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9

You can get Sentinel-1 data from scihub.esa. Requires only registration (And most likely, non-commercial use). As Sentinel-1 has just become operational the archive is not very extensive but should grow quite quickly. You can set request data-access propospal on Alaska Satellite Facility. Some data open access. For ALOS-PALSAR you must be a resident of the ...


6

Panchromatic images are created when the imaging sensor is sensitive to a wide range of wavelengths of light, typically spanning a large part of the visible part of the spectrum. Here is the thing, all imaging sensors need a certain minimum amount of light energy before they can detect a difference in brightness. If the sensor is only sensitive (or is only ...


5

you can either check "update georeferencing" or create a new rectified image using "rectify". These tools are in the drop down menu of th georeferencing toolbar.


5

Only managed to find a couple of sources for SAR images and data: You can download SAR images from here which are mostly focused on ecological sites such as forests: You can download SAR samples from here which contain fairly large datasets (note: the last 4 links at the bottom of the SAR section are dead) Hope this helps.


5

Yes, there is a way to do that. In the symbology palette for the overlay raster, you can select the Display Background Value (R, G, B) _ _ _ as ___ option (see screenshot for a raster I have doing the same thing with a white background. Assuming your background image is truly all white, your values will also be 255, 255, 255 in the boxes. Make sure to select ...


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 looks like GDAL is describing the outer edge of the 'origin pixel' and Arcmap is refering to the center of the origin pixel. If you add half the resolution of a pixel they'll match fine. This definition is often different with different software, it doesnt really matter, though you should know what you're looking at so you can take it into account. One ...


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: ...


3

I guess you have gdal and the bindings installed, and some coding ability, so I'll just provide an outline: import org.gdal.gdal.gdal; import org.gdal.gdal.Band; import org.gdal.gdal.Dataset; ... Dataset dataset = gdal.Open(filename); Band band = dataset.GetRasterBand(1); ... // Do some band operation, like band.ReadRaster() to get the data, whatever you ...


3

You can block out the "white" using a Mask function, through the image analysis window. Change NoData Interpretation to "All", and add values 0 (minimum) and 250 (maximum) to all bands in your raster. As your image may contain "near white" values, you may want to lower the masking threshold to, say, 245. The "white" values will also depend on the pixel depth ...


3

Try converting your color list from RGB format to HSV format and then sort the HSV list. What program did you get the RGB values out of? You might be able to tell it to simply report out HSV values. If you can't get HSV directly from that program, you could convert RGB to HSV here http://www.rapidtables.com/convert/color/rgb-to-hsv.htm Background: RGB ...


3

JAXA have made global L-band SAR mosaics at 50 m spatial resolution available from the PALSAR sensor: http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm Registration is required to download the data.


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

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

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

For getting DSM or a DTM some countrys have thier own DSM for free, for example in Spain is this page www.cnig.es, and you can download for free the DSM and DTM but only for Spain. Search if your country have similar system.


2

Here's a fully reproducible example with output: set.seed(310366) # so we get the same random numbers library(raster) uk = getData("GADM",country="GBR", level=0) bbox(uk) # tells us the bounds (I think it goes as far west as Rockall) # make 200 points over that area: pts = cbind(runif(200,-13,1), runif(200,50,60)) That code has done the basic setup. Now ...


2

Using QGIS, the simpliest way is to load the data as delimited text, select EPSG:4326 WGS84 as CRS and save the result with Project -> Save as image. The Raster -> Conversion -> Rasterize function is a bit more sophisticated, but since your points are close to each other, it needs reprojecting to a projected CRS like UTM zone 10 first to get a ...


2

Since the points don't form a regular grid you could use gdal_rasterize. Set up a VRT header like so: <OGRVRTDataSource> <OGRVRTLayer name="test"> <SrcDataSource>test.csv</SrcDataSource> <GeometryType>wkbPoint</GeometryType> <GeometryField encoding="PointFromColumns" x="longitude" y="latitude" ...


2

rasterImage draws a raster image at a given location and size. Below is a very rough example, which you can hopefully adjust to your needs. (I made up some location points, you would obviously have to use yours.) library(rgdal) library(png) # load icons in PNG format iconfile1 <- ...


2

I do this a lot with historic maps, and began by using @nicksan's method, but had the same issues the OP mentioned. I haven't used the mask method (will try soon) but here's what I do now, if you can deal with not having the red and blue in your overlay: Make sure you have Updated Georeferencing in the georeferencing toolbar and then remove the layer from ...


2

Two of the best commercial high resolution multispectral products available are Worldview-2 and Worldview-3. These sensors are commonly used for natural resources and biodiversity applications. You can learn more about these products here. Another more cost efficient option is to use RapidEye medium resolution imagery (details). Of course, if your budget ...


2

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, ...


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

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

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

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

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 ...


1

Not sure I understand about the cache issue but you can set the export resolution if that's what you mean:


1

The biggest data provider is DigitalGlobe. They also have the arguably best satellite (WorldView-3). You can buy directly from them, or you can go through one of the many resellers. A price of around 16$ per sqkm is usual for 4 spectral bands and 20$ for 8-bands - link to list with prices. A slightly cheaper option is Pleiades and SPOT data from Airbus ...



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