Name
Title | ||
|---|---|---|
ARTMAP neural networks for information fusion and data mining: map production and target recognition methodologies. |
Abstract | ||
|---|---|---|
The Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial images. The Lincoln Lab system has been successfully fielded, but is limited to target/non-target identifications and does not produce whole maps. Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of canonical algorithms and a benchmark testbed has enabled the evaluation of candidate recognition networks as well as pre- and post-processing and feature selection options. The resulting mapping methodology sets a standard for a variety of spatial data mining tasks. In particular, training pixels are drawn from a region that is spatially distinct from the mapped region, which could feature an output class mix that is substantially different from that of the training set. The system recognition component, default ARTMAP, with its fully specified set of canonical parameter values, has become the a priori system of choice among this family of neural networks for a wide variety of applications. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S0893-6080(03)00007-8 | Neural Networks |
Keywords | Field | DocType |
new spatial data mining,artmap neural network,artmap,remote sensing,recognition engine,pattern recognition,resulting mapping methodology,candidate recognition network,hierarchical system,information fusion,system recognition component,mapping,target recognition methodology,mapping method,lincoln lab system,image analysis,map production,data mining,spatial data mining task,adaptive resonance theory,feature selection,neural network | Hierarchical control system,Geospatial analysis,Adaptive resonance theory,Data mining,Feature selection,A priori and a posteriori,Sensor fusion,Artificial intelligence,Pixel,Artificial neural network,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 7 | 0893-6080 |
Citations | PageRank | References |
33 | 1.79 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olga Parsons | 1 | 33 | 1.79 |
| Gail A. Carpenter | 2 | 2909 | 760.83 |