http://www.cnr.it/ontology/cnr/individuo/prodotto/ID80596

Information-theoretic textural features of SAR images: an assessment for land cover classification (Contributo in atti di convegno)

Type

Prodotto della ricerca (Classe)
Contributo in atti di convegno (Classe)

Label

Information-theoretic textural features of SAR images: an assessment for land cover classification (Contributo in atti di convegno) (literal)

Anno

2004-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1117/12.568006 (literal)

Alternative label

B. Aiazzi, L. Alparone, S. Baronti, M. Bianchini (2004)
Information-theoretic textural features of SAR images: an assessment for land cover classification
in SPIE Remote Sensing Europe 2004, Gran Canaria, Spain, 12-17 Settembre 2004
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

B. Aiazzi, L. Alparone, S. Baronti, M. Bianchini (literal)

Pagina inizio

290 (literal)

Pagina fine

300 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

Volume pubblicato nel Novembre 2004. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=851154 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

Proceedings of SPIE Remote Sensing Europe 2004, Image and Signal Processing for Remote Sensing X (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

5573 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

5573 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

SPIE Vol. 5573, Image and Signal Processing for Remote Sensing X, L.Bruzzone (Ed.), pp. 290-300, Nov. 2004. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

11 (literal)

Note

ISI Web of Science (WOS) (literal)
Scopus (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

IFAC-CNR Massimo Bianchini: ISC-CNR (literal)

Titolo

Information-theoretic textural features of SAR images: an assessment for land cover classification (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

0-8194-5520-2 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

L. Bruzzone (literal)

Abstract

In this work, two feature, calculable from SAR images on a per-pixel basis, but relying on global image statistics, are described and discussed. The rationale is that spatial heterogeneity is regarded as uncertainty, that is unpredictability of a sample feature, e.g., the square root of local variance, from another pixel feature, like the local mean. Thus, such an uncertainty can be measured by resorting to Shannon's Information Theory in a mathematically rigorous and physically consistent manner. Starting from the multiplicative noise and texture models peculiar of SAR imagery, the conditional information of square root of estimated local variance to local mean has been found to be a powerful heterogeneity measurement, very little sensitive to the noise, and thus capable of capturing subtle variations of backscatter and texture whenever they are embedded in a heavy speckle. On the other side, the joint information of standard deviation to mean, although not strictly a heterogeneity feature, can be used as a textural feature for automated segmentation and classification, thanks to its noise-insensitiveness and to its capability of highlighting man-made structures. Experimental results carried out on C-band SIR-C and X-band X-SAR data of the city of Pavia, in Italy, demonstrate that the proposed features are useful for automated segmentation and classification tasks. Promising results are obtained in terms of discrimination of urban and suburban areas with different degrees of building density. Furthermore, the additional capabilities stemming from the joint use of X-band data, analogous to those available after the launch of the upcoming COSMO/SkyMed mission, are highlighted and discussed. (literal)

Editore