Name
Title | ||
|---|---|---|
Partially supervised classification of optical high spatial resolution images in urban environment using spectral and textural information |
Abstract | ||
|---|---|---|
Conventional multispectral classification methods exhibit poor performances in the detection of urban objects, in high spatial resolution satellite images. This is because multispectral classification is based on the spectral information of the individual pixels and such spectral information is very moderate in the case of high spatial resolution sensor data. We propose to solve the problem by an integrated approach that considers also the important information contained in the spatial arrangement of pixel intensities by means of two methodological aspects. The first aspect is the data fusion to the detection of urban areas by means of the integration of the spectral information with the spatial information represented by texture features extracted from the grey level co-occurrence matrix (GLCM). The second one is the partially supervised classification that exploits the maps provided by the classification of both spectral and texture information by means of a hierarchical clustering algorithm. The proposed approach has been tested on a multispectral image of the IKONOS MS sensor, at 4-meter spatial resolution, acquired over an urban area in Brazil. A quantitative evaluation of the classification performances, based on the overall and average accuracy values and the separability factor, will be reported. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/IGARSS.2004.1368949 | Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International |
Keywords | Field | DocType |
grey systems,image classification,image texture,pattern clustering,sensor fusion,Brazil,GLCM,IKONOS MS sensor,data fusion,grey level co-occurrence matrix,hierarchical clustering algorithm,methodological aspect,multispectral classification method,multispectral image,optical high spatial resolution image,partially supervised classification,pixel intensity,quantitative evaluation,separability factor,spatial arrangement,spatial information,spectral information integration,spectral/textural information,spectral/texture information,texture feature,urban environment,urban object detection | Spatial analysis,Computer science,Remote sensing,Artificial intelligence,Contextual image classification,Hierarchical clustering,Computer vision,Pattern recognition,Image texture,Multispectral image,Sensor fusion,Pixel,Image resolution | Conference |
Volume | ISSN | ISBN |
1 | 2153-6996 | 0-7803-8742-2 |
Citations | PageRank | References |
6 | 1.69 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michaela De Martino | 1 | 14 | 4.28 |
| Giorgia Macchiavello | 2 | 6 | 1.69 |
| Sebastiano B. Serpico | 3 | 749 | 64.86 |