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The use of wavelets for feature extraction of cities from satellite sensor images
| Content Provider | Scilit |
|---|---|
| Author | Mesev, Victor |
| Copyright Year | 2003 |
| Description | Most image analysts would agree that, when extracting urban/suburban information from remotely sensed data, it is more important to have high spatial resolution (often finer than 5 m by 5 m) than high spectral resolution (i.e., a large number of spectral bands) (Jensen and Cowen 1999). However, some researchers (Latty and Hoffer 1981; Irons et al. 1985; Green et al. 1993; Muller 1997) have reported that finer spatial resolution image data do not necessarily improve traditional spectral-based image classification. Moreover, the spectral classification approach has been criticized when fine spatial resolution images are used, especially for urban features (Latty and Hoffer 1981; Markham and Townshend 1981; Woodcock and Strahler 1987; Cushnie 1987; Myint 2001). Traditional image classification meth ods, such as the maximum likelihood classifier, use spectral information (pixel values) as a basis to analyse and classify remote sensing images. They become less efficient when complex urban features are analysed (see Mesev, Chapter 9 in this book). Book Name: Remotely-Sensed Cities |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2004-0-01269-6&isbn=9780429181160&doi=10.1201/9781482264678-15&format=pdf |
| Ending Page | 160 |
| Page Count | 26 |
| Starting Page | 135 |
| DOI | 10.1201/9781482264678-15 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2003-03-06 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Remotely-sensed Cities Remote Sensing Extracting Classification Hoffer Spatial Resolution Spectral Sensing |
| Content Type | Text |
| Resource Type | Chapter |
