IF YOU WANT USE Google Earth Engine you Have to Be a TRUSTED TESTER (like me).
GEE is an amazing database of Satellite Images. In this short video, I show HOW TO:
Download Bands of OLI sensor (Landsat 8).
Build virtual raster in Qgis, and save the result as GeoTIF.
In the second part of this video: The resulting Geotif image will be classified semi-automatically with E-cognition.
Foarte interesant tutorialul pentru clasificarea supervizata a imaginilor satelitare.
Ca tot vorbeam mai devreme despre Sentinel. Ca tot e european. De la ESA.
I have also updated the user manual that is available here.
Following the first basic tutorial of this new version.
- Bare soil.
- Short Wavelength Infrared 1;
- Short Wavelength Infrared 2.
2. Set the Input Image in SCP
sample_image.tif. Once selected,
sample_image.tifis set as Input image, the image is displayed in the map and bands are loaded in the Band set.
4-3-2(corresponding to the band numbers in Band set). You can see that image colors in the map change according to the selected bands, and vegetation is highlighted in red (if the item
3-2-1was selected, natural colors would be displayed).
3. Create the Training Input File
training.scp) in order to create the Training input. The path of the file is displayed in Training input. A vector is added to QGIS layers with the same name as the
Training input(in order to prevent data loss, you should not edit this layer using QGIS functions).
4. Create the ROIs
|Class name||Class ID|
TIP : You can draw temporary polygons (the previous one will be overridden) until the shape covers the intended area.
Water; also set C ID = 1 and C Info =
Lake. Now click to save the ROI in the Training input.
TIP : Dist value should be set according to the range of pixel values; in general, increasing this value creates larger ROIs.
Built-up; also set C ID = 2 (it should be already set) and C Info =
Vegetation(red pixels in color composite
RGB=4-3-2) and a ROI for the class
Bare soil(green pixels in color composite
RGB=4-3-2) following the same steps described previously. The following images show a few examples of these classes identified in the map.
5. Create a Classification Preview
In Classification algorithm select the Spectral Angle Mapping Algorithm. In Classification preview set Size = 500; click the button and then left click a point of the image in the map. The classification process should be rapid, and the result is a classified square centered in clicked point.
TIP : When loading a previously saved QGIS project, a message could ask to handle missing layers, which are temporary layers that SCP creates during each session and are deleted afterwards; you can clickCancel and ignore these layers.
In general, it is good to perform a classification preview every time a ROI (or a spectral signature) is added to the ROI Signature list. Therefore, the phases Create the ROIs and Create a Classification Preview should be iterative and concurrent processes.
6. Create the Classification Output
“SASPlanet is a program designed for viewing and downloading satellite maps”
La recomandarea unui prieten, (thx Liviu) 🙂 pun pe blog link catre o mica platforma software care permite vizualizarea si descarcarea imaginilor satelitare din diverse surse.
Marturisesc ca m-am dus initial la linkul dat de google http://sasplanet.software.informer.com/ insa m-am lasat pagubas pentru ca tot ce am facut a fost sa descarc un “software informer”, adica un instrument cu ajutorul caruia imi descarc softul, etc. etc. etc. dar nu a produs decat un uninstall scurt. Asa ca am sapat si am gasit tool-ul aici: https://www.openhub.net/p/sasplanet motiv pentru care va recomand acest link. In dreapta: download, si veti descarca o arhiva de vreo 12.5 mb, o chestie “portabila”, care dupa dezarhivare, va fi “ready to run”.
Cam asa arata:
SASPlanet is a program designed for viewing and downloading high-resolution satellite imagery and conventional maps submitted by such services as Google Maps, DigitalGlobe, Kosmosnimki, Yandex.Maps, Yahoo! Maps, VirtualEarth, Gurtam, OpenStreetMap, eAtlas, Genshtab maps, iPhone maps, Navitel maps, Bings Maps (Bird’s Eye) etc., but in contrast to all these services all downloaded images will remain on your computer and you will be able to view them, even without connecting to the internet. In addition to the satellite-based maps you can work with the political landscape, combined maps and maps of the Moon and Mars.
Cu alte cuvinte, putem vedea si descarca (offline), chiar si hartile de pe Luna sau Marte. Wow, am zis, interesant. Si culmea, chiar functioneaza.
Creditele, desigur, merg la gagiul asta, Viktor Demydov:
Un scurt tutorial, care va duce si la alte tutoriale, gasiti aici:
Nu ma intru in alte detalii, pentru ca treaba e destul de “self-explanatory”.
Pentru cine e interesat de imagini satelitare Landsat 8.\
Users can search for Landsat 8 imagery by geographic location or scene ID. Imagery results are then easily browsable. The results can be further filtered based on date of image acquisition, cloudiness and sun elevation. The web browser is built using the open data archive Landsat on AWS (more: Landsat 8 satellite imagery available for free via Amazon Web Services). Built using MapBox’s mapping platform and loaded with base layers from OpenStreetMap, the map browser also uses tile technology to rapidly render imagery scenes. This technology also makes viewing data on tablets and smartphones possible.
Once the desired scene is selected, the user can then selected which bands to view. EOS DA has developed technology that transforms the raw satellite imagery data stored in 16-bit GeoTIFF format on-the-fly into the displayed images.
Exploring the different bands available makes for some interesting views. For example, the North Sea’s water surface looks like a deep universe with a myriad of stars pan sharpen RGB:
This view of a valley in Kazakhstan looks like a painting:
Explore the Landsat 8 viewer and be sure to submit any feedback you have on the experience by clicking the submit feedback button located in the lower right hand column (right above the Twitter logo).
Visit: Landsat 8 Viewer