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Point cloud/image comparison?


Processing MOD35 cloud mask image?3d model to point cloudGeoReference XYZ point cloudThinning large LiDAR point cloud?Resampling point cloud data using ArcGIS Desktop?Reduce point cloud elevation dataSeeking open source point cloud software?Import point cloud (.ply) into PostgreSQL ( PostGIS )Georeferencing terrestrial LiDAR point cloud?Vertical 3D Point Cloud visualisation













-1















I used MATLAB software to classify point cloud data into 3 classes; (1) main; (2) protrusion and (3) recess. In MATLAB I assigned a distinctive color to each class to differentiate between classes (yellow, red and blue; but black is not among classification color and it is only background color which is the places which no point exist and therefore, no class was assigned). When the classified point cloud displayed, I took a screen shot of it (TIFF images attached). I wish if I could attach point clouds (txt format), but apparently it is not possible.



In my MATLAB code, there are several parameters to adjust to get the optimum classification results (presented in three colors in reference image and reference point cloud). If I change those parameters from optimum values to other values, then the classification results are changed.



Now, if I assume the reference figure (or reference point cloud) is the base for comparison, I need to calculate how much accurate the other three figures have been classified?



For example, if I say %30 of whole reference image is in red (openings), then how much is this red percentage in any of the other three figures?



Similarly for blue and yellow color percentage. This percentage or any other quantitative comparison. I have been trying to find out a way to compare the accuracy of classification either using MATLAB or ARC GIS/MAPS, ENVI or any other tools, but so far no luck.










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    -1















    I used MATLAB software to classify point cloud data into 3 classes; (1) main; (2) protrusion and (3) recess. In MATLAB I assigned a distinctive color to each class to differentiate between classes (yellow, red and blue; but black is not among classification color and it is only background color which is the places which no point exist and therefore, no class was assigned). When the classified point cloud displayed, I took a screen shot of it (TIFF images attached). I wish if I could attach point clouds (txt format), but apparently it is not possible.



    In my MATLAB code, there are several parameters to adjust to get the optimum classification results (presented in three colors in reference image and reference point cloud). If I change those parameters from optimum values to other values, then the classification results are changed.



    Now, if I assume the reference figure (or reference point cloud) is the base for comparison, I need to calculate how much accurate the other three figures have been classified?



    For example, if I say %30 of whole reference image is in red (openings), then how much is this red percentage in any of the other three figures?



    Similarly for blue and yellow color percentage. This percentage or any other quantitative comparison. I have been trying to find out a way to compare the accuracy of classification either using MATLAB or ARC GIS/MAPS, ENVI or any other tools, but so far no luck.










    share|improve this question









    New contributor




    fifi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.























      -1












      -1








      -1








      I used MATLAB software to classify point cloud data into 3 classes; (1) main; (2) protrusion and (3) recess. In MATLAB I assigned a distinctive color to each class to differentiate between classes (yellow, red and blue; but black is not among classification color and it is only background color which is the places which no point exist and therefore, no class was assigned). When the classified point cloud displayed, I took a screen shot of it (TIFF images attached). I wish if I could attach point clouds (txt format), but apparently it is not possible.



      In my MATLAB code, there are several parameters to adjust to get the optimum classification results (presented in three colors in reference image and reference point cloud). If I change those parameters from optimum values to other values, then the classification results are changed.



      Now, if I assume the reference figure (or reference point cloud) is the base for comparison, I need to calculate how much accurate the other three figures have been classified?



      For example, if I say %30 of whole reference image is in red (openings), then how much is this red percentage in any of the other three figures?



      Similarly for blue and yellow color percentage. This percentage or any other quantitative comparison. I have been trying to find out a way to compare the accuracy of classification either using MATLAB or ARC GIS/MAPS, ENVI or any other tools, but so far no luck.










      share|improve this question









      New contributor




      fifi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.












      I used MATLAB software to classify point cloud data into 3 classes; (1) main; (2) protrusion and (3) recess. In MATLAB I assigned a distinctive color to each class to differentiate between classes (yellow, red and blue; but black is not among classification color and it is only background color which is the places which no point exist and therefore, no class was assigned). When the classified point cloud displayed, I took a screen shot of it (TIFF images attached). I wish if I could attach point clouds (txt format), but apparently it is not possible.



