Target detection in SAR imagery by genetic programming
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Summary
The results of an extensive numerical study show that a novel approach, using genetic programming (GP), successfully evolves detectors which satisfy the earlier objectives and thus the principles of detection can be discovered and reused.
- Type
- article
- Published
- 1999-05-01
- Cited by
- 137
- References
- 1
- OpenAlex
- https://openalex.org/W2074138018
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:62240019
Keywords
Genetic programming, Synthetic aperture radar, Computer science, Detector, Artificial intelligence
References
Cited by
- Advanced Techniques for Scene Analysis
- Classification Strategies for Image Classification in Genetic Programming
- Automatic Detection of Ships in Spaceborne SAR Imagery
- Linear Genetic Programming for Multi-class Classification Problems
- Learning Composite Operators For Object Detection
- Change detection target by semivariogram textural classification added to practical application of wind speed retrieval using RADARSAT-1/SAR imagery
- Holism for target recognition in synthetic aperture radar imagery
- Using measured daily meteorological parameters to predict daily solar radiation
- Applying Online Gradient Descent Search to Genetic Programming for Object Recognition
- A Field Guide to Genetic Programming
- Genetic programming as strategy for learning image descriptor operators
- Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision
- Learning Discriminative Feature Representations for Visual Categorization
- A genetic programming framework for content-based image retrieval
- Depth-control strategies for crossover in tree-based genetic programming
- Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data
- Performing automatic target detection with evolvable finite state automata
- Recognition of sand dredges in the Changjiang River based on ASAR remote sensing data
- The prediction of profile deviations when Creep Feed grinding complex geometrical features by use of neural networks and genetic programming with real-time simulation
- Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification
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