Unsupervised Land Use Land Cover

The method uses atmospherically compensated 8-band (optical+NIR) images from the WorldView-2 and WorldView-3 satellites. For each image pixel a similarity index is computed between its spectral signature (radiometric vector data) and entries from a custom spectral-signature library. LULC layers are generated by a hierarchical class assignment algorithm and are post-processed by connected and structural morphological operators for noise reduction. An intelligent blend-in method guarantees class overlap resolution and minor blank spot treatment. 

The LULC method is based on a number of image analysis algorithms that do not require user input for class feature learning (machine learning) and classification. It is fully automated and concludes in < 2min for typical WorldView-2 multi-spectral full extent data sets, i.e. 120km x  20km @ 2m spatial resolution. 

The LULC output contains the following classes: vegetation, bare soil, water, cloud, shadows (major), no-data zones & unclassified (mostly built-up). 

The LULC method is available on the GBDX platform of DigitalGlobe; task name protogenV2LULC.

Gallery (click the images for better resolution)

Alexandria, Egypt

Latakia, Syria

Buenos Aires, Argentina

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The opinions expressed in this article are those of the author and not those of the author's employer and/or volunteer affiliations. The methods, algorithms and instructions are meant for educational purposes only and are not part of or in full, any official product.