Back to Search
Start Over
Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture
- Source :
- Remote Sensing; Volume 6; Issue 8; Pages: 7339-7359, Remote Sensing, Vol 6, Iss 8, Pp 7339-7359 (2014)
- Publication Year :
- 2014
- Publisher :
- MDPI AG, 2014.
-
Abstract
- Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In this paper, a new method that combines spectral information and multivariate texture is proposed. The multivariate textures are separately extracted from multispectral data using a multivariate variogram with different distance measures, i.e., Euclidean, Mahalanobis and spectral angle distances. The multivariate textures and the spectral bands are then combined for urban built-up area extraction. Because the urban built-up area is the only target class, a one-class classifier, one-class support vector machine, is used. For comparison, the classical gray-level co-occurrence matrix (GLCM) is also used to extract image texture. The proposed method was evaluated using bi-temporal Landsat TM/ETM+ data of two megacity areas in China. Results demonstrated that the proposed method outperformed the use of spectral information alone and the joint use of the spectral information and the GLCM texture. In particular, the inclusion of multivariate variogram textures with spectral angle distance achieved the best results. The proposed method provides an effective way of extracting urban built-up areas from Landsat series images and could be applicable to other applications.
- Subjects :
- Multivariate statistics
Mahalanobis distance
urban built-up area
multivariate texture
OCSVM
Landsat
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Spectral bands
Land cover
Distance measures
Support vector machine
Image texture
General Earth and Planetary Sciences
lcsh:Q
lcsh:Science
Variogram
Remote sensing
Subjects
Details
- ISSN :
- 20724292
- Volume :
- 6
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....5d7816c6ea2705218fd2ffc10ecfe89d