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6

Your understanding is correct. Obviously, you aren't limited to just two points in time, but that is only a minor variation. Multi temporal information is generally used for change detection, but it also provides a good tool to take phenological information into account when doing vegetation classification.


5

I hacked together a solution for this and wrote a blog article a while back on a very similar topic, which I will summarize here. The script is intended to extract a river from a 4-band NAIP image using an image segmentation and classification approach. Convert image to a numpy array Perform a quick shift segmentation (Image 2) Convert segments to raster ...


4

It's normal that azimuth and range resolution of SAR sensors differ, because they depend on different variables: The azimuth resolution (AR) of a SAR system is: AR=Length_of_antenna/2 The slant range resolution (SRR) of a SAR system is: SRR=(Speed_of_light*pulse_length)/2 The ground range resolution (GRR) of a SAR system is: ...


4

Otsu's method does not really care about actual values since it tries to minimize the total variance within classes while maximizing the distance between the classes. So, you could just run Otsu on your original data (no need to rescale) and it will provide you with the optimal threshold to use to achieve the goal listed above. I don't know what is your ...


3

RRD (reduced resolution dataset) files are no longer supported by ArcMap, since version 10.1 I believe. By the looks of that, you have an RRD, which is a pyramid set based on an original raster, but no the original data. ArcMap now uses a different type of pyramid with a .tif.ovr file extension. You should be looking for an associated jpeg, the original ...


3

1. where should I begin? Do you know what Image Classification is? If not here's an intro article ESRI wrote about for arcgis. You don' need arcgis to read it. Read it, and in the end you'll understand what you should need. Keep in mind that image classification is about creating classes. To do that should well defined classes beforehand (how many, ...


3

You could look at clustering in scikit-learn. You will need to read the data into numpy arrays (I'd suggest rasterio) and from there you can manipulate the data so that each band is a variable for classification. For example, assuming you have the three bands read into python as red, green, and blue numpy arrays: import numpy as np import sklearn.cluster ...


2

Your last attempt looks very promising. With more than 5 points you might get an even better picture. I use this transformation settings: Using as many border points as possible for georeferencing, I take the coordinates from the map canvas with the middle icon: and get this picture (with clipping to GADM borders):


2

When clipping the image it is likely that you removed the edge of the image. The edge of Landsat TM imagery is assigned 0 in all bands. This will result in 0 no longer being the minimum and a significant increase in the mean value across the raster. Furthermore, I would assume that you have also clipped the image to no longer include clouds, which would ...


2

Using OpenStreetMap you can compute the building height through some tags on buidings. As you can see there is an extrusion in Bucharest (and whole world) in this 3D simulation: http://demo.f4map.com/#lat=44.4379244&lon=26.1004697&zoom=18. Note that the accuracy of the height of buildings is random and some buildings does not have height relative ...


2

Sentinel-1 data is published as Open Data, with attribution, see licence here. Registration required. An API is provided. See Scientific Data Hub for details. Software is also provided to process data.


2

Here's a fuller answer about the synthetic aperture radar (SAR) data available from the Alaska Satellite Facility at no cost to users. The datasets include Seasat (1978 data newly processed in 2013), InSAR, PALSAR (including radiometrically terrain-corrected products), RADARSAT-1, ERS-1, ERS-2, JERS-1, UAVSAR, AirMOSS, AirSAR, and more. SMAP data will ...


2

Yes, DXF supports embedded raster images. It saves them as BMP-type bitmaps, so no compression, mucho space bloat. They use type 310 tags, followed by the hex value of the raw image. I haven't figured out all the details. To see how it's done, create an AutoCAD drawing with an embedded image, as described in one of these sites: ...


2

We recently stubled across this issue as well and it is documented here: The merged LiDAR shows the trouble you report. The reason is that one flightline is much brighter than the other flightline so that the LiDAR points cannot simply be merged and have their intensity processed together. In the same flightline you also notice the negative effects of clouds ...


2

This effect could be a consequence of having different point densities within the flight line overlap regions. A possible solution would be to homogenize the LiDAR cloud. With Fusion the command line to accomplish such task is ThinData: ThinData allows you to thin LIDAR data to specific pulse densities. This capability is useful when comparing analysis ...


2

Yes, you can use the acquisition_date in the metadata, which is the date in ISO format (YYYY-MM-DD). The date in the filename is Year+Julian Day. You can check the acquisition date against the filename if you wish using the NASA Julian Day Calendar This shows 2014-08-10 = 2014222


2

Make sure you're using a 64 bit build of QGIS. The limitation on exported composer sizes/DPI is much higher on a 64 bit build.


2

NDVI is defined for any two bands with near-infrared and infrared data (it is an empirical remote sensing index). As such, you can calculate it straight from the DNs. This is mostly OK if you are only classifying or analyzing vegetation on a single image without significant atmospheric effects (cirrus clouds...) However, if you are performing change ...


2

Use Raster Layer Statistics. It gives you. Statistics [html] Analysis results in HTML format. Minimum value [number] Minimum cell value. Maximum value [number] Maximum cell value. Sum [number] Sum of all cells values. Mean value [number] Mean cell value. Valid cells count [number] Number of cell with data. No-data ...


2

Don't use the OpenLayers plugin. Use the QuickMapServices plugin instead, as that works correctly with the map composer.


2

Pulic accounts do not support file upload. From the doc page https://doc.arcgis.com/en/arcgis-online/share-maps/add-items.htm " In addition, organizational accounts can add image files and use the URL to show images in web apps, pop-ups, and so on. You need to share the CSV and image files with everyone (public) to see the URL in the item details page. "


1

Your HTML page is on a server (even if it's on your desktop) ? If not, you'll have to host it somewhere Tumblr/any other blog can reach. Do not consider hosting it on your PC tho as you'll have to expose it publicly. Once it has been done, just embeds an iframe in your blog page that will point to your leaflet HTML file. It will do it. Br.


1

Get the source of your image and asign the listeners: var lyrSource = layerPrec.getSource(); lyrSource.on('imageloadstart', function(event) { console.log('imageloadstart event fired'); }); lyrSource.on('imageloadend', function(event) { console.log('imageloadend event fired'); }); lyrSource.on('imageloaderror', ...


1

Right click the broken image and select properties. That should tell you the path where the webmap is looking for the image. Either copy your image to that location, or change the feature property in QGIS to the location of the image.


1

You can use unsupervised classification methods; But it is too much hassle to recommend it


1

Option 1 Just specify an attribution for any of your layers that you have added to the map, pointing the image file you want to overlay. In your constructor for the layer: 'attribution': "<img src='myimage.jpg'/>" You can then adjust where that is displayed by messing with the div.olControlAttribution CSS: div.olControlAttribution { ...


1

You're going to be doing accuracy assessment. You want to bring the data for validation (Google Earth) into ArcMap. Then you can "truth" the classification for each of your random points in a shapefile. Afterwards you can sample those same points in your classified image and compute your error matrix.


1

Try the print widget in the ArcGIS Javascript library. https://developers.arcgis.com/javascript/jssamples/widget_print.html Sorry this wont work if you are not using Server 10.1 or greater


1

As Luigi anwered: self.canvas.scene().addItem(...) adds image to the canvas. Thanks!


1

a QgsMapCanvas is a QGraphicsView so you can add graphics items as usual in the scene of this class QGraphicsScene regards



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