The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Propomos um classificador adaptativo de textura de amplitude complexa que leva em consideração a altura e também as estatísticas de reflexão de imagens de radar interferométrico de abertura sintética (SAR). O classificador utiliza as informações da fase para segmentar as imagens. O sistema consiste em um pré-processador de dois estágios e um SOFM de valor complexo. O pré-processador extrai vetores de características de valor complexo correspondentes às estatísticas de altura e refletância dos blocos na imagem. O seguinte SOFM gera um conjunto de modelos (referências) de forma adaptativa e classifica um bloco em uma das classes representadas pelos modelos. A experiência demonstra que o sistema segmenta com sucesso uma imagem SAR interferométrica em um lago, uma montanha e assim por diante. O desempenho é melhor que o de um sistema convencional que lida apenas com a informação de amplitude.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copiar
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
Copiar
@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},}
Copiar
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 -