Detection of compound structures using a Gaussian mixture model with spectral and spatial constraints

Çağlar Arı; Selim Aksoy

doi:10.1117/12.919827

24 May 2012 Detection of compound structures using a Gaussian mixture model with spectral and spatial constraints

Çağlar Arı, Selim Aksoy

Proceedings Volume 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII; 83901Y (2012) https://doi.org/10.1117/12.919827
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

High spectral and high spatial resolution images acquired from new generation satellites have enabled new applications. However, the increasing amount of detail in these images also necessitates new algorithms for automatic analysis. This paper describes a new approach to discover compound structures such as different types of residential, commercial, and industrial areas that are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. The proposed approach uses a robust Gaussian mixture model (GMM) where each Gaussian component models the spectral and shape content of a group of pixels corresponding to a primitive object. The algorithm can also incorporate spatial constraints on the layout of the primitive objects in terms of their relative positions. Given example structures of interest, a new learning algorithm fits a GMM to the image data, and this model can be used to detect other similar structures by grouping pixels that have high likelihoods of belonging to the Gaussian object models while satisfying the spatial layout constraints without any requirement for region segmentation. Experiments using WorldView-2 data show that the proposed method can detect high-level structures that cannot be modeled using traditional techniques.

Citation Download Citation

Çağlar Arı and Selim Aksoy "Detection of compound structures using a Gaussian mixture model with spectral and spatial constraints", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901Y (24 May 2012); https://doi.org/10.1117/12.919827

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Expectation maximization algorithms

Data modeling

Detection and tracking algorithms

Image segmentation

RGB color model

Buildings

Roads

Show All Keywords

Keywords/Phrases

Search In:

Publication Years