense for the color scheme.  When I think of healthy vegetation, I envision bright green leaves and not red.  Throughout my existence I have been taught to associate red with danger such as stop lights, firetruck lights, lava, ect.

(Fig. 2) Display bar for both Mono images.

The display for the Mono image has the unhealthy vegetation displayed in black and the healthy vegetation displayed as shades of gray to white being the healthiest.  This scheme follows a conventional way of thinking and the average reader would interpret the information properly.

Now go to section 4.5.5 of the software manual and list what the two types of mosaics are. Do these produce orthorectified images? Why or why not?


Two types of Mosaics

1. Fast Mosaic
2. Fine Mosaic

No, neither of these mosaics produce an orthorectified image.  As stated before to be an "orthorectified" you have to utilize elevation data to complete the process.  The definition used in the manual for the fine mosaic say it uses "techniques to finely align the imagery".

Generate Mosaics.  Describe the quality of the mosaic. Where are there problems. Compare the speed with the quality and think of how this could be used.

The quality of the mosaics are decent.  The time to generate the mosaics was short compared to more complicated systems.  This could be very beneficial when working in the field and you want to make sure your flight successfully captured your images.  If the flight was not successful you would know immediately and would have the opportunity to make the necessary adjustments and refly the mission.  Additionally, if you could analyze the results right in the field you could also use that information to locate and physically examine points of interest before leaving the site.

There are areas in the images which do not line up perfectly.  Most noticeably on the east portion on the images there is an alignment issue (Fig. 7-11).

Navigate to the Export to Pix4D section. What does it mean to export to Pix4D? Run this operation and look at the file. What are the numbers in the file used for? (Hint: you will use this later when we use Pix4D)

The GEMs software generates a file which can be open and utilized in the program Pix4D.  Pix4D
is a photogrammetry software which CAN produce orthomosaic 3D images if the proper elevation values are captured with the image.  The software produces Excel files which contain the latitude, longitude, Omega, Phi, and Kappa for each image in the respective modes (NDVI FC1, NDVI FC2, NIR, RGB).

What is a geotif, and how can it be used? 

Geotiff refers to a TIFF file which has geographic data embedded with in the image file.  The geographic data contained within the image can be utilized to position the image in the correct geographic location on software programs such as ArcMap.  The Geotiff is completely open source which allows the file type to be interoperable.  The file type behave exactly like a regular tiff file but has the additional geographic data attached.

Go into the Tiles folder and examine the imagery. How are the geotifs different than the jpegs?

After analyzing the images I cannot tell see any visual difference when viewing them in the windows explorer viewer.

Now open Microsoft Image Composite Editor (ICE) software and generate a mosaic for each set of images. What is the quality of the product compared to GEMs. Does this produce a Geotif? Where might Microsoft Image Composite Editor be useful in examining UAS data?

The quality seems to be the same as the GEMs software as far as resolution quality though the color schemes are different.  The black background makes the image standout better.  However, the programs lacks the ability to fully "stitch" or mosaic the image correctly. The program does not produce a Geotiff.  All of the images I stitched images I created were "upside down" (South was the top of the image).  The ICE program could be very useful in the field in analyzing captured images.  The program runs fairly fast and would allow you to "stitch" your images together to see the results and make sure you didn't have any gaps in your captured data.  All of the terminology throughout the program referred to panoramic images which I feel led to some of the errors I encountered.

Results

(Fig. 3) Comparison of various sensor typically attached to UAS platforms.

Microsoft Image Composite Editor

The first image I ran in the ICE software was the NDVI FC1 images.  Inspecting the results and comparing them to the results of the GEMs software the ICE software incorrectly stitched the image together (Fig.4).  The large yellow clump of vegetation on the left side of the image is incorrectly placed.

(Fig. 4) NDVI FC1 image "stiched" in Microsoft ICE. 

The second image I ran in the ICE program was the NDVI FC2 images (Fig. 5).  The results were better than the FC1 images.  The majority of the image is assembled correctly in comparison to the first one.  However, there are still a number of errors with the stitched image.  The square/rectangle sitting by it self at the bottom of the image is the most noticeable error.

