Processing Multi-Spectral Imagery

Introduction

          The Micasense RedEdge 3 is a multispectral camera that can capture 5 different bands all at the same time. The bands being capture simultaneously are the red, green, blue, RedEdge and near infared (NIR) bands. The imagery used in this lab is from a RedEdge sensor. Before getting into the methodology and results of this lab it is essential to have a little background regarding the RedEdge sensor. The camera has a lens focal length of 5.5 mm and a lens field of view of 47.2. The image sizes are 4.8 mm by 3.6 mm and the resolution is 1280 x 960 pixels. A standard RGB sensor only has red, green and blue, whereas the RedEdge boasts the RGB along with NIR and RedEdge. It is essential to take note of the proper order of the bands which is blue, green, red, RedEdge and NIR. The addition of the RedEdge and NIR allow the user to have very precise quantitative data regarding the type and healthiness of vegetation. The ultimate goal of this lab is to process imagery captured from an unmanned aerial system and to then classify whether the land use is pervious or impervious.

Methods

          The image set used for this lab was very large, so a good amount of time was allotted to allow the files to copy over from the share folder, the images were provided from Professor Joseph Hupy. Once the data sets were copied over a new project was opened in Pix4D. From here Figure 1 below shows how the new project was named, the name included the data the images were processed, the location of the images and the type of sensor that was used as well. .  The file was saved in a location dedicated to this exercise and using a name that tells the user everything they need to know about what is in the folder.

Figure 1

   

           Figure 2 illustrates a one of the few changes that had to be made before the data could be processed. In previous exercises 3D maps were created, but for the purposes of examining land cover, a Ag Multi spectral template was selected. The RedEdge is compatible with this template as shown on the right side. By using this template there will be no Raster DSM or Orthomosaic Geotiff.

            A few of the processing options needed to be changed before the data could be processed. The Geotiff with transparency box was check, as there was issues when it was left unchecked. From here the processing was ran much the same way as was went through in detail in the previous two labs.

Figure 2

           Figure 3 below shows the amount of overlap that was in the images after processing the images in Pix4D. It is clear there is a sufficient amount of overlap on 80% of the flight, the area that is being examined close has good overlap.

Figure 3

         Figure 4 is the quality report, this is always provided as it gives the viewer an idea of the quality of the data. Only 69% of the images were calibrated, this was explained in Pix4D by looking at where the pictures were taken from, as the drone was taking off there were many images on the way up that were not used because they had no benefit.

Figure 4

             Figure 5 below shows a summary of information about the area covered and the data it was processed as well as the band used in this particular flight shown below. The camera model names are all shown clearly, the blue, green, red, RedEdge and NIR. The second to bottom row shows the amount of area that was covered in the flight, that being 6.904 acres.

Figure 5

          After the processing was completed in Pix4D there is a Geotiff for each of the five bands. For use in this lab the bands needs to be brought together as one composite band. This was done in ArcMap using the composite tool under the help menu. After all five bands were added as the input, a output location in the same folder used in the Pix4D processing was selected. Once a composite was made the layer was copied three times in order to ensure there is three different combinations of bands that each display something different. This is where is it essential to make sure the bands are in the correct order, the correct order being blue, green, red, RedEdge, NIR. Changing the order of the bands allows for different bands to be the center displayed.


Results

          Below are maps showing RGB, Near Infared, and RedEdge, below that is a map showing what areas of the flight are permeable and impermeable. Each of the figures 6-8 has the bands arranged in different ways as shown in the legends and that accounts for the variability in each.

Figure 6

          Figure 6 above is the closest of figures 6-8 to an orthomosaic, though there is a slight influence from the red band that shows up as a purple color on the map. Vegetation shows up much the same as it would on an aerial image, the healthy vegetation is a dark green while the unhealthier vegetation seems to appear more brown. The road on the west side of the map runs north to south and the driveway to the house runs west to east. The main structure in the map is almost center and the structure is a house.

Figure 7

           Figure 7 above illustrates the false colors generated from a near infrared band. The healthiest vegetation is shown by the darker shades or orange as shown to the east of the house. When comparing the field to the north of the house it is clear how sparse and unhealthy the vegetation is compared to the area east of the house. 

Figure 8

         Figure 8 above does a fantastic job of illustrating what areas are healthy vegetation and what areas are not. The road on the west side of the map is shown clearly, along with the drive way. The healthy vegetation to the east of the house can be shown very distinctly by a dark red/pink. In comparison the area to the north of the house that looked unhealthy in figure 7, it now is shown here that the vegetation is in fact healthy. In contrast the southern third of the map is a very pale color rather than a dark red. Around the house a large majority of the yard is shown dark red, most likely this can be assumed because the homeowner most likely waters there yard.

Conclusion

            This lab required the knowledge of both Pix4D along with ArcMap and ArcPro. By processing multi-spectral imagery gathered by the MicaSense RedEdge Senser the type of land use was able to be determined. Each band has its own benefits and the use of both Pix4D and the Arc programs together helped to showcase the skills learned throughout this course. Sensors such as the RedEdge are designed for use in the Agriculture department and they can be used in a similar fashion to this exercise in order to see what areas of vegetation are doing good and what areas are not. This has uses in the commercial and residential levels. Value added data analysis coupled with UAS data allows for the user to see what areas are doing well and what are not, the UAS data allows for accurate imagery with the sensors.