Agisoft photoscan professional gcp free

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
With the Photos pane showing the image thumbnails, images in which an observation has been manually set pinned will be annotated with a green flag.
 
 

Agisoft photoscan professional gcp free.大疆精灵4航测输出正摄影和三维模型教程

 
Agisoft photoscan professional user manual This will show the images that you think has the GCP in view in the window free study guide for cpc exam. Metashape, formerly known as Agisoft PhotoScan, is a standalone software Import GCP to control the accuracy of results, scale tool to. Although photogrammetry software, such as Pix4D and Agisoft PhotoScan, can also estimate volume without GCPs [25,26], the cases applied by.

 

Agisoft photoscan professional gcp free

 

FYI, there is a user forum for Agisoft here. You will get an informed response from the helpful developers in a timely manner. I work the same thing here and I am using the old aerial photography. You should click on the View Estimated see red circle and see if it is compare to the one you have View source. Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Start collaborating and sharing organizational knowledge.

Create a free Team Why Teams? Learn more about Teams. Ask Question. Asked 3 years, 3 months ago. Modified 29 days ago. Viewed times. Cli k OK , the hit the delete ke to remove these points. We have used 3 images here as a threshold due to the generally high overlap; in other projects you may need to use only 2. You can repeat this selection and filtering process using some of the other criteria listed below. App op iate th eshold alues ill a a d the e ill ot e a ight o e to use.

Ho e e , g adual selection is a valuable tool to identify and remove points that are either outliers or at the weakest end of the quality distribution. Reprojection error: This metric represents image residuals, but is complicated by the fact that PhotoScan scales these values based on the image matching, so the do t di e tl efle t values in pixels for each point.

Nevertheless, it is useful in order to identify and remove the worst points largest values. Appropriate values to use as thresholds will vary between projects, and will depend on the number of images matched per point and the imaging geometry. It might be to do with the scale that points have been matched at. At this point, it is worth checking that there are no images for which almost all observations have been removed.

In the Reference pane, ie the P oje tio s olu i the Cameras; images with few observations e. Ideally, the distribution of such points, rather than their total number, should be the criterion for removal. To select points manually, in the Model pane, rotate the view so that you can see all the outlying sparse points as clearly as possible.

Click-drag in the Model pane to select the points you wish, and press the delete key to remove. Click the Navigation button to return the mouse to a navigation function rather than selection. You georeference for your survey. These data have already been automatically used as control measurements – even without any GCPs in the project, the 3D model appears sensibly oriented and scaled. However, typically, these GPS data are rather poor precision e.

PhotoScan assumes a default camera position precision of 10 m as seen in the Reference Settings dialog box, and applied in X, Y and Z directions , which is probably appropriate. Detailed analyses of these types of di e tl geo efe e ed surveys are out of scope of this exercise. In using these camera position data, PhotoScan has detected or assumed that the camera coordinate values are in WGS 84 latitude and longitude.

To avoid conflict with GCP data provided in a different coordinate system, select all the cameras in the Reference pane click on one row in the table, then press Control-A and untick the check boxes. This deselects the camera position data from being used in any further georeferencing calculations. Note: If, in your own surveys, you do t have precisio information to import with GCP coordinates, the precision columns can be omitted and precision estimates set globally within PhotoScan later.

Select the coordinate system and return to the Import CSV dialog box. E su e that Refine marker positions based on image content is he ked. Zoom in to see the cone clearly, right click on the top right corner of the cone base, the sele t Pla e a ke …. A white dot will appear at the point, attached to a green flag, denoting a pinned GCP observation. In the Reference panel, look on the right hand side of the Markers table, and you should see that a number of o se atio s ha e o ee auto ati all ade of that GCP the P oje tio s olu.

If this has remained at 1, find the same GCP in another image e. This time, you will be guided to its location by a striped line, along which PhotoScan is expecting the marker to be located. Find the GCP, and place the marker as before. With multiple observations, PhotoScan has sufficient information to estimate where a marker should be in other images. In the Reference pane, right click on the table entry for gcp, and select Filter Photos by Markers.

