Adaptive Complex-Amplitude Texture Classifier that Deals with Both Height and Reflectance for Interferometric SAR Images
Summary :
We propose an adaptive complex-amplitude texture classifier that takes into consideration height as well as reflection statistics of interferometric synthetic aperture radar (SAR) images. The classifier utilizes the phase information to segment the images. The system consists of a two-stage preprocessor and a complex-valued SOFM. The preprocessor extracts a complex-valued feature vectors corresponding to height and reflectance statistics of blocks in the image. The following SOFM generates a set of templates (references) adaptively and classifies a block into one of the classes represented by the templates. Experiment demonstrates that the system segments an interferometric SAR image successfully into a lake, a mountain, and so on. The performance is better than that of a conventional system dealing only with the amplitude information.
- Publication
- IEICE TRANSACTIONS on Electronics Vol.E83-C No.12 pp.1912-1916
- Publication Date
- 2000/12/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Problems of Random Scattering and Electromagnetic Wave Sensing)
- Category
- SAR Interferometry and Signal Processing
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Andriyan Bayu SUKSMONO, Akira HIROSE, "Adaptive Complex-Amplitude Texture Classifier that Deals with Both Height and Reflectance for Interferometric SAR Images" in IEICE TRANSACTIONS on Electronics,
vol. E83-C, no. 12, pp. 1912-1916, December 2000, doi: .
Abstract: We propose an adaptive complex-amplitude texture classifier that takes into consideration height as well as reflection statistics of interferometric synthetic aperture radar (SAR) images. The classifier utilizes the phase information to segment the images. The system consists of a two-stage preprocessor and a complex-valued SOFM. The preprocessor extracts a complex-valued feature vectors corresponding to height and reflectance statistics of blocks in the image. The following SOFM generates a set of templates (references) adaptively and classifies a block into one of the classes represented by the templates. Experiment demonstrates that the system segments an interferometric SAR image successfully into a lake, a mountain, and so on. The performance is better than that of a conventional system dealing only with the amplitude information.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/e83-c_12_1912/_p
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@ARTICLE{e83-c_12_1912,
author={Andriyan Bayu SUKSMONO, Akira HIROSE, },
journal={IEICE TRANSACTIONS on Electronics},
title={Adaptive Complex-Amplitude Texture Classifier that Deals with Both Height and Reflectance for Interferometric SAR Images},
year={2000},
volume={E83-C},
number={12},
pages={1912-1916},
abstract={We propose an adaptive complex-amplitude texture classifier that takes into consideration height as well as reflection statistics of interferometric synthetic aperture radar (SAR) images. The classifier utilizes the phase information to segment the images. The system consists of a two-stage preprocessor and a complex-valued SOFM. The preprocessor extracts a complex-valued feature vectors corresponding to height and reflectance statistics of blocks in the image. The following SOFM generates a set of templates (references) adaptively and classifies a block into one of the classes represented by the templates. Experiment demonstrates that the system segments an interferometric SAR image successfully into a lake, a mountain, and so on. The performance is better than that of a conventional system dealing only with the amplitude information.},
keywords={},
doi={},
ISSN={},
month={December},}
Copy
TY - JOUR
TI - Adaptive Complex-Amplitude Texture Classifier that Deals with Both Height and Reflectance for Interferometric SAR Images
T2 - IEICE TRANSACTIONS on Electronics
SP - 1912
EP - 1916
AU - Andriyan Bayu SUKSMONO
AU - Akira HIROSE
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E83-C
IS - 12
JA - IEICE TRANSACTIONS on Electronics
Y1 - December 2000
AB - We propose an adaptive complex-amplitude texture classifier that takes into consideration height as well as reflection statistics of interferometric synthetic aperture radar (SAR) images. The classifier utilizes the phase information to segment the images. The system consists of a two-stage preprocessor and a complex-valued SOFM. The preprocessor extracts a complex-valued feature vectors corresponding to height and reflectance statistics of blocks in the image. The following SOFM generates a set of templates (references) adaptively and classifies a block into one of the classes represented by the templates. Experiment demonstrates that the system segments an interferometric SAR image successfully into a lake, a mountain, and so on. The performance is better than that of a conventional system dealing only with the amplitude information.
ER -
English
