| タイトル | Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification |
| 著者（英） | Ritter, Niles; Hepner, George F.; Bryant, Nevin; Logan, Thomas |
| 著者所属（英） | Florida State Univ.|Jet Propulsion Lab., California Inst. of Tech. |
| 発行日 | 1990-04-01 |
| 言語 | eng |
| 内容記述 | Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing. |
| NASA分類 | EARTH RESOURCES AND REMOTE SENSING |
| レポートNO | 90A30607 |
| 権利 | Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/356941 |
