Abstract
An Artificial Neural Network (ANN) for change detection from multi-temporal satellite images, which was reported in [6], has been further developed and tested, as part of a study of an area of high spatio-temporal heterogeneity along a climatic gradient between humid and arid climate regions. Four recognition classes, "positive change", "negative change", "false change", and "no change" were learned by a backpropagation feedforward ANN and then applied to Landsat images that were acquired over the study area in 1992 and 1997. A comparison with existing classification techniques indicates, in many instances, significantly improved performance due to the ANN developed.
| Original language | English |
|---|---|
| Journal | International Geoscience and Remote Sensing Symposium (IGARSS) |
| Volume | 2 |
| State | Published - 1 Jan 2002 |
Fingerprint
Dive into the research topics of 'A neural network-based technique for change detection of linear features and its application to a Mediterranean Region'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver