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Proceedings Paper

Learning to merge: a new tool for interactive mapping
Author(s): Reid B. Porter; Sheng Lundquist; Christy Ruggiero
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Paper Abstract

The task of turning raw imagery into semantically meaningful maps and overlays is a key area of remote sensing activity. Image analysts, in applications ranging from environmental monitoring to intelligence, use imagery to generate and update maps of terrain, vegetation, road networks, buildings and other relevant features. Often these tasks can be cast as a pixel labeling problem, and several interactive pixel labeling tools have been developed. These tools exploit training data, which is generated by analysts using simple and intuitive paint-program annotation tools, in order to tailor the labeling algorithm for the particular dataset and task. In other cases, the task is best cast as a pixel segmentation problem. Interactive pixel segmentation tools have also been developed, but these tools typically do not learn from training data like the pixel labeling tools do. In this paper we investigate tools for interactive pixel segmentation that also learn from user input. The input has the form of segment merging (or grouping). Merging examples are 1) easily obtained from analysts using vector annotation tools, and 2) more challenging to exploit than traditional labels. We outline the key issues in developing these interactive merging tools, and describe their application to remote sensing.

Paper Details

Date Published: 18 May 2013
PDF: 12 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431F (18 May 2013); doi: 10.1117/12.2018093
Show Author Affiliations
Reid B. Porter, Los Alamos National Lab. (United States)
Sheng Lundquist, Los Alamos National Lab. (United States)
Christy Ruggiero, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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