Feature Selection Approach Based on FODPSO and SVM
A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data.
The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral-spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.
Related Image Processing Projects Titles:
- A Pansharpening Method Based on the Sparse Representation of Injected Details.
- Block Adjustment for Satellite Imagery Based on the Strip Constraint.
- Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors.
- Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification.
- Learning Understandable Neural Networks With Nonnegative Weight Constraints.
- Two-Tier Tissue Decomposition for Histopathological Image Representation and Classification.
- Feature Matching With an Adaptive Optical Sensor in a Ground Target Tracking System.
- Classification of Hyperspectral Image Based on Sparse Representation in Tangent Space.
- Spectral Unmixing of Hyperspectral Imagery Using Multilayer NMF.
- A New Sparsity-Based Band Selection Method for Target Detection of Hyperspectral Image.
- An Adaptive Pixon Extraction Technique for Multispectral/Hyperspectral Image Classification.
- COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography.
- A Novel Range Grating Lobe Suppression Method Based on the Stepped-Frequency SAR Image.
- Sparse Unmixing of Hyperspectral Data Using Spectral A Priori Information.
We want to support Uncompromise Matlab service for all your Requirements Our Reseachers and Technical team keep update the technology for all subjects ,We assure We Meet out Your Needs.
- Matlab Research Paper Help
- Matlab assignment help
- Matlab Project Help
- Matlab Homework Help
- Simulink assignment help
- Simulink Project Help
- Simulink Homework Help
- Matlab Research Paper Help
- NS3 Research Paper Help
- Omnet++ Research Paper Help
- Customised Matlab Assignments
- Global Assignment Knowledge
- Best Assignment Writers
- Certified Matlab Trainers
- Experienced Matlab Developers
- Over 400k+ Satisfied Students
- Ontime support
- Best Price Guarantee
- Plagiarism Free Work
- Correct Citations
Unlimited support we offer you
For better understanding purpose we provide following Materials for all Kind of Research & Assignment & Homework service.
- Result snapshot
- Video Tutorial
- Instructions Profile
- Sofware Install Guide
- Execution Guidance
- Implement Plan
Matlab projects innovators has laid our steps in all dimension related to math works.Our concern support matlab projects for more than 10 years.Many Research scholars are benefited by our matlab projects service.We are trusted institution who supplies matlab projects for many universities and colleges.
Reasons to choose Matlab Projects .org???
Our Service are widely utilized by Research centers.More than 5000+ Projects & Thesis has been provided by us to Students & Research Scholars. All current mathworks software versions are being updated by us.
Our concern has provided the required solution for all the above mention technical problems required by clients with best Customer Support.
- Novel Idea
- Ontime Delivery
- Best Prices
- Unique Work