AUTOMATIC FEATURES EXTRACTION IN SUB-URBAN LANDSCAPE USING VERY HIGH RESOLUTION COSMO-SKYMED SAR IMAGES

被引：1

作者：

Del Frate, Fabio ^{[1
]}

Pratola, Chiara ^{[1
]}

Schiavon, Giovanni ^{[1
]}

Solimini, Domenico ^{[1
]}

机构：

[1] Univ Roma Tor Vergata, Earth Observat Lab, DISP, I-00133 Rome, Italy

来源：

2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2011年

关键词：

Cosmo-SkyMed; very high resolution; image classification; GLCM; neural networks; URBAN ENVIRONMENTS; TEXTURE;

D O I：

10.1109/IGARSS.2011.6050006

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The new generation of spaceborne instruments, capable of capturing a large amount of very-high resolution images within a short revisit time, is allowing remote sensing researchers and final users to receive huge amounts of data in rather short times. Such a scenario makes it mandatory the development of techniques, as much as possible automatic, for the understanding and the effective exploitation of the available information. This contribution deals with the features extraction from Spotlight Cosmo-SkyMed SAR imagery (1 m spatial resolution) by means Multi Layer Perceptron Neural Network (MLP-NN) algorithms. For a better pixel characterization, textural parameters have been also considered as additional information for the classification procedure.

引用

页码：3614 / 3617

页数：4

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