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I am still waiting on this solution, hoping that it can be resolved soon!
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09-03-2020
09:16 AM
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Hi Philipp, I had the same issue when trying to get an object-detection deep learning algorithm to run. I had a 16-bit unsigned orthomosaic with 5 bands. Following Makiko's advice on this thread, I extracted the first three bands (RGB bands), and then compiled them back into an orthomosaic using the "Composite" tool under "Raster Tools." After this, I exported the raster to my working directory as a TIFF file. I then created my training samples, and exported them as PNG files. I was able to do this with no problem. As I am using object detection, I exported them with the Meta Data Format being "PASCAL Visual Object Classes," and it worked just fine. Hope that helps!
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09-03-2020
08:14 AM
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I have a raster dataset (of a field of crops) with 5 bands that I have classified using Image Segmentation. I'm trying to get the kappa value to investigate the errors of my classification method, but the matrix turns up empty. I started with a raster dataset that I segmented, and from that segmented image, I generated training samples. I then classified the segmented image (using Random Tree) into 5 classes (leaves, grain heads, soil, shadows, and ground targets). The outputs generated from that process included a classified raster and a ECD file. From there, I used the "Create Accuracy Points" to create the points needed to compute the confusion matrix. Parameters used: Input Raster = {The raster I just classified} Target Field = "Classified" Number of Random Points = 500 Sampling strategy = "Stratified Random." I then tried to compute the confusion matrix, but all my results are zeros. I'm not sure why. Is there something that I have to do to merge my accuracy points with the classes of my classified raster
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11-05-2019
03:00 PM
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Hello everyone, I am trying to mask some background in an agricultural research plot using the OTSU algorithm. This creates two separate classes of pixels, which can then be separated. I have read about this in the literature, but I haven't been able to find this feature. Could anyone tell me the steps to execute this algorithm? Thanks in advance!
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08-07-2019
10:31 AM
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Hi Kushendra, I did not get a chance to normalize this data. Since it is not a normalized dataset, I am thinking to just use the data as is. If you absolutely have to normalize it, I would try to extract the data (i.e. using the "zonal statistics as table" tool) and then normalize it in this method: https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range Not claiming to be an expert, but this might help!
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06-10-2019
01:50 PM
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I am beginning some work with LiDAR sensors (mounted on small unmanned aircraft systems), and would like to know if there is a way to transform LAS data to a solid, 3D model that can be used for object-based image classification? Specifically, I am looking at LiDAR sensors for vegetation mapping and would like to be able to classify vegetation within the 3D model. Is there a way to get to this point using ArcGIS Pro? I've used ArcGIS Pro for remote sensing projects, so I'm familiar with the program. Do I need to convert the LAS file to multipoints, and then krig them into a 3D model? Any advice would help, thanks!
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05-23-2019
12:44 PM
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Thank you both for your inputs. I appreciate the help a lot! Thanks for directing me to the Remote Sensing option Dan, I appreciate that!
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04-02-2019
10:03 AM
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Hi everyone, I am trying to calculate my green chlorophyll index (GCI), one of my many vegetation indices that I'm trying to calculate. I am working with imagery from a Micasense RedEdge 3 camera (with proper radiometric calibration procedures already completed). I thought that you are supposed to get data values between -1 and 1, but instead, my values range from 0.09-14.4, as you can see by the image that I have attached. The equation for the GCI, as defined by the literature, is: (NIR/Green) - 1 My dataset bands are defined here, for this example: Green = "band_2" and NIR = "band_5" The equation I used for Raster Calculator is this: Float("band_5") / Float("band_2") - 1 Any help would be greatly appreciated! Thanks guys! (P.S. This is a picture of a grain sorghum research plot)
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04-01-2019
02:58 PM
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Hello everyone!
I am working with multispectral data collected with a MicaSense RedEdge 3, providing me 5 bands to work with (Red, Green, Blue, Rededge, NIR). I am able to generate vegetation indexes (VI) such as the NDRE and the NDVI. Because I am working on an experiment with mapping sorghum vegetation, I created classes (with supervised classification polygons) to define elements such as soil reflectance, dead plants, shadows, grain heads, and plant leaves. created each class to a white color (except the 'plant leaves' classification, which was labeled green for visibility). Note: All multispectral indices were created first, and then the classified image was generated.
You can see in the picture that the only elements I wish to work with are the green sections.
My question is this: How do I extract the VI data for only the 'plant leaves' class?
Any help would be much appreciated. Thanks all!
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03-06-2019
08:00 AM
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