The Photos pa e ill o o l list i ages i hi h this GCP is expected to be visible. With the Photos pane showing the image thumbnails, images in which an observation has been manually set pinned will be annotated with a green flag. Images in which the GCP has been identified by automated image matching are annotated with a blue flag, and grey furled flags indicate images in which the GCP is expected, but has not been manually identified or successfully located by image matching.

Grey-flagged positions are not used in georeferencing calculations. Double-click on an image annotated by a grey furled flag in the Photos pane, then drag the marker into the appropriate position in the image if you are confident you know where it should go!

This will pin the observation, as indicated by the green flag annotation in the Photos pane. Poor-quality observations are not usually worth it. Practice this process on two more GCPs before proceeding to Section 4. In the Reference panel, click on the Update button to re- calculate the georeferencing transform.

Thus, update does ot ha ge the shape of the 3-D model, just its size, position and orientation. You ill o see alues appea i the e o olu of the Ma ke s ta le, hi h ep ese t the isfit between the photogrammetric and the control data. If any are substantially larger than expected e. The values you see should be somewhere in the 0. The survey now has a preliminary georeference based on the GCPs identified in the images, and PhotoScan can estimate the positions of the remaining GCPs in images.

For any remaining GCPs with no observations except gcp , use the Filter photos by marker fu tio to e a le ou to lo ate the GCPs in the images.

Note — ignore gcp as it does not relate to a cone location! You might notice that PhotoScan estimated that gcp was rather far from its location in the image and, having pinned the marker, it shows a much greater error than the others.

This suggests that it is not consistent with all the other GCPs. Click the Update button again to re-calculate the transform. Uncheck gcp in the Markers table to remove it from georeferenceing calculations and re-run the update. This straightforward exploration of the error distribution on GCPs helps identify potential problems in the data — here, gcp has been identified as being substantially less consistent with the photogrammetric model than all the other GCPs.

This information gives you the image residuals for all observations of that marker, and enables individual weak observations to be identified. Such observations can either be removed or adjusted by opening the appropriate image in the Image pane. However, the GCPs have not been used to help refine the shape of the model. In PhotoScan, bundle adjustment is carried out via the Opti ize Ca e as utto o the Reference pane toolbar.

Ensuring that all your GCPs are checked active , click the Optimize Cameras button and then, leaving the selection of camera parameters at its default values, click OK.

To change the weightings, in the Reference pane, click on the Settings button, and edit the alues i the Image coordinates accuracy o app op iatel e. Re-run the bundle adjustment, and check that RMS image error values have not changed substantially. Small changes can be used to update the settings values and the adjustment run again, if required.

As you did previously, see how removing gcp from the bundle adjustment by unchecking its box affects the results. Note down the total error values for control and check points. Do you think gcp should be included in the adjustment? If decimetric accuracy or better is required, then appropriate weighting may well be important.

See James et al. These include k radial distortion and p1, p2 tangential distortion. In many cameras, tangential distortion is very small and can often be neglected. O the Adjusted ta , edit the p a d p alues to. However, finding suitable camera placements to capture overlapping images from different perspectives is difficult around stockpiles carried on barges with a narrow space.

Unmanned aerial vehicle UAV -based photogrammetry, which is flexible and low-cost, can work in a close-range domain, can generate high-resolution and dense 3D point clouds, and can be used for the aerial mapping of 3D terrain models [ 12 , 13 , 14 ]; thus, it has been widely accepted for the volume estimation of stockpiles on land [ 15 , 16 , 17 , 18 ].

UAV-based photogrammetry can acquire typically high-resolution aerial stereo images at low-altitude positions and reconstruct the 3D surface of a stockpile [ 17 ]. Previous studies have confirmed the accuracy of 3D modeling using UAV-based photogrammetry [ 19 , 20 ]. High-resolution remote sensing images with a fine ground sampling distance offer an opportunity to describe irregular stockpiles in detail, and they can be used to create precise 3D surface models i.

