Extracting spatially and spectrally coherent regions from images

Author

Bandukwala, Farhana

Author_Institution

GXP Commercial Products, BAE Syst., San Diego, CA, USA

fYear

2011

fDate

6-9 June 2011

Firstpage

1

Lastpage

4

Abstract

Extracting spectrally homogeneous regions as features from multispectral raster data has unique challenges when accurate shape preservation is a priority. We tackle this task by representing neighborhoods that contain heterogeneously classified pixels as a graph. We then use graph-cut based combinatorial optimization to eliminate spuriously classified pixels. After the region of interest is uniformly classified, we use a vectorization step to extract it as a feature.

Keywords

feature extraction; geophysical image processing; graph theory; optimisation; feature extraction; graph-cut based combinatorial optimization; heterogeneously classified pixels; multispectral raster data; neighborhood representation; spectrally coherent region extraction; spuriously classified pixels; vectorization step; Classification algorithms; Feature extraction; Joining processes; Measurement; Optimization; Shape; Spatial coherence; Classification; combinatorial optimization; graph-cut; spatial coherence; vectorization;

fLanguage

English

Publisher

ieee

Conference_Titel

Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011 3rd Workshop on

Conference_Location

Lisbon

ISSN

2158-6268

Print_ISBN

978-1-4577-2202-8

Type

conf

DOI

10.1109/WHISPERS.2011.6080963

Filename

6080963