Date of Award

Spring 1-1-2012

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Aerospace Engineering Sciences

First Advisor

William Emery

Second Advisor

James Maslanik

Third Advisor

Robert Leben

Fourth Advisor

Cora Randall

Fifth Advisor

Kumar Navulur

Abstract

The ability to automatically generate large-area land-use/land-cover (LU/LC) classification maps from very-high spatial resolution (VHR) satellite data is dependent on two capabilities: (1) the ability to create a data model able to accurately classify satellite data into the appropriate surface types and (2) the ability to apply this model to the multiple images necessary to create a large-area VHR mosaic. This research describes methods for improving these capabilities by leveraging the unique characteristics of VHR in-track and composite multi-angle data. It is shown that new features can be extracted from both in-track and composite multi-angle data in order to improve classification performance. These features encode information extracted from the spatial and spectral variations of the multi-angle data, such as spectral fluctuation with view-angle and pixel height. This additional knowledge provides the capability to both improve image classification performance (29% in the demonstrated experiments) and include urban LU/LC classes, such as bridges, high-volume highways, and parking lots, that are normally difficult to identify in multispectral urban data. Additionally, methods that apply a multispectral classification model across multiple images (model portability) are also explored using the simplifying test cases of in-track and composite multi-angle data. The in-track results show that the portability of a multispectral model can be improved from no portability (losing all classification capability when applying the model across the multi-angle images) to a 10% reduction in kappa coefficient across the sequence of in-track images when physically based image normalization techniques are appropriately applied. The additional noise of seasonality limits the portability performance in the composite multi-angle sequence to an approximate reduction in kappa coefficient of 20% in the best cases.

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