Name
Title | ||
|---|---|---|
Rule-Based Classification of a Very High Resolution Image in an Urban Environment Using Multispectral Segmentation Guided by Cartographic Data |
Abstract | ||
|---|---|---|
Classification algorithms based on single-pixel analysis often do not give the desired result when applied to high-spatial-resolution remote-sensing data. In such cases, classification algorithms based on object-oriented image segmentation are needed. There are many segmentation algorithms in the literature, but few have been applied in urban studies to classify a high-spatial-resolution remote-sensing image. Furthermore, the user must specify the spectral and spatial parameters that are data dependent. In this paper, we propose an automatic multispectral segmentation algorithm inspired by the specific idea of guiding a classification process for a high-spatial-resolution remote-sensing image of an urban area using an existing digital map of the same area. The classification results could be used, for example, for high-scale database updating or change-detection studies. The algorithm developed uses digital maps and spectral data as inputs. It generates the segmentation parameters automatically. The algorithm is able to provide a segmented image with accuracy greater than 90%. The segmentation results are then used in a rule-based classification using spectral, geometric, textural, and contextual information. The classification accuracy of the proposed rule-based classification is at least 17% greater than the maximum-likelihood classification results. Results and future improvements will be discussed. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TGRS.2010.2044508 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
geophysical image processing,geophysical techniques,image classification,image segmentation,image texture,object-oriented methods,remote sensing,automatic multispectral segmentation algorithm,cartographic data,contextual information,geographic database,geometric information,high-spatial-resolution remote-sensing image,maximum-likelihood classification,object-oriented image segmentation,remote-sensing data,rule-based classification,single-pixel analysis,spatial parameters,spectral parameters,textural information,urban environment,very high resolution image,Geographic database,high-resolution satellite imagery,rule-based classification,urban environment | Data mining,One-class classification,Scale-space segmentation,Remote sensing,Segmentation-based object categorization,Image segmentation,Multispectral segmentation,Artificial intelligence,Contextual image classification,Computer vision,Image texture,Statistical classification,Mathematics | Journal |
Volume | Issue | ISSN |
48 | 8 | 0196-2892 |
Citations | PageRank | References |
15 | 0.95 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mourad Bouziani | 1 | 15 | 0.95 |
| Kalifa Goita | 2 | 15 | 0.95 |
| Dong-Chen He | 3 | 189 | 21.81 |