Several ground control points GCPs are typically measured using a real-time kinematic global positioning system to georeference the UAV-based photogrammetric point clouds. However, compared with the abovementioned methods, which rely on discretely distributed measuring points obtained from GPS or total station instruments for stockpile surface modeling, UAV-based photogrammetry can provide a more accurate solution to estimate the volume of stockpiles.

In addition, UAVs allow surveyors to collect overlapping images far away from stockpiles instead of climbing them. In this manner, surveyors are not exposed to danger during on-site operations. In addition, terrestrial laser scanning TLS has become a popular tool for obtaining the 3D points of a terrain surface [ 21 , 22 ].

TLS-based methods are also widely used to measure the volume of stockpiles because they can rapidly capture dense 3D point clouds for the modeling of irregularly shaped stockpile surfaces [ 23 , 24 ]. However, TLS-based methods still need surveyors to walk around the boundaries of stockpiles at the edge of the vessel or climb up stockpiles to afford full coverage of the surface.

LiDAR sensors with small size and light weight, such as a high-definition HDL E LiDAR sensor, can be mounted on UAV for airborne laser scanning ALS , which is available to collect 3D point clouds when the barges are basically stationary and motionless; otherwise, ALS-based methods cannot be used to reconstruct the surface of stockpiles when barges are moving or shaking.

Meanwhile, a laser instrument is far more expensive than low-cost drones, e. Therefore, UAV-based photogrammetry is more applicable to the volume estimation of stockpiles compared with TLS-based methods. At present, measuring the volume of stockpiles under the circumstance of a moving or shaking barge, i. In other words, compared with similar studies for the absolute orientation in the applications of UAV-based photogrammetry [ 19 , 20 ], UAV imaging and GCP measurement may be needed given the relative motion between the barge and the background.

A stable framework for collecting the GCPs to georeference the generated digital surface model of stockpiles carried in a dynamic environment may not be provided. Therefore, studies on the use of UAV-based photogrammetry for the volume measurement of stockpiles under barge movement have been rarely reported.

Although photogrammetry software, such as Pix4D and Agisoft PhotoScan, can also estimate volume without GCPs [ 25 , 26 ], the cases applied by the software could be only applicable to the volume estimation on land and are quite different with the case of volume estimation of stockpiles carried on barges. Specifically, the stockpile-free surface is typically not a plane but a complex irregular surface, thus measuring volume above a plane using photogrammetry software such as Agisoft PhotoScan is unavailable to estimate the volume of stockpiles carried on barges, and still requires a unified reference to align stockpile-covered and stockpile-free surface models for volume estimation.

On this basis, an accurate and efficient approach using GCP-free UAV photogrammetry is proposed in this study to estimate the volume of a stockpile carried on a barge under a dynamic environment. An indirect absolute orientation based on the geometry of the vessel is used to establish a custom-built framework that can provide a unified reference between stockpile-covered and stockpile-free surface models.

In addition, UAV images cover a large proportion of water, which is typically characterized as weak texture and variable undulation. As a result, the water around a barge becomes meaningless for the surface model of the barge. Particularly, a coarse-to-fine matching strategy is initially used to determine the corresponding points among overlapping images via the scale-invariant feature transform SIFT algorithm [ 27 ] and the subpixel Harris operator [ 28 ].

Then, SfM and semi-global matching SGM algorithms [ 29 ] are used to recover the 3D geometry and generate the dense point clouds of stockpile-covered and stockpile-free surface models. In turn, these dense point clouds are transformed into a custom-built framework using a rotation matrix that consists of tilt and plane rotations. Lastly, the volume of the stockpile is estimated by multiplying the height difference between the stockpile-covered and stockpile-free surface models by the size of the grid that is defined using the resolution of these models.

The main contribution of this study is to propose an approach using GCP-free UAV-based photogrammetry that is particularly suitable to estimate the volume of stockpiles carried on barges in a dynamic environment. In this approach, the adaptive aerial stereo image extraction, which helps to capture sufficient overlaps for the photogrammetric process from UAV video, and simple linear iterative clustering SLIC algorithm, is used to generate a ROI for improving the performance of image matching by excluding water intervention.

