Spatial data mining: clustering of hot spots and pattern recognition

Abstract

Spatial data mining is the extraction of implicit knowledge, spatial relations or other patterns not explicitly stored in spatial database. The focus of this paper is placed on the information derivation of spatial data. Geographical coordinates of hot spots in forest fire regions, which are extracted from the satellite images, are studied and used in the detection of likely fire points. False alarms can occur in the derived hotspots. While this false information can be identified by comparing the radiance detected at several bands, we introduce a different approach to remove some of the false alarms. We use clustering and a Hough transformation to determine regular patterns in the derived hotspots and classify them as false alarms on the assumption that fires usually do not spread in regular patterns such as in a straight line. This project demonstrates the application of spatial data mining to reduce false alarms from the set of hotspots derived from NOAA images.