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.