Introduction:
When Pix4D performs bundle-block adjustment on aerial images, the horizontal and vertical accuracy are dependent on the on-board GPS. Dependence on the on-board GPS can cause distortion of the resulting products. By recording the positions of recognizable features within the study area, it is possible to rectify the bundle-block adjustment to the known points and thus reduce distortion. For these points to be effective, the image analyst needs to be able to identify the exact location of the control point.
Methods:
The Litchfield mine site has been used for several of the previous labs, so it seemed prudent to place permanent GCP markers around the location, to ensure the accuracy of future flights. The markers were constructed of black plastic, commonly used to make the 'boards' surrounding hockey rinks. The plastic is flexible and weatherproof, so they should survive the elements, as well as any vehicles running them over. The GCP markers were spray painted with bright yellow spray paint, so a large 'X' is visible in the center of the marker. This 'X' will allow the analyst to precisely identify the point later when performing bundle block adjustment. After marking the 'X' on all of the markers, they were each labeled with individual letters of the alphabet, so they may be more easily identified in the field during later data collection.
On April 11, 2016, we travelled to the Litchfield site, and placed the GCP markers around the site. Before placing the markers, we removed sticks and flattened the soil/rock-material to ensure they aren't moved by wind. All of the selected locations had good aerial visibility, and were outside of well-travelled areas. The markers were evenly placed throughout the mine area, with some around the edges and in the center.
Discussion:
The control points also need to be recorded at a higher level of accuracy than the GPS on the imaging platform, otherwise they may actually decrease the accuracy of the resulting imagery. The markers were placed outside of well-travelled areas so they wouldn't interfere with mine operations, and to reduce the chance they will be buried, destroyed, or moved by mine equipment or material. It was important that all of the marker locations had high aerial visibility, because markers with poor aerial visibility would only be accurately identifiable in some of the images, reducing the effectiveness of the bundle-block adjustment. The spacing of the GCPs was extremely important, as they increase the accuracy of imagery between them, and can cause distortion if the density of them differs within the study area.
Conclusion:
GCP marking has been one of the most discussed topics within the UAS community as of late, and this exercise provided good insight into GCP installation and creation. The placing of GCP markers is the most time-consuming portion of the data collection process, so by installing permanent markers, it will allow future flights to be performed more quickly without having to sacrifice positional accuracy.
Sunday, April 17, 2016
Sunday, April 10, 2016
Creating Hydro Flow Models from UAS collected imagery by means of advanced point cloud classification methods.
Introduction:
UAS imagery can be used to generate point clouds in a '.las' format, similar to LiDAR data. However, point clouds generated from UAS imagery require a substantially different processing workflow than their LiDAR collected contemporaries. All of the non-ground points should be removed, before flow models can be created from a point cloud. This paper will describe one method for performing this critical step. An orthomosaic and DSM can be useful for specific industries, but by classifying the point cloud and generating a DTM, substantially more value can be added to the data. The DTM surfaces can potentially be used to model runoff, in order to prevent erosion on construction sites or mine operations. If multiple flights of the same area were performed monthly, the classified orthomosaics could be used to perform post-classification land use/land cover (LULC) change detection. Post-classification LULC change detection would allow mine reclamation personnel precise information regarding the progress of the reclamation process, specifically how much land changed from one LULC class to another.
Study Area:
The UAS images were captured of the Litchfield gravel pit on March 13, 2016 between 11:30AM and 2:00PM. The day was overcast, eliminating defined shadows and reducing the overall contrast in the captured images. The imagery used for point cloud generation was flown at 200ft, with 12 megapixel images. The images were captured using a Sony A6000, with a Voightgander 15mm lens at f/5.6 (Figure 1).
Methods:
1. The first processing step is to import the imagery, geolocation information, and camera specifications into Pix4D as a new project. The processing should be calibrated to produce a densified point cloud, as well as an orthomosaic.
2. Next, object based classification will be performed on the orthomosaic, using the Trimble eCognition software.
3. The classified output will be converted to a shapefile, using Esri ArcGIS.
4. ArcGIS will be used to reclassify the densified point cloud by using the 'Classify Las by Feature' tool and the shapefiles created in the previous step.
5. The 'Make Las Feature Layer' and 'Las dataset to Raster' tools will be used to create a DTM from only the ground points.
6. (optional) If the output DTM still contained some of the low vegetation, the 'Curvature', 'Reclassify', 'Raster to polygon', and 'Classify Las by Feature' tools will be used to identify and reclassify any remaining low vegetation points.
7. The ArcGIS Spatial Analyst toolbox will be used to perform runoff modeling
Discussion:
The imagery was collected without GCPs, however, previous imagery was collected with GCPs so it was possible to use the image-to-image rectification process to add GCPs to the image - increasing its overall spatial accuracy.
LiDAR sensors also record pulse intensity, which allows the analyst to more easily differentiate between different types of land cover for a given point, however as the UAS collected data using passive remote sensing techniques, this is not possible.
Conclusion:
The classification accuracy could be even further improved by capturing the imagery with a multi-spectral sensor.
UAS imagery can be used to generate point clouds in a '.las' format, similar to LiDAR data. However, point clouds generated from UAS imagery require a substantially different processing workflow than their LiDAR collected contemporaries. All of the non-ground points should be removed, before flow models can be created from a point cloud. This paper will describe one method for performing this critical step. An orthomosaic and DSM can be useful for specific industries, but by classifying the point cloud and generating a DTM, substantially more value can be added to the data. The DTM surfaces can potentially be used to model runoff, in order to prevent erosion on construction sites or mine operations. If multiple flights of the same area were performed monthly, the classified orthomosaics could be used to perform post-classification land use/land cover (LULC) change detection. Post-classification LULC change detection would allow mine reclamation personnel precise information regarding the progress of the reclamation process, specifically how much land changed from one LULC class to another.
Study Area:
The UAS images were captured of the Litchfield gravel pit on March 13, 2016 between 11:30AM and 2:00PM. The day was overcast, eliminating defined shadows and reducing the overall contrast in the captured images. The imagery used for point cloud generation was flown at 200ft, with 12 megapixel images. The images were captured using a Sony A6000, with a Voightgander 15mm lens at f/5.6 (Figure 1).
 |
| Figure 1: The Sony A6000 |
Methods:
1. The first processing step is to import the imagery, geolocation information, and camera specifications into Pix4D as a new project. The processing should be calibrated to produce a densified point cloud, as well as an orthomosaic.
2. Next, object based classification will be performed on the orthomosaic, using the Trimble eCognition software.
3. The classified output will be converted to a shapefile, using Esri ArcGIS.
4. ArcGIS will be used to reclassify the densified point cloud by using the 'Classify Las by Feature' tool and the shapefiles created in the previous step.
5. The 'Make Las Feature Layer' and 'Las dataset to Raster' tools will be used to create a DTM from only the ground points.
6. (optional) If the output DTM still contained some of the low vegetation, the 'Curvature', 'Reclassify', 'Raster to polygon', and 'Classify Las by Feature' tools will be used to identify and reclassify any remaining low vegetation points.
7. The ArcGIS Spatial Analyst toolbox will be used to perform runoff modeling
Discussion:
The imagery was collected without GCPs, however, previous imagery was collected with GCPs so it was possible to use the image-to-image rectification process to add GCPs to the image - increasing its overall spatial accuracy.
LiDAR sensors also record pulse intensity, which allows the analyst to more easily differentiate between different types of land cover for a given point, however as the UAS collected data using passive remote sensing techniques, this is not possible.
Conclusion:
The classification accuracy could be even further improved by capturing the imagery with a multi-spectral sensor.
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