C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
COSIT 2001 Proceedings of the International Conference on Spatial Information Theory: Foundations of Geographic Information Science
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Interactive training of advanced classifiers for mining remote sensing image archives
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ADaM: a data mining toolkit for scientists and engineers
Computers & Geosciences
Ontology-based documentation of land degradation assessment from satellite images
International Journal of Remote Sensing
Proceedings of the 13th International Conference on Extending Database Technology
Mining regions of remote sensing images
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
A geographical approach to self-organizing maps algorithm applied to image segmentation
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Retrieving images for remote sensing applications
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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Remote sensing image databases are the fastest growing archives of spatial information. However, we still have a limited capacity for extracting information from large remote sensing image databases. There are currently very few techniques for image data mining and information extraction in large image data sets, and thus we are failing to exploit our large remote sensing data archives. This paper proposes a methodology to provide guidance for mining remote sensing image databases. The basic idea is to use domain concepts to build generic description of patterns in remote sensing images, and then use structural approaches to identify such patterns in images. We illustrate our proposal with a case study for detecting land use patterns in Amazonia from INPE's remote sensing image database.