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31

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 ...


19

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 ...


14

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 ...


12

Have you looked at using FWTools? There is a python script called gdal_merge that is available within FWTools. You can use a list as input. The command with usage would be: gdal_merge -o c:\temp\output_image.tif -q -v --optfile c:\temp\rasterlist.txt


11

Save your excel file as CSV file go to Add Vector Layer in QGIS and navigate to your CSV file and load it In the print composer, go to Add attribute table, as you can see below: Select the Source from Layer Feature from the window in the right,as shown below: You can change the font and formats based on your needs, and here is final output: UPDATE In ...


10

GDAL supports .img format, both the basic Imagine and the extended Imagine (greater than 2GB), thus any software that utilizes GDAL drivers would support ERDAS Imagine. The most workable and well documented that I have seen is QGIS. It is also open source and therefore free.


10

NASA's "Black Marble" is not one satellite image, but a mosaic gathered over several weeks and heavily edited: The data was acquired over nine days in April 2012 and thirteen days in October 2012. It took satellite 312 orbits and 2.5 terabytes of data to get a clear shot of every parcel of Earth's land surface and islands. This new data was then mapped ...


9

Posterizing was a great start: it eliminated most of the compression artifacts and simplified the cartography enough to enable additional cleaning. Much of the cleaning of a categorical raster involves so-called "morphological" operations. These include expanding one category into its neighbors, shrinking it back again, and region grouping contiguous mono-...


9

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 ...


7

Under'Options', choose the 'CRS' tab, and see the choices for new layers. Choose either "Use project CRS", or 'Use default CRS displayed below'.


7

If you want to show your geometries as vectors instead of images there are a couple of tricks that you can apply to reduce the load of your page: Use TopoJSON instead of GeoJSON Remove all the attributes that you are not going to use in the applicaation and also the whitspaces. Taking into account your visualization scale, simplify your geometries and ...


7

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 ...


7

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 ...


6

If you're using Python I'd recommend using the GDAL library, which has it's own Python bindings. Assuming you've got both GDAl (see this GIS StackExchange question for details on how to install on windows) and numpy installed, your code could look something like: from osgeo import gdal import numpy as np #Open our original data as read only dataset = gdal....


6

Ok, in the end this script did the job: X1=456 Y1=307 X2=469 Y2=316 Z=9 for x in `seq $X1 $X2`; do for y in `seq $Y1 $Y2`; do echo "Getting ${x},${y}" curl -s http://localhost:20008/tile/SteveCountryVic/${Z}/${x}/${y}.png -o ${Z}_${y}_${x}.png & done wait done montage -mode concatenate -tile "$((X2-X1+1))x" "${Z}_*.png" out_z${Z}_${X1}_${Y1}-${X2}_${Y2}....


6

Try converting your file to another/the same format (Raster/conversion/translate(convert format).There you can define a value for "no data", which you can set to a number different than 0. Hope it helps


6

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)


6

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 <- download.file('http://icons.iconarchive.com/icons/oxygen-icons.org/oxygen/...


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.


6

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 ...


6

As far as I know, only tiled scenes are distributed since october on the ESA hub, so you will need to download all the tiles. Note that there are several alternative websites to download S2 data with more options. Because you are close to France, you could be lucky to have preprocessed images from CESBIO


6

I acknowledge in advance that the following links don't provide you with direct access to the full scenes, but I do think that they are useful resources for searching the Sentinel-2 archive. In particular, the Sentinel-2 on AWS portal and it's associated browser provides you with an efficient viewer. In addition, the above portal, provides you with a ...


6

What you want is the projection, not the coordinate reference system. You need to look at the lines of longitude and latitude and work out from their curvature what possible projections it might be. For example, the latitude lines are curved, which rules out anything like Mercator or Peters projection. Conic projections [https://en.wikipedia.org/wiki/...


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

JAXA have made global L-band SAR mosaics at 25 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.


5

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.


5

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.


5

This is a known issue with ESRI. Their Page suggests these following workarounds (quoted from ESRI): Use one of the following two solutions to solve this issue. It is highly recommended to download and use ArcGIS Pro to perform all printing and exporting functions. ArcGIS Pro is not limited by the graphical device interface (GDI) ...


5

You can use Qgis2threejs plugin in QGIS to view an image in 3D. It has great options and potential capabilities that you can see in the plugin's documentation. Then plugin can be downloaded from QGIS Plugin Manager, and you can refer to the pdf documentation if you stuck somewhere, or on GIS SE to seek a help on a specific issue. Here is a sample output: ...


5

That depends what processing level you are referring to. Most data is delivered as Level-1C. According to the Sentinel-2 documentation: Level-1C product provides orthorectified Top-Of-Atmosphere (TOA) reflectance, with sub-pixel multispectral registration. Cloud and land/water masks are included in the product. Level-0, 1A and 1B are not directly ...


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