In particular, a custom-built framework instead of prerequisite GCPs is defined to provide the alignment between stockpile-covered and stockpile-free surface models. The remainder of this paper is organized as follows: In Section 2 , the two study areas and the materials are introduced.

In Section 3 , the proposed approach using UAV-based photogrammetry is described in detail. In Section 4 , comparative experimental results are presented in combination with detailed analysis and discussion. In Section 5 , the conclusion of this study and possible future works are discussed. The stockpiles consist of sand and gravel Figure 1 c , which are used for the construction of the third runway of the Hong Kong International Airport with a reclamation area over ha.

This project includes land formation, construction of sea embankments outside the land, foundation reinforcement, installation of monitoring and testing equipment, and construction of a drainage system.

Test site downstream of the Zhuhai Bridge, Southern China: a the study area that includes several barges; b the geospatial location described by Google Earth; c on-site several barges; d a tidal change plot in the test site.

The construction area of this project is characterized by a slow rising tide and quick ebb tide, which are caused by the influence of the surrounding topography. Furthermore, the flat tide lasts a long time, and the tidal range is between 0. The water flow velocity in the middle part of the construction area is relatively slow, and the velocity gradually decreases from north to south. Moreover, the water depth of the construction area is shallow in the south and deep in the north. The volume of the reclamation area, which is mainly filled with sand, is approximately 90 million m 3.

Traditionally, as shown in Figure 2 , the stockpile needs to be reshaped into a regular shape, e. The volume of stockpiles carried on barges is usually identified by field measurements using measuring tools, e. However, some problems, such as low accuracy, low efficiency, numerous surveyors needed, large human error, difficulty in monitoring, and easy divergence with the sand supplier, arise when the traditional method is used.

In this case, as shown in Figure 1 c, placing the instruments e. To find an alternative to the traditional manual volume measurement, UAV photogrammetry and laser scanning are compared and evaluated in terms of indicators, such as accuracy, efficiency, cost, and working conditions. Volume measurement using the traditional method: a stockpile carried on a barge; b manual operation for reshaping the surface of the stockpile; c stockpile with a trapezoidal surface through the reshaping of b ; d volume measurement using a tool, e.

The volume of stockpiles carried on barges should be measured at the test site before unloading the stockpiles into the construction area. In this study, the traditional measuring method and laser scanning were compared with the proposed method in June These experiments were performed under good weather conditions, e.

The field measurements include three parts:. It requires four people to perform the task in approximately 2 h. The measuring tape is used to measure the widths and lengths of the top and the bottom. Thus, the volume V stockpile of stockpiles with a regular trapezoid shown in Figure 3 a can be calculated using the following formula:.

However, the trapezoid reshaped through manual operation is seldom a perfectly regular shape, and the error between the calculated result and the real volume of the stockpile cannot be ignored. To calculate the exact volume of the stockpile as accurately as possible, the stockpile is partitioned into several small trapezoids that can be considered for reshaping.

A small trapezoid is shown in Figure 3 b. Stockpile with a regular trapezoid: a model of a stockpile with a regular trapezoid above the flat surface of the vessel; b a small stockpile with a regular trapezoid on a barge. The surveyor carries the sensor on his back and walks along the side of the barge cabin to scan the stockpile-covered and stockpile-free surfaces with 0. The barges should basically be stationary and motionless during laser scanning; otherwise, the measured 3D point clouds become invalid.

In other words, laser scanning cannot be used to reconstruct the surface of stockpiles when barges are moving or shaking. Five GCPs for each of the experimental barges are measured for absolute orientation, and seven GCPs are measured as checkpoints to validate the accuracy of stockpile-covered and stockpile-free surface models. Finally, two GCPs are selected for exhibition in Figure 5. The validity of the GCPs measured is ensured by conducting the field measurement under a windless environment and on a static barge.

