My Account | Careers | Downloads | Blog

PROJECT TITLE :

Semisupervised Discriminant Feature Learning for SAR Image Category via Sparse Ensemble

ABSTRACT:

Terrain scene classification plays an necessary role in varied artificial aperture radar (SAR) image understanding and interpretation. This paper presents a completely unique approach to characterize SAR image content by addressing category with a limited variety of labeled samples. In the proposed approach, every SAR image patch is characterize by a discriminant feature which is generated in an exceedingly semisupervised manner by utilizing a spare ensemble learning procedure. In particular, a nonnegative sparse coding procedure is applied on the given SAR image patch set to generate the feature descriptors initial. The set is combined with a restricted variety of labeled SAR image patches and an abundant range of unlabeled ones. Then, a semisupervised sampling approach is proposed to construct a collection of weak learners, in that every one is modeled by a logistic regression procedure. The discriminant data can be introduced by projecting SAR image patch on every weak learner. Finally, the features of SAR image patches are produced by a sparse ensemble procedure that can scale back the redundancy of multiple weak learners. Experimental results show that the proposed discriminant feature learning approach can achieve a better classification accuracy than several state-of-the-art approaches.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction ABSTRACT: At the moment, one of the most cutting-edge approaches for
PROJECT TITLE : Semisupervised Multiple Choice Learning for Ensemble Classification ABSTRACT: Due to the fact that it is so effective at enhancing the predictive performance of classification models, ensemble learning has a wide
PROJECT TITLE : Discriminative Mixture Variational Autoencoder for Semisupervised Classification ABSTRACT: For the purpose of feature extraction in semisupervised learning, a deep probability model known as the discriminative
PROJECT TITLE :A Low-Rank Approximation-Based Transductive Support Tensor Machine for Semisupervised ClassificationABSTRACT:Within the fields of machine learning, pattern recognition, image processing, and laptop vision, the data
PROJECT TITLE :Network Traffic Classification Using Correlation Information - 2013ABSTRACT:Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry