| dc.contributor.author |
Ashish, D |
en |
| dc.contributor.author |
McClendon, RW |
en |
| dc.contributor.author |
Hoogenboom, G |
en |
| dc.date.accessioned |
2014-06-06T06:48:58Z |
|
| dc.date.available |
2014-06-06T06:48:58Z |
|
| dc.date.issued |
2009 |
en |
| dc.identifier.issn |
01431161 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1080/01431160802549187 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/4367 |
|
| dc.subject.other |
Aerial images |
en |
| dc.subject.other |
Agricultural fields |
en |
| dc.subject.other |
Analysis techniques |
en |
| dc.subject.other |
Artificial Neural Network |
en |
| dc.subject.other |
Classification , |
en |
| dc.subject.other |
Colour image |
en |
| dc.subject.other |
Data sets |
en |
| dc.subject.other |
Environmental applications |
en |
| dc.subject.other |
Georgia |
en |
| dc.subject.other |
High resolution |
en |
| dc.subject.other |
Independent model |
en |
| dc.subject.other |
Landuse classifications |
en |
| dc.subject.other |
Model development |
en |
| dc.subject.other |
Multi-spectral |
en |
| dc.subject.other |
Multispectral images |
en |
| dc.subject.other |
Pixel intensities |
en |
| dc.subject.other |
Probabilistic neural networks |
en |
| dc.subject.other |
Remote sensing technology |
en |
| dc.subject.other |
Spatial informations |
en |
| dc.subject.other |
Textural parameters |
en |
| dc.subject.other |
Time interval |
en |
| dc.subject.other |
Backpropagation |
en |
| dc.subject.other |
Edge detection |
en |
| dc.subject.other |
Geographic information systems |
en |
| dc.subject.other |
Land use |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
Pixels |
en |
| dc.subject.other |
Remote sensing |
en |
| dc.subject.other |
Image processing |
en |
| dc.subject.other |
artificial neural network |
en |
| dc.subject.other |
detection method |
en |
| dc.subject.other |
GIS |
en |
| dc.subject.other |
image analysis |
en |
| dc.subject.other |
image classification |
en |
| dc.subject.other |
land use |
en |
| dc.subject.other |
multispectral image |
en |
| dc.subject.other |
probability |
en |
| dc.subject.other |
remote sensing |
en |
| dc.subject.other |
satellite data |
en |
| dc.subject.other |
satellite imagery |
en |
| dc.subject.other |
spectral resolution |
en |
| dc.subject.other |
Georgia |
en |
| dc.subject.other |
North America |
en |
| dc.subject.other |
United States |
en |
| dc.title |
Land-use classification of multispectral aerial images using artificial neural networks |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1080/01431160802549187 |
en |
| heal.publicationDate |
2009 |
en |
| heal.abstract |
During the past decade, there have been significant improvements in remote sensing technologies, which have provided high-resolution data at shorter time intervals. Considerable effort has been directed towards developing new classification strategies for analysing this imagery, but the use of artificial intelligence-based analysis techniques has been somewhat limited. The aim of this study was to develop an artificial neural network (ANN)-based technique for the classification of multispectral aerial images for land use in agricultural and environmental applications. The specific land-use classes included water, forest, and several types of agricultural fields. Multispectral images at a 1-m resolution were obtained for the state of Georgia, USA from a Geographic Information Systems (GIS) data clearinghouse. These false-colour images contained green, red and infrared true-colour information. Three approaches were used for the preparation of the inputs to the ANN. These included histograms of the pixel intensities, textural parameters extracted from the image, and matrices of the pixels for spatial information. A probabilistic neural network was used. Seven hundred images were used for model development and 175 for independent model evaluation. The overall accuracy for the evaluation data set was 74% for the histogram approach, 71% for the spatial approach and 89% for the textural approach. The evaluation of ANNs based on various combinations of all three approaches did not show an improvement in accuracy. We also found that some approaches could be used selectively for certain classes. For example, the textural approach worked best for forest classes. For future studies, edge detection prior to classification, with more careful selection of each class, should be included for land-use classification of multispectral images. |
en |
| heal.journalName |
International Journal of Remote Sensing |
en |
| dc.identifier.issue |
8 |
en |
| dc.identifier.volume |
30 |
en |
| dc.identifier.doi |
10.1080/01431160802549187 |
en |
| dc.identifier.spage |
1989 |
en |
| dc.identifier.epage |
2004 |
en |