Activity 10: Construction of a point cloud data set, true orthomosaic, and digital surface model using Pix4D software.

Introduction

The last activity was using  the GEMS sensor to collect imagery as well as the GEMS Software tool to create mosaics of the images collected. This week we are exploring another image processing software package called Pix4D. We explored some of the simple tasks that Pix4D has to offer. The software functions by finding common points on multiple images. Many .JPG files are loaded into the software and it lays them out in order of time and location they were collected. This can either be done having the images be geotaged as they are collected, which means a lat/long position is assigned to each image, or a .bin file can be loaded seperate of the images. The .bin files contain flight data about the flight path and in the case of the GEMS sensor when images were collected. This flight file is then matched up with the .JPG files and Pix4D can lay them out in order. Once the images are lain out in order Pix4D starts looking for points in multiple images that overlap. Points that appear in two or more images are called key points. These points overlap and align and the higher image overlap you have the more of these key points the software will find resulting in a better quality mosaic and 3D image. 

When it comes to image collection for images that are going to be used in Pix4D there are some considerations that need to be taken into consideration. One of the biggest is that a sufficient amount of image overlap is planned in the flight. For creating 3D images or getting good quality mosaics in Pix4D at least 75% frontal and 60% side lap is required. This is the recommendation for most images but not all it varies with the terrain being collected in the images. When looking at agriculture even more overlap is recommended because of the similarity of the images. Corn for example looks very similar in all the images and Pix4D will have a hard time finding key points so increased overlap is essential to make sure the mosaic still is high quality. This doesn't only apply to agriculture, any uniform surface such as water, sand, snow or trees should should be flown with increased overlap. Another tip that can help improve the quality of the mosaics is to set the exposure settings on the camera so that as much contrast as possible is captured in the images.

Combing two flights into one large mosaic is possible however certain parameters should be met. There are a couple of things that are important the biggest is to make sure that the conditions during the two flights were pretty similar. You don't want it to be sunny one day and cloudy the other day because Pix4D will combine the two flights but they will definitely still look like two separate flight not one big one. The light difference will be very obvious. Also making sure you fly the flights at the same altitude and overlap rate is also important. If the flights are collected at different altitudes you will be dealing with two different scales which will look weird when it is mosaiced. Similar overlap is also important and overlap between the two flight is also important so that areas between the flight don't get left out. 

Ground control points or GCPs are not necessary for Pix4D to process images however they can be useful. When very high spatial accuracy is needed like with volumetric analysis or vegetation change the GCPs assure that all the images are tied to the exact actual location on the earths surface or very close to it depending on the accuracy of the GPS unit. The GPS unit we have on campus has sub mm accuracy so that is very good for increasing the spatial accuracy of the images.

One final part of this software that is nice is the quality report. The quality report basically tells you how well the images you input turned into the mosaic. Figure 1, 2, and 3 are parts of the quality report. 

Figure 1 This is the first portion of the quality report. This is basically an overview of the project as a whole. This portion tells you the project name, when it was processed, how large of any area the image covered and how long the project took to finish. Also included in this part is a preview of what the RGB mosaic is going to look like after processing and how the DSM is going to look. The DSM or digital surface model is dealing with the 3D aspect of the image. In this report you can see that the shelter in the middle of the RGB image stands out as bright red in the DSM which means Pix4D is reading as higher elevation than the rest of the image.One last part is the calibration details which tell you how many of the images were able to be used by the software. It also tells you how many images were geolocated. The higher percentage both these numbers are the better the mosaic will turn out. For this report both categories are at about 95% which is very good.

Figure 2 What figure 2 is showing is how well the area of the mosaic was overlapped. Green areas mean that 5 or more images overlapped in those areas while the yellow and red areas are only 1 to 2 images overlapping. The more green area the better the mosaic and the more accurate the 3D image will be. This image has very good overlap but it is easy to see why it is important to fly a larger area than you actually want to collect data for. The further to the outside of the image you get the less overlap to assure good overlap throughout fly a much larger area than you actually want the data for.

Figure 3 This is an review of how many key points were found between the images. The darker the area the more key points are found the lighter the fewer were found. Again maximizing the number of key points is essential for getting a good mosaic and 3D image.

Methods

For this activity we processed two data sets. One is of the Eau Claire soccer complex here in town using the GEMS sensor and the other is the same location with a Canon SX260 camera. Please watch the video below (Figure 4) to see how these were run and what the final products of each are. Figure 5 is the resulting mosaic from the GEMS sensor project.

Figure 4  This is a video on how to create new project in Pix4D and run imagery you have collected

Figure 5 Mosaic of the Eau Claire Soccer Park collected by the GEMS sensor

The video in Figure 4 was showing the process for running the GEMS data in Pix4D. The same steps are followed in order to run the SX260 except when the images are being added. The images for the SX260 are geotaged meaning they have lat/long information included with them unlike the GEMS images so there is no need to add the .POG file with the trigger locations in it. Figure 6 is the mosaic from the SX260 project.

Figure 6 This the mosaic created when the images collected with the Sx260 camera are run in Pix4D   

Once the projects have run an the mosaics are completed there are many things that you can do to manipulate the mosaics. A few we explored are the creation of "fly" through animations of the mosaics, line measurements, surface area measurements and volume calculation of objects in the mosaic. Figures 7 and 8 are the two animations I created moving through the mosaics.

Figure 7 This is the animation for the GEMS data or Figure 5

Figure 8 This is the animation for the SX260 data or Figure 6

The video in figure 9 explains how to created line features and measurements, polygon measurement and measure the volume of a 3D object. All of these tasks are easy to and didn't take long. The video also discusses how to export the created features so that they can be use in ArcMap to create maps with.

Figure 9 This is a short tutorial on some of the functions in Pix4D. All of these functions were performed on the GEMS data set in the video.

Discussion

I thought that so far Pix4D has been very easy to use. The help menu makes it easy to find basically anything you don't know how to do in the software. How you interact with the software is very easy as well. There are hidden details that need to be paid close attention to when creating and running projects however for a pretty new user of this software I haven't had many problems getting tripped up by the details.

Creating the line, polygon and volume measurements and shape files was very straight forward and easy to understand. I created a few maps from the shape files I made in Pix4D. Figure 10 is the maps with GEMS data and figure 11 is the Sx260 data.

Figure 10 This is the a masked portion of the GEMS data run in Pix4D and brought into ArcMap. You can see the 3 features I created and brought in from Pix4D. They line up very will with the imagery or in other words they are in the same location on this map that they were when I created them in Pix4D. This spatial accuracy and consistency is important for accurately mapping spatial features. If you look at the difference between the GEMS imagery and the base map you can see that the GEMS image is moved slightly left and downward from where the base map is. This is because of different levels of spatial accuracy between the two maps. We won't know which is more accurate until we use GCPs to tie down the imagery to the earths surface which is in the next lab.

Figure 11 This is the masked SX260 data run in Pix4D. Again you can see the features I created in Pix4D and exported to ArcMap. The thing that should be noticed about this map is how poorly the base map and SX260 imagery line up. The SX260 imagery is moved way down and to the right of from where the base map imagery is. This shows how poor the GPS unit in the camera is that geotags the images. The base map, imagery and features are all in the same projections and yet the imagery is off by probably 50 meter or more. This is not good spatial accuracy and should defiantly be tied down with GCPs to place in the correct location. It will be interesting to see how well this issue is corrected by the GCPs next week when we run some SX260 imagery with GCPs.

Pix4D has a lot to offer as a software package and from this small introduction to some of its functions this week I look forward to seeing what else it has to offer. The next project will be incorporating GCPs when processing imagery.