Abstract
In this paper, we describe a general data mining model for investigating remotely sensed imagery data. We use data mining methodologies to automatically identify patterns (or segmentation classes) and their respective attributes within the image, thus enabling a complete and meaningful description of all pixels. The Data Investigation Model for Unsupervised Segmentation (DIMUS) consists of five modules: inspector, clues generator, classifier, justifier, and mapper. Within the framework of this model, various data mining issues, such as data transformation and selection, information description, determination of segmentation classes, knowledge verification and presentation, are addressed. We have applied the model and implemented a fully automated technique that mines remotely sensed images to learn significant classes or patterns through unsupervised clustering. We have tested successfully the technique on a variety of imagery domains.
Original language | English (US) |
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Pages | 798-800 |
Number of pages | 3 |
State | Published - 1998 |
Externally published | Yes |
Event | Proceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5) - Seattle, WA, USA Duration: Jul 6 1998 → Jul 10 1998 |
Other
Other | Proceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5) |
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City | Seattle, WA, USA |
Period | 7/6/98 → 7/10/98 |
ASJC Scopus subject areas
- Computer Science Applications
- General Earth and Planetary Sciences