EO signal models & feature extractionmquartulli@vicomtech.org

Just an entree…

• Objective: getting the discussion started

• Because of this, presented in possibly more controversial / provocative terms than I really consider the topic to be

• More in–depth lesson by Mihai Datcu in the next days

Approaches to visionD. Marr 1982 “Vision”, Freeman NY:

- Feature-based

- Bottom-up

- Building representations out of signals

D.Ballard 1991 “Animate Vision” in “Artificial intelligence” 48, pp. 57-86

- Objective-based

- Well-posed “localized” problem

- Makes use of prior information

Mining as fusion

Approaches for IE

SRTM Baltimore, 25m

RSAT LasVegas, 15m Intermap Maastricht, 0.5m

XSAR Bern, 30m

Primitive features for optical dataA diverse gamut of content-based primitive feature extractors:

* pixel-based descriptors — colorimetry in multiple spaces

* texture [Franklin et al. Computers & Geosciences 1996]

* morphology, shapes [Benediktsson et al. IEEE TGRS 2003]

* object-oriented descriptors [Blaschke et al. ISPRS JPRS 2010]

* combinations thereof [Blume et al. AeroSense'97]

OPT: A 2012 “golden standard”For metric resolution classification [Chauffert IGARSS 2012]:

• HSV color-based descriptor

• Histograms of Oriented Gradients (HOG) [Pohl et al. IJRS 1998]

• Local Binary Patterns [Molinier IEEE TGRS 2007]

• Line Segment Detector [Molinier IEEE TGRS 2007]

• edge density [Inglada IGARSS 2012]

• SIFT [Perrotton Computer Vision Systems 2008]

Extensions• Morphological image analysis models [Soille1999]

• Synthetic Aperture Radar:

• Physical simulation models [Franceschetti2003]

• Bayesian structural models [Quartulli2004]

Bayesian model-based IE

– Existing models or explicit assumptions for p(S) – Hierarchies of models

( )( )Dp

SpSDpDpDSpDSp )()|()(),(| ==

Updated description

Existing description

New information

SAR: Bayesian information extractionGMRF +

Space Variant Γ(.) Likelihood

Backscatter intensity

Despeckled

backscatter

intensity

Backscatter

Intensity texture

normSRTM Baltimore, 25m

DEM: Bayesian information extractionGMRF + Space Variant Gaussian Likelihood

DEM elevation map

Elevation

texture

norm

Clean

elevation

map

SRTM Baltimore, 25m

Feature fusion for building reconstructionby resolving high-resolution detail

Baltimore, USA