Generally, UAV-based stereo remotely sensed images are acquired in autonomous flights with waypoints predefined using the mission planning software package [ 19 , 20 ]. However, this method cannot satisfy the requirement of overlapping images using fixed waypoints when barges are moving or shaking. In this case, such images are extracted from UAV-based videos instead of images to ensure sufficient overlapping. The DJI Mavic Pro maintains the nadir orientation of the consumer-grade camera during video acquisition.

UAV videos are obtained under good weather conditions, e. The flight altitude is set as 35 m above the barge level, and the ground sample distances are 2. The interior orientation parameters of the sensor carried on the DJI Mavic Pro are calculated from several views of a calibration pattern, i. Systematic errors, i. The mean reprojected error of the adjustment is 0. The parameters are optimized through self-calibrating bundle adjustment. Eight views of the 2D chessboard exhibited as examples.

Red circles with a center denote the referenced corners. This study aims to use a workflow for the volume estimation of stockpiles carried on barges using UAV photogrammetry without the assistance of GCPs. The proposed approach, as demonstrated in Figure 7 , includes four stages: 1 Self-adaptive stereo images are extracted to obtain overlapping images from UAV-based video. Workflow of the volume estimation of stockpiles carried on barges using GCP-free unmanned aerial vehicle UAV photogrammetry.

In this study, UAV-based video is captured to ensure sufficient overlap because it can obtain a sequence of frames. On the basis of the variables, i. Ideally, the flight speed is assumed to be a fixed value. The steps are as follows:. The ROI of the barges and the stockpiles is defined to exclude the area of water in all UAV images and suit the volume measurement of the stockpiles carried on barges, thereby improving the accuracy of image matching and accelerating photogrammetry.

In accordance with the clear gap of image color, intensity, and texture between barge and water, image segmentation is used to classify water and non-water regions. Moreover, effective and efficient segmentation is achieved by segmenting the UAV image on top of the pyramid Figure 9 a,b into superpixels Figure 9 c by a simple linear iterative clustering SLIC algorithm, which does not require much computational cost [ 33 ].

Region of interest ROI extraction on the top of the image pyramid by jointly using simple linear iterative clustering SLIC and Sobel algorithms: a UAV image pyramid; b the down-sampled image; c the result of SLIC segmentation in which two red superpixels are selected as seeds; d the gradient information detected using the Sobel algorithm; e the ROI, where blue and yellow denote the regions of water and barge, respectively.

The operation of two adjacent regions R k and R l , which are merged into a new region is defined as:. Generally, UAV images contain a part of water regions on both sides of the barge to ensure the coverage of full sides. Thus, only one strip of overlapping UAV images can cover a barge. In this case, two red superpixels on both sides of the UAV image in Figure 9 c are selected as seeds to trigger superpixel merging.

Then, the ROI is shaped by reversing the water regions using Equation 3. Feature extraction and matching are performed using a sublevel Harris operator S-Harris coupled with the SIFT algorithm, which is the most popular and commonly used method in the field of photogrammetry and computer vision [ 34 , 35 ]. To achieve evenly distributed matches, this study uses a coarse-to-fine matching strategy to find corresponding points between two stereo images under the constraint of the ROI instead of directly matching images using SIFT.

In the coarse-matching stage, ROI-based SIFT feature extraction and matching on the top of the UAV image pyramid are performed to compute the initial relative orientation of two stereo images.

In the fine-matching stage, gridding S-Harris operator [ 28 ] and SIFT descriptor S-Harris-SIFT are jointly used to find the corresponding points along the epipolar lines obtained from the initial relative orientation. Clearly, these stages are implemented to accelerate the efficiency and accuracy of image matching, and especially to obtain evenly distributed matches even in areas with weak texture.

Traditionally, aerial triangulation is assisted by the initial exterior orientation parameters obtained from an airborne GPS and inertial measurement unit. Compared with traditional photogrammetry, the exterior orientation parameters of each frame in the UAV video cannot be captured and cannot be available for aerial triangulation.