Classifying land development in high-resolution panchromatic satellite images using straight-line statistics

We introduce a set of measures based on straight lines to assess land development levels in high-resolution (1 m) panchromatic satellite images. Most urban areas locally (such as in a 400/spl times/400 m/sup 2/ area) exhibit a preponderance of straight-line features, generally appearing in fairly si...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2004-04, Vol.42 (4), p.907-919
Main Authors: Unsalan, C., Boyer, K.L.
Format: Article
Language:English
Subjects:
Applied geophysics
Earth sciences
Earth, ocean, space
Exact sciences and technology
Image analysis
Internal geophysics
Length measurement
Performance analysis
Robustness
Satellites
Spatial coherence
Statistics
Testing
Urban areas
Voting
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Summary:We introduce a set of measures based on straight lines to assess land development levels in high-resolution (1 m) panchromatic satellite images. Most urban areas locally (such as in a 400/spl times/400 m/sup 2/ area) exhibit a preponderance of straight-line features, generally appearing in fairly simple quasi-periodic organizations. Wilderness and rural areas produce line structures in more random spatial arrangements. We use this observation to perform an initial triage on the image to restrict the attention of subsequent more computationally intensive analyses. Statistical measures based on straight lines guide the analysis. We base these measures on length, contrast, orientation, periodicity, and location. On these, we trained and tested parametric and nonparametric classifiers. These tests were for a two-class problem (urban versus rural). However, because our ultimate goal is to extract residential regions, we then extended these ideas to address the detection of suburban regions. To do so, some use of spatial coherence is required; suburban regions are especially difficult to detect. Therefore, we introduce a decision system to perform suburban region classification via an overlapping voting method for consensus discovery. Our data were taken from regions all around the world, which underscores the robustness of our approach. Based on extensive testing, we can report very promising results in distinguishing developed areas.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2003.818835