Global Earth Observation and Image Processing

A vast amount of digital satellite and aerial imagery is being acquired by modern Earth Observation sensors every day. The real challenge is to analyse the raw imagery quickly, extract useful, actionable information with higher accuracy, and apply it in real-world decision making and applications.

While there seems no short of image processing techniques and promises, conventional paradigms need to be critically reviewed and a mere data-driven approach is not enough. We develop accessible image analysis software tools and batch processing workflows, and make a contribution to this direction.

Spectral Exploration, Transformation and Discovery

We are developing a set of image spectral analysis routines that are specific to the latest and most popular open imagery sources, including the Copernicus/ESA Sentinel-2 and NASA/USGS Landsat-8 satellite imagery.

The new software – Spectral Discovery for Sentinel-2 Imagery and Spectral Discovery for Landsat-8 Imagery (version 2.0) – provides accessible and highly efficient tools for rapid band combinations, adaptive image stretchingadvanced image pan-sharpening and exploratory image feature extraction. It has been increasingly used by geospatial professionals, environmental scientists and the general public worldwide. (The new software is being promoted by our collaborators at GeoSage and SatelliteImagery360.com; more international collaborators and distributors are welcome.)

An example of rapid image band combinations: Animated view of 336 band combinations with a Landsat-8 scene.

See Features of Interest: From Pixels to Information

We constantly ask the following important questions in image analysis: (1) What are the Features of Interest (FOI) over pixels? (2) How can they be effectively extracted from pixels in an automated digital workflow? (3) Is the classification accuracy acceptable for the applications at hand? (4) Are there any cost-effective alternatives to achieve the same objective? …

The March 2016 flooding in the U.S. South captured by Landsat-8 imagery. An example of automated classification of surface water areas (in light blue). LANDSAT_SCENE_ID = “LC80230382016080LGN00”; DATE_ACQUIRED = 2016-03-20.

See the Invisible: Applications of Short-wave Infrared (SWIR) Bands

Short-wave infrared (SWIR) bands from the latest satellites (e.g. medium-resolution Landsat-8 and Sentinel-2, and high-resolution WorldView-3) are capable of detecting unique surface features invisible to the human eye and dynamic phenomena through heavy smoke.

The May 2016 Fort McMurray Fire (Alberta, Canada) captured by the latest Sentinel-2 satellite imagery. Left: Natural colour imagery showing full smoke; Right: False-colour SWIR imagery. Raw data: download site; Tile name: 12/V/VH; Date: 201605-05.

 Detecting lava flows/heat through smoke using Landsat-8 imagery. Left: Natural-colour image with RGB bands; Right: False-colour image with SWIR bands. Location: Holuhraun Lava Field, Iceland. LANDSAT_SCENE_ID = “LC82170152014249LGN00”; DATE_ACQUIRED = 2014-09-06.

See the Detail: Advanced Image Fusion

After combining the spectral signatures of the multispectral input and the spatial sharpness of the panchromatic input, the best attributes of both inputs, the output imagery greatly assists image interpretation and visualisation.

Pan-sharpening of Landsat-8 imagery from coarse 30m-resolution (Left) to very sharp 15m-resolution (Right). Location: San Francisco; Image source: Landsat-8; LANDSAT_SCENE_ID = “LC80440342013106LGN00”; DATE_ACQUIRED = 2013-04-16.