Name
Abstract | ||
|---|---|---|
Most existing multitemporal change detection methods use the spectral information alone. However, the inclusion of spatial and temporal information in change detection could improve the accuracy of change detection. This study proposes a new method which includes the multivariate texture in change detection by the direct multitemporal classification. The multivariate texture was extracted from two multispectral images, by the Pseudo Cross Multivariate Variogram (PCMV), which is an extension of the traditional Pseudo Cross Variogram (PCV). The experiments showed that the inclusion of multivariate texture could significantly improve the overall accuracy of change detection, compared to that of using the spectral information alone. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IGARSS.2007.4423202 | IGARSS |
Keywords | Field | DocType |
geophysical techniques,spectral information,multitemporal classification,multitemporal data,pcv,pseudo cross variogram,spatial information,pcmv,multispectral images,multitemporal change detection,pseudo cross multivariate variogram,feature extraction,image classification,change detection,multivariate texture information,multivariate texture,image texture,symmetric matrices,pixel,geographic information systems,geoscience,remote sensing,data mining,radiometry,multispectral imaging | Variogram,Computer vision,Change detection,Pattern recognition,Image texture,Multivariate statistics,Computer science,Remote sensing,Multispectral image,Feature extraction,Artificial intelligence,Contextual image classification | Conference |
Volume | Issue | ISSN |
null | null | 2153-6996 |
ISBN | Citations | PageRank |
978-1-4244-1212-9 | 1 | 0.38 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peijun Li | 1 | 81 | 9.08 |
| Tao Cheng | 2 | 1 | 0.38 |
| Gabriele Moser | 3 | 919 | 76.92 |
| Sebastiano B. Serpico | 4 | 749 | 64.86 |
| Defeng Ma | 5 | 3 | 0.78 |