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Application of OBIA to marine datasets Dr Markus Diesing
2nd May 2016 GeoHab workshop, Winchester, UK
What is OBIA? Object-Based Image Analysis
• Widely applied method in remote-sensing, material sciences and biomedical
imaging (GEOBIA vs OBIA)
• “Sub-discipline of GIScience devoted to partitioning remote sensing imagery into
meaningful objects, and assessing their characteristics through spatial, spectral
and temporal scale.” (Hay and Castilla, 2006).
• Consists of Segmentation of an image into image objects and
Classification based on object features
A few definitions Image Objects:
• Group of contiguous pixels in a map
• Regions which are generated by one or more criteria of homogeneity in one or more dimensions of a feature space
• Building blocks of OBIA
Features:
• Predictor variables derived from remote-sensing data
Why OBIA?
• Less computationally intensive (thousands of objects versus millions of pixels)
• Image objects exhibit useful features (e.g. shape, texture, context) that pixels lack
• Repeatable and applies classification rules systematically
• Easily integrated into GIS
Hay and Castilla (2006)
Why objects: What’s wrong with pixels?
Blaschke and Strobl (2002) Blaschke (2010) ISPRS J Photogramm, 65, 2-16
L-resolution H-Resolution
Beyond pixels
• Additional spectral information (mean, standard deviation,
skewness etc.)
• Geometry (shape, extent, position etc.)
• Texture
• Context (to neighbouring objects, sub-objects, super-objects)
• Hierarchy
• Many more…
Context is important
Not viewing pixels in isolation
Geological context from ancillary data (e.g. existing maps)
• Diamicton unlikely to occur beyond the terminal moraine
• Slide fans are related to marine canyons
Context in seabed mapping
MAREANO
Hierarchy
Burnett and Blaschke (2003) Ecol Model, 168, 233–249
Benz et al. (2004) ISPRS J Photogramm, 58, 239- 258
Hierarchy: bedforms
Data source: Defra DEM
Hierarchy and context Each object knows its ...
Neighbour objects
Sub-objects
Super-object
OBIA workflow
Export Data Input
Classification
Segmentation
Segmentation
Creation or modification of image objects is called segmentation.
Several segmentation algorithms are available:.
Object features
Classification B) Classifier
NN, kNN, SVM, DT, RF, Bayes
Based on samples
Combination of both A) and B)
A) Knowledge based classification
using conditions and functions
Formulation of expert knowledge
Marine OBIA (MOBIA?)
• OBIA on optical remote sensing data (intertidal, shallow subtidal)
• OBIA on seabed stills images/photo mosaics
• OBIA on acoustic remote sensing data (multibeam echosounder)
Lacharite et al. (2015)
Acoustic data: differences to optical data
Main limitation: only one band (backscatter) plus ancillary data (DEM)
No spectral signatures, no band ratios, band differences, band products
Backscatter: Vesterdjupet example
Software options (non exhaustive)
• eCognition (Trimble)
• Interimage
• Orfeo ToolBox
• RSGISLIB (Peter Bunting, Univ. Aberystwyth)
• RSOBIA toolbar for ArcGIS (Tim LeBas, NOCS)
• Others…
Main points
• OBIA advantageous when image resolution is high relative to objects of interest
• Beyond pixels: provides more features that can be exploited for classification
• Offers possibilities where spectral properties are not unique, but shape and neighbourhood
relations are distinct
• Allows to incorporate expert knowledge and contextual information
• Repeatable and transparent approach
• Need to keep limitations of acoustic data in mind
Key publications Terrestrial
Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M. 2004. Multi-resolution, object-
oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry
and Remote Sensing, 58: 239–258. http://www.sciencedirect.com/science/article/pii/S0924271603000601.
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and
Remote Sensing, 65: 2–16. http://www.sciencedirect.com/science/article/pii/S0924271609000884.
Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Queiroz Feitosa, R., et al. 2014.
Geographic Object-Based Image Analysis - Towards a new paradigm. ISPRS Journal of Photogrammetry and
Remote Sensing, 87: 180–191. http://www.sciencedirect.com/science/article/pii/S0924271613002220.
Burnett, C., and Blaschke, T. 2003. A multi-scale segmentation/object relationship modelling methodology for
landscape analysis. Ecological Modelling, 168: 233–249.
http://www.sciencedirect.com/science/article/pii/S030438000300139X.
Key publications Marine
Diesing, M., Green, S. L., Stephens, D., Lark, R. M., Stewart, H. A., and Dove, D. 2014. Mapping seabed sediments:
Comparison of manual, geostatistical, object-based image analysis and machine learning approaches. Continental Shelf
Research, 84: 107–119. http://www.sciencedirect.com/science/article/pii/S0278434314001629.
Lacharité, M., Metaxas, A., and Lawton, P. 2015. Using object-based image analysis to determine seafloor fine-scale
features and complexity. Limnology and Oceanography: Methods, 13: 553–567. http://doi.wiley.com/10.1002/lom3.10047.
Lucieer, V., Hill, N. A., Barrett, N. S., and Nichol, S. 2013. Do marine substrates ‘look’ and ‘sound’ the same? Supervised
classification of multibeam acoustic data using autonomous underwater vehicle images. Estuarine, Coastal and Shelf
Science, 117: 94–106. http://www.sciencedirect.com/science/article/pii/S0272771412004246.
Lucieer, V. L. 2008. Object-oriented classification of sidescan sonar data for mapping benthic marine habitats. International
Journal of Remote Sensing, 29: 905–921. http://www.informaworld.com/10.1080/01431160701311309.
Lucieer, V., and Lamarche, G. 2011. Unsupervised fuzzy classification and object-based image analysis of multibeam data to
map deep water substrates, Cook Strait, New Zealand. Continental Shelf Research, 31: 1236–1247.