Name
Title | ||
|---|---|---|
An Ann Based Automatic Hyperspectral Image Processing System With Adaptive Dimensionality Reduction |
Abstract | ||
|---|---|---|
This paper describes an artificial neural network based system for classifying the contents of hyperspectral images that is able to automatically reduce the dimensionality of the data provided by the hyperspectrometers without compromising their efficacy. The data reduction is achieved through the adaptation of the window size and the number of parameters that make up the description of the spectral signatures within the window as training progresses. Following this approach, a user just needs to specify the minimum resolution desired on the output or category image and the level of discrimination among categories, and the system will try to meet these requirements by modifying during training the size and number of inputs to the network. When it is not possible to comply with both requirements, the system will provide a compromise solution that minimizes the global discrimination error, which takes into account the spatial discrimination and the discrimination among classes. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596956 | 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 |
Keywords | Field | DocType |
data reduction,neural nets,classification algorithms,pixel,artificial neural network,hyperspectral imaging,artificial neural networks,materials | Dimensionality reduction,Computer science,Artificial intelligence,Artificial neural network,Computer vision,Pattern recognition,Curse of dimensionality,Hyperspectral imaging,Pixel,Statistical classification,Spectral signature,Machine learning,Data reduction | Conference |
ISSN | Citations | PageRank |
1098-7576 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Prieto | 1 | 0 | 0.34 |
| Daniel Souto | 2 | 19 | 4.70 |
| Richard J. Duro | 3 | 571 | 205.27 |
| Fernando López-Peña | 4 | 48 | 9.12 |