Name
Title | ||
|---|---|---|
Urban built-up area extraction using combined spectral information and multivariate texture |
Abstract | ||
|---|---|---|
Urban built-up area information is required by many applications, such as research of urbanization rate. Urban built-up area extraction using moderate resolution remotely sensed data (e.g. Landsat TM/ETM+) presents numerous challenges, such as very heterogeneous spectral features of urban areas, spectral confusion between built-up class and others. Considering that image texture is one of the important spatial information for identifying urban land cover, a new methodology to address these issues is proposed. This approach involves processes as the following, as a first step, multivariate texture is computed through multivariate variogram. Spectral bands and multivariate texture are then combined in classification process for built-up area extraction. One-Class Support Vector Machine (OCSVM) classifier was used in this process. A comprehensive evaluation is present with Landsat TM data of Beijing, China. Results demonstrate that the proposed method significantly improves the accuracy of urban area extraction. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IGARSS.2013.6723772 | IGARSS |
Keywords | Field | DocType |
ocsvm classifier,terrain mapping,urban built up area extraction,spectral information,beijing,spectral confusion,built-up area,multivariate variogram,moderate resolution remotely sensed data,china,spatial information,spectral bands,landsat etm+ data,image classification,one class support vector machine,geophysical image processing,information extraction,multivariate texture,image texture,landsat tm data,support vector machines,satellites,earth,feature extraction,data mining,accuracy,remote sensing | Spatial analysis,Data mining,Computer science,Remote sensing,Artificial intelligence,Spectral bands,Contextual image classification,Land cover,Variogram,Computer vision,Built-up area,Multivariate statistics,Image texture | Conference |
Volume | Issue | ISSN |
null | null | 2153-6996 |
ISBN | Citations | PageRank |
978-1-4799-1114-1 | 0 | 0.34 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Zhang | 1 | 1102 | 188.11 |
| Peijun Li | 2 | 37 | 9.63 |
| Haiqing Xu | 3 | 4 | 1.84 |