Literature review of artificial neural networks and knowledge-based systems for image analysis and interpretation of data in remote sensing

Goı̈ta, Kalifa; Gonzalez-Rubio, R.; Bénié, G.B.; Royer, A.; Michaud, François

doi:10.1109/cjece.1994.6592069

Cited by 6 publications

(3 citation statements)

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“…Short overview Binford (1982) and Brooks (1984) give early surveys of model-based analysis systems, Rao and Jain (1988) treat various aspects of knowledge representation and control in computer vision, while Kanade (1980) reports of the levels of knowledge required for region segmentation. A review of ANNs and KBS in Remote Sensing is given in Goita et al (1994), and of KBS in Matsuyama (1989Matsuyama ( , 1993, Haralick and Shapiro (1993) and Crevier and Lepage (1997). Sowmya and Trinder (2000) review modelling and representation issues in automated feature extraction from aerial and satellite images.…”

Section: Knowledge-based Object Extractionmentioning

confidence: 99%

Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems

Baltsavias¹

2004

ISPRS Journal of Photogrammetry and Remote Sensing

217

117

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Section: Knowledge-based Object Extractionmentioning

confidence: 99%

Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems

Baltsavias¹

2004

ISPRS Journal of Photogrammetry and Remote Sensing

217

117

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“…For example, in - In recent years, Artificial Neural Networks (ANNs) have emerged as a powerful technique for modeling general input-output relationships. ANNs have been widely used in various areas such as control [1], telecommunications [2], biomedicine [3], remote sensing [4], pattern recognition [5], manufacturing [6] and etc. In RF/microwave areas,…”

Section: Thesis Motivationmentioning

confidence: 99%

Neural network based techniques for microwave passive components modeling and design

Zhang¹

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“…Generally ANNs are strong techniques for modeling any input/output relationships. Many applications have been reported in several areas such as control [5], telecommunications [6], biomedical [7], remote sensing [8], pattern recognition [9] , and manufacturing [10]. However, ANNs are being used frequently in the RF/microwave design area [11].…”

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confidence: 99%

Sensitivity-Analysis-Based Adjoint Neural Network Techniques for Nonlinear Applications

Sadrossadat¹

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Artificial neural networks (ANN) have recently emerged as a powerful computeraided design (CAD) tool for modeling nonlinear devices and circuits. The overall objective of this thesis is to develop sensitivity analysis based neural network techniques for both frequency domain and transient modeling of nonlinear circuits. The proposed techniques not only adds sensitivity data to the obtained model but also makes conventional training more efficient. The first contribution of this thesis is the development of sensitivity-analysis-based adjoint neural-network (SAANN) technique for modeling microwave passive components. This method adds sensitivity data to the obtained model. In addition, the SAANN technique reduces the amount of training data required for model development increasing the efficiency of model development. As a further contribution, this thesis presents a novel robust modeling technique, adjoint state-space dynamic neural network (ASSDNN), for transient modeling of nonlinear optical/electrical components and circuits. This technique adds time-domain sensitivity data, which does not exist in current optoelectronic and physics-based simulators, to the output of the obtained model. The proposed technique requires less training data for creating the model and consequently makes training faster and more efficient. Furthermore, this technique was developed such that it can take advantage of parallel computation. This results in the technique being much faster and efficient than conventional transient modeling techniques. In addition, the evaluation time for models of nonlinear optical-electrical and physics-based devices generated using the proposed technique is reduced compared to current simulation tools. ii To my parents Fatemeh Pourmoghadas and Hamid Sadrossadat iii Acknowledgments First of all, I would like to thank my supervisors, Professor Pavan Gunupudi and Professor Qi-Jun Zhang for their constant support, encouragement, guidance and expert supervision throughout my PhD's program. Their professional leaderships have made the research through my PhD program a rewarding journey. Their continuous striving for research at the highest level will influence me for my professional future life. It was my honor to work under their supervision and guidance.

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Literature review of artificial neural networks and knowledge-based systems for image analysis and interpretation of data in remote sensing

Cited by 6 publications

References 0 publications

Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems

Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems

Neural network based techniques for microwave passive components modeling and design

Sensitivity-Analysis-Based Adjoint Neural Network Techniques for Nonlinear Applications

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