3-D terrain from synthetic aperture radar images

A G  Bors; E R  Hancock; R C  Wilson

3-D terrain from synthetic aperture radar images

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Surface analysis is important for automatic terrain cartography, and for airborne navigation. This paper proposes a new approach to shape-from-shading (SFS) in synthetic aperture radar (SAR) images. The SFS problem is embedded in a Bayesian framework. We maximize the surface orientation probability using SAR image statistics, local smoothing and constraints imposed by object discontinuities. We model the statistics of the SAR image distribution as a product between the Rayleigh and Bessel functions. We derive the optimal edge detector for this distribution. The resulting edges are classified as ridges and ravines according to a statistical test. Afterwards, the edges are used as constraints in the estimation of the surface normals. We propose various smoothing algorithms for the vector field of surface normals using robust statistics and surface curvature consistency. The results provided by these algorithms are compared with those given by local averaging.

Original language	English
Title of host publication	IEEE WORKSHOP ON COMPUTER VISION BEYOND THE VISIBLE SPECTRUM: METHODS AND APPLICATIONS, PROCEEDINGS
Place of Publication	LOS ALAMITOS
Publisher	IEEE Computer Society
Pages	63-72
Number of pages	10
ISBN (Print)	0-7695-0640-2
Publication status	Published - 2000

Keywords

SHAPE
SAR

Cite this

@inproceedings{ae75cfb2adac4e00af6f7713f63ad93f,

title = "3-D terrain from synthetic aperture radar images",

abstract = "Surface analysis is important for automatic terrain cartography, and for airborne navigation. This paper proposes a new approach to shape-from-shading (SFS) in synthetic aperture radar (SAR) images. The SFS problem is embedded in a Bayesian framework. We maximize the surface orientation probability using SAR image statistics, local smoothing and constraints imposed by object discontinuities. We model the statistics of the SAR image distribution as a product between the Rayleigh and Bessel functions. We derive the optimal edge detector for this distribution. The resulting edges are classified as ridges and ravines according to a statistical test. Afterwards, the edges are used as constraints in the estimation of the surface normals. We propose various smoothing algorithms for the vector field of surface normals using robust statistics and surface curvature consistency. The results provided by these algorithms are compared with those given by local averaging.",

keywords = "SHAPE, SAR",

author = "Bors, {A G} and Hancock, {E R} and Wilson, {R C}",

year = "2000",

language = "English",

isbn = "0-7695-0640-2",

pages = "63--72",

booktitle = "IEEE WORKSHOP ON COMPUTER VISION BEYOND THE VISIBLE SPECTRUM: METHODS AND APPLICATIONS, PROCEEDINGS",

publisher = "IEEE Computer Society",

address = "United States",

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TY - GEN

T1 - 3-D terrain from synthetic aperture radar images

AU - Bors, A G

AU - Hancock, E R

AU - Wilson, R C

PY - 2000

Y1 - 2000

N2 - Surface analysis is important for automatic terrain cartography, and for airborne navigation. This paper proposes a new approach to shape-from-shading (SFS) in synthetic aperture radar (SAR) images. The SFS problem is embedded in a Bayesian framework. We maximize the surface orientation probability using SAR image statistics, local smoothing and constraints imposed by object discontinuities. We model the statistics of the SAR image distribution as a product between the Rayleigh and Bessel functions. We derive the optimal edge detector for this distribution. The resulting edges are classified as ridges and ravines according to a statistical test. Afterwards, the edges are used as constraints in the estimation of the surface normals. We propose various smoothing algorithms for the vector field of surface normals using robust statistics and surface curvature consistency. The results provided by these algorithms are compared with those given by local averaging.

AB - Surface analysis is important for automatic terrain cartography, and for airborne navigation. This paper proposes a new approach to shape-from-shading (SFS) in synthetic aperture radar (SAR) images. The SFS problem is embedded in a Bayesian framework. We maximize the surface orientation probability using SAR image statistics, local smoothing and constraints imposed by object discontinuities. We model the statistics of the SAR image distribution as a product between the Rayleigh and Bessel functions. We derive the optimal edge detector for this distribution. The resulting edges are classified as ridges and ravines according to a statistical test. Afterwards, the edges are used as constraints in the estimation of the surface normals. We propose various smoothing algorithms for the vector field of surface normals using robust statistics and surface curvature consistency. The results provided by these algorithms are compared with those given by local averaging.

KW - SHAPE

KW - SAR

M3 - Conference contribution

SN - 0-7695-0640-2

SP - 63

EP - 72

BT - IEEE WORKSHOP ON COMPUTER VISION BEYOND THE VISIBLE SPECTRUM: METHODS AND APPLICATIONS, PROCEEDINGS

PB - IEEE Computer Society

CY - LOS ALAMITOS

ER -