Remote Sensing Imagery and Signature Fields Reconstruction via Aggregation of Robust Regularization With Neural Computing
The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. First, the problem-oriented modification of the previously proposed fused Bayesian- regulari...
| Autores: | , |
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| Tipo de documento: | capítulo de livro |
| Estado: | Versão publicada |
| Data de publicação: | 2007 |
| País: | México |
| Recursos: | Instituto Tecnológico y de Estudios Superiores de Occidente |
| Repositório: | Repositorio Institucional del ITESO |
| Idioma: | inglês |
| OAI Identifier: | oai:rei.iteso.mx:11117/3309 |
| Acesso em linha: | http://hdl.handle.net/11117/3309 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Remote Sensing Fused Bayesian Regularization Neural Networks |
| Resumo: | The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. First, the problem-oriented modification of the previously proposed fused Bayesian- regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill-poseness due to system-level and model-level uncertainties. Second, the modification of the Hopfield-type maximum entropy neural network (NN) is proposed that enables such NN to perform numerically the robust adaptive FBR technique via efficient NN computing. Finally, we report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the devel- oped method. |
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