(Fig. 5) NDVI FC2 image "stitched" in Microsoft ICE.

The final image I ran in the ICE program was the NDVI mono images (Fig. 6).  The created image has the least amount of errors for the three images I ran through the program.

(Fig. 6) NDVI Mono image "stiched" in Microsoft ICE.

Additional research will be need to work the bugs out of the ICE system to render it useful in the UAS world.


GEMS Software

The fine mono display gives the best image clarity image of the two mono displays (Fig. 7).  The image displays more precise locations which are healthy and those which are not.

(Fig. 7) Mosaic image of the Fine Mono results from the GEMs sensor.

The fine NDVI mono displays a lower clarity between healthy and unhealthy vegetation.  The brightest (healthy) areas of (Fig. 8) seem to run together and is more generalized.  According to the software manual the NDVI mono is beneficial when monitoring emergence of new vegetation.

(Fig. 8) Mosiac image of the Fine NDVI Mono results from the GEMs sensor.

The NDVI FC1 is the same results as the NDVI mono but displayed with different color ramp variation (Fig. 9). This adjustment to the color ramp makes it easier to read and see variations in the vegetation health.  However, with the "healthy" vegetation displayed as orange/red gives the reader the wrong idea of the health of the plant in my opinion.

(Fig. 9) Mosaic image of the Fine NDVI FC1 results from the GEMs sensor.

The NDVI FC2 image is the same display as the NDVI FC1 but the color ramp has been changed to fix the issue of readers misunderstanding the displayed results (Fig. 10).  Red is displaying the unhealthy vegetation and the green is displaying the healthy vegetation.  After comparing FC1 and FC2 I believe the FC1 shows better contrast between healthy and unhealthy but misleads the reader.

(Fig. 10) Mosaic image of the Fine NDVI FC2 results from the GEMs sensor.

The find RGB mosaic displays and great overhead view of the area (Fig. 11).  Comparing this image to the above results it is easy to see the darker green vegetation in the image is the healthiest.  This image is very beneficial when trying to locate exact areas and types of vegetation in the study area.

(Fig. 11) Mosaic image of the Fine RGB results from the GEMs sensor.

Conclusion

Relate the GEMs sensor to the software.

The GEMs sensor and the created software integrate well together and produce results in a timely manner.  The software is super easy to use and the instructions walk you through the proper steps to achieve the desired results.  The operator of the program need not be a remote sensing expert to understand the basics.  The only complaint I have is the file/date labeling system makes it complicated to decipher what day the flight was actually flown.

Relate the GEMs to UAS applications.

The GEMs sensor and software has the ability to be a valuable tool with various UAS applications. The weight and the size of the sensor make it capable of being attached to virtually any platform in the UAS industry.

One of the limiting factor with the GEMs sensor is the narrow field of view.  The narrow field of view requires long flight times to cover a small area.  Most of the multirotor UAS platforms only have a maximum flight time of 30 minutes.  If you were trying to fly an actual farm field of any size it would require multiple flights.  If the company was to increase the field of view you could dramatically decrease the flight times and obtain the same quality of data with the proper end and side lap set in the parameters.

Overall provide your impression of the GEMs imagery and the sensor.

The GEMs sensor in general is a good tool to be used with UAS platforms.  With a few minimal adjustments the sensor could be great.  For what this sensor cost I would have expected a higher resolution camera.  My cell phone which cost considerably less has a better camera on it.  I feel the technology could be derived from cell phones to increase the megapixels of the GEMs sensor.  The low megapixels reduces the ability for the sensor to focus in on individual plants would be useful in orchards or tree type applications.  As stated above the narrow field of view restricts the uses for a real farming applications.  With all the sensor options out there on the market today I cannot say this sensor would be on the top of my list until improvements were made to the camera field of view and pixel resolution.

Sources

ImStrat Corporation PDF document http://www.imstrat.ca/uploads/files/brochures/orthomosaic.pdf
Sentek Software User Manual