Name
Abstract | ||
|---|---|---|
In the last decades the number of natural and anthropic changes affecting population worldwide has raised dramatically. This fact, coupled with the increasing world population living in urban areas, requires the development of a detailed and reliable map of global urban extent. This letter reports on a new approach for urban mapping from Landsat images, based on the Normalized Difference Spectral Vector (NDSV). This spectral transformation allows the creation of a normalized signature that becomes peculiar of each land cover class within the scene. The urban extent classification is obtained by analyzing the NDSV data in conjunction with a Spectral Angle Mapper (SAM) based classifier. The experiments presented in this letter show the effectiveness of the proposed technique in detecting urban areas in extremely different environments. The results of the proposed methodology have been compared with the ones obtained by classifying the NDSV using other classifiers [namely, maximum likehood (ML) and support vector machines (SVM)], and also to the results obtained by classifying the calibrated data using the ML, SVM and SAM classifiers. The NDSV+SAM approach has provided the best results, with an overall accuracy of 97%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LGRS.2013.2274327 | Geoscience and Remote Sensing Letters, IEEE |
Keywords | Field | DocType |
geophysical image processing,image classification,remote sensing,terrain mapping,Landsat images,NDSV data,SAM based classifier,Spectral Angle Mapper,anthropic change,global urban extent map,land cover class,natural change,normalized difference spectral vector,spectral transformation,urban areas,urban mapping,Landsat,Normalized Spectral Difference Vector (NDSV),remote sensing,spectral analysis,urban mapping | Terrain mapping,Population,Computer vision,Normalization (statistics),Remote sensing,Support vector machine,Spectral transformation,Artificial intelligence,Classifier (linguistics),Contextual image classification,Land cover,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 3 | 1545-598X |
Citations | PageRank | References |
12 | 1.92 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emanuele Angiuli | 1 | 12 | 1.92 |
| Giovanna Trianni | 2 | 90 | 10.92 |