Moving forward in this mini-series on image processing with the Semi-automatic Classification (SCP) plugin in QGIS, we will see how to carry out the creation of training areas required for supervised image classification.
In addition we will see how to create and edit the spectral signatures and how to preview the result of the classification based on the algorithm and the signatures collected.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been taking stunning photos of the earth’s surface since 1999.
Aster is a joint project between NASA and Japan’s Ministry of Economy, Trade and Industry, and has been taking high resolution images of life on earth for 17 years.
For the first time, the 2.95 million satellite images are now available to the public via the ASTER Gallery Map.
A recently released report by the European Commission’s Joint Research Centre focuses on analyzing how repositories of open source aerial and satellite imagery can be used to help monitor nuclear activity. The report entitled, “Commercial Satellite Imagery as an Evolving Open-Source Verification Technology: Emerging Trends and Their Impact for Nuclear Nonproliferation Analysis” was written by Frank Pabian , the Senior Open-Source Information Research Analyst for Nonproliferation Monitoring and Verification at the European Commission’s Joint Research Center.
The report’s abstract:
One evolving and increasingly important means of verification of a State’s compliance with its international security obligations involves the application of publicly available commercial satellite imagery. The International Atomic Energy Agency (IAEA) views commercial satellite imagery as “a particularly valuable open source of information.” In 2001, the IAEA established an in-house Satellite Imagery Analysis Unit (SIAU) to provide an independent capability for “the exploitation of satellite imagery which involves imagery analysis, including correlation/fusion with other sources (open source, geospatial, and third party). Commercial satellite imagery not only supports onsite inspection planning and verification of declared activities,” but perhaps its most important role is that it also “increases the possibility of detecting proscribed nuclear activities.” Analysis of imagery derived from low-earth-orbiting observation satellites has a long history dating to the early 1906s in the midst of the Cold War era. That experience provides a sound basis for effectively exploiting the flood of now publicly available commercial satellite imagery data that is now within reach of anyone with Internet access. This paper provides insights on the process of imagery analysis, together with the use of modern geospatial tools like Google Earth, and highlights a few of the potential pitfalls that can lead to erroneous analytical conclusions. A number of illustrative exemplar cases are reviewed to illustrate how academic researchers (including those within the European Union’s Joint Research Centre) and others in Non-Governmental Organizations are now applying commercial satellite imagery in combination with other open source information in innovative and effective ways for various verification purposes. The international constellation of civil imaging satellites is rapidly growing larger, thereby improving the temporal resolution (reducing the time between image acquisitions), but the satellites are also significantly improving in capabilities with regard to both spatial and spectral resolutions. The significant increase, in both the volume and type of raw imagery data that these satellites can provide, and the ease of access to it, will likely lead to a concomitant increase in new non-proliferation relevant knowledge as well. Many of these new developments were previously unanticipated, and they have already had profound effects beyond what anyone would have thought possible just a few years ago. Among those include multi-satellite, multi-sensor synergies deriving from the diversity of sensors and satellites now available, which are exemplified in a few case studies. This paper also updates earlier work on the subject by this author and explains how the many recent significant developments in the commercial satellite imaging domain will play an ever increasingly valuable role for open source nuclear nonproliferation monitoring and verification in the future.
A basemap provides a background of geographical context for the content you want to display on a map. When you create a new map, you can choose which basemap you want to use. You can change the basemap of the current map at any time by using the basemap gallery or using your own layer as the basemap. You can also create a basemap containing multiple layers from the Contents pane in the map viewer.
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”.