Abstract

RIT's Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool allows modeling of real world scenes to create synthetic imagery for sensor design and analysis, trade studies, algorithm validation, and training image analysts. To increase model construction speed, and the diversity and size of synthetic scenes which can be generated it is desirable to automatically segment real world imagery into different material types and import a material classmap into DIRSIG.

This work contributes a methodology based on standard texture recognition techniques to supervised classification of material types in oblique aerial imagery. Oblique imagery provides many challenges for texture recognition due to illumination changes with view angle, projective distortions, occlusions and self shadowing. It is shown that features derived from a set of rotationally invariant bandpass filters fused with color channel information can provide supervised classification accuracies up to 70% with minimal training data.

Library of Congress Subject Headings

Remote sensing--Data processing; Image processing--Digital techniques; Visual texture recognition; Classification

Publication Date

10-5-2014

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

David Messinger

Advisor/Committee Member

Harvey Rhody

Advisor/Committee Member

Carl Salvaggio

Comments

Physical copy available from RIT's Wallace Library at G70.4 .H377 2014

Campus

RIT – Main Campus

Plan Codes

IMGS-MS

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