Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery
From MaRDI portal
Publication:2270853
DOI10.1016/j.patcog.2009.01.011zbMath1192.68592OpenAlexW1969076164WikidataQ61941562 ScholiaQ61941562MaRDI QIDQ2270853
Publication date: 29 July 2009
Published in: Pattern Recognition (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.patcog.2009.01.011
fuzzy clusteringdifferential evolutioncluster validity measuresremote sensing satellite imagerystatistical significant test
Related Items
Maximum likelihood estimation of Gaussian mixture models using stochastic search ⋮ Hidden genes genetic optimization for variable-size design space problems ⋮ Rational spectral collocation and differential evolution algorithms for singularly perturbed problems with an interior layer ⋮ Anisotropic diffusion filtering through multi-objective optimization ⋮ An unsupervised learning algorithm for membrane computing ⋮ Towards improving fuzzy clustering using support vector machine: application to gene expression data ⋮ Reliability-based fuzzy clustering ensemble ⋮ Application of Multiobjective Optimization Techniques in Biomedical Image Segmentation—A Study
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Optimization by Simulated Annealing
- Differential evolution. A practical approach to global optimization. With CD-ROM.
- Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- Stochastic adaptive search for global optimization.
- An efficient technique for superfamily classification of amino acid sequences: feature extraction, fuzzy clustering and prototype selection
- Stochastic global optimization.
- Statistical Properties of Error Estimators in Performance Assessment of Recognition Systems
- Printer graphics for clustering
- Unresolved Problems in Cluster Analysis
- Clustering Categorical Data Based on Distance Vectors