Abstract
Automatic learning of geospatial intelligence is challenging due to the complexity of articulating knowledge from visual patterns and to the ever-increasing quantities of image data generated on a daily basis. In this setting, human inspection and annotation is subjective and, more importantly, impractical. In this letter, we propose a knowledge-discovery algorithm that uses content-based methods to link low-level image features with high-level visual semantics in an effort to automate the process of retrieving semantically similar images. Our algorithm represents geospatial images by using a high-dimensional feature vector and generates a set of association rules that correlate semantic terms with visual patterns represented by discrete feature intervals. We also provide a mathematical model to customize the relevance of feature measurements to semantic assignments as well as methods of querying by semantics and by example.
Original language | English (US) |
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Article number | 4914801 |
Pages (from-to) | 38-42 |
Number of pages | 5 |
Journal | IEEE Geoscience and Remote Sensing Letters |
Volume | 7 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2010 |
All Science Journal Classification (ASJC) codes
- Geotechnical Engineering and Engineering Geology
- Electrical and Electronic Engineering