      In my MATLAB code, there are several parameters to adjust to get the optimum classification results (presented in three colors in reference image and reference point cloud). If I change those parameters from optimum values to other values, then the classification results are changed.



      Now, if I assume the reference figure (or reference point cloud) is the base for comparison, I need to calculate how much accurate the other three figures have been classified?



      For example, if I say %30 of whole reference image is in red (openings), then how much is this red percentage in any of the other three figures?



      Similarly for blue and yellow color percentage. This percentage or any other quantitative comparison. I have been trying to find out a way to compare the accuracy of classification either using MATLAB or ARC GIS/MAPS, ENVI or any other tools, but so far no luck.







      image point-cloud






      share|improve this question









      New contributor




      fifi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









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      Check out our Code of Conduct.









      share|improve this question




      share|improve this question








      edited 4 hours ago









      PolyGeo

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      asked yesterday









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          If I undestood correctly, what you are looking for is called an "accuracy assessment". This can be done in most GIS Software, most of the time it's to calculate the accuracy of raster classifications.



          The output of an accuracy assessment is a "confusion matrix", which basically tells you, how accurate the classification of each class was. So if you got 2 classes red and yellow, it tells you how many "red" where correctly classified as "red", how many "yellow" where falsely classified as "red", and how many "red" where falsely classified as "yellow" (and so on).



          Here is the implementation in ArcGIS and more information about the confusion matrix, it basically works the same in every GIS that can handle raster data.






          share|improve this answer








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            0














            If I undestood correctly, what you are looking for is called an "accuracy assessment". This can be done in most GIS Software, most of the time it's to calculate the accuracy of raster classifications.



            The output of an accuracy assessment is a "confusion matrix", which basically tells you, how accurate the classification of each class was. So if you got 2 classes red and yellow, it tells you how many "red" where correctly classified as "red", how many "yellow" where falsely classified as "red", and how many "red" where falsely classified as "yellow" (and so on).



            Here is the implementation in ArcGIS and more information about the confusion matrix, it basically works the same in every GIS that can handle raster data.






            share|improve this answer








            New contributor




            Rob is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
            Check out our Code of Conduct.

























              0














              If I undestood correctly, what you are looking for is called an "accuracy assessment". This can be done in most GIS Software, most of the time it's to calculate the accuracy of raster classifications.



              The output of an accuracy assessment is a "confusion matrix", which basically tells you, how accurate the classification of each class was. So if you got 2 classes red and yellow, it tells you how many "red" where correctly classified as "red", how many "yellow" where falsely classified as "red", and how many "red" where falsely classified as "yellow" (and so on).



              Here is the implementation in ArcGIS and more information about the confusion matrix, it basically works the same in every GIS that can handle raster data.






              share|improve this answer








              New contributor




              Rob is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.























                0












                0








                0







                If I undestood correctly, what you are looking for is called an "accuracy assessment". This can be done in most GIS Software, most of the time it's to calculate the accuracy of raster classifications.



                The output of an accuracy assessment is a "confusion matrix", which basically tells you, how accurate the classification of each class was. So if you got 2 classes red and yellow, it tells you how many "red" where correctly classified as "red", how many "yellow" where falsely classified as "red", and how many "red" where falsely classified as "yellow" (and so on).



                Here is the implementation in ArcGIS and more information about the confusion matrix, it basically works the same in every GIS that can handle raster data.






                share|improve this answer








                New contributor




                Rob is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.










                If I undestood correctly, what you are looking for is called an "accuracy assessment". This can be done in most GIS Software, most of the time it's to calculate the accuracy of raster classifications.



                The output of an accuracy assessment is a "confusion matrix", which basically tells you, how accurate the classification of each class was. So if you got 2 classes red and yellow, it tells you how many "red" where correctly classified as "red", how many "yellow" where falsely classified as "red", and how many "red" where falsely classified as "yellow" (and so on).



                Here is the implementation in ArcGIS and more information about the confusion matrix, it basically works the same in every GIS that can handle raster data.







                share|improve this answer








                New contributor




                Rob is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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