PROJECT TITLE :

Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding - 2018

ABSTRACT:

Feature extraction may be a terribly vital step for polarimetric artificial aperture radar (PolSAR) image classification. Several dimensionality reduction (DR) strategies have been employed to extract options for supervised PolSAR image classification. But, these DR-primarily based feature extraction strategies only consider every single pixel independently and thus fail to require into account the spatial relationship of the neighboring pixels, therefore their performance might not be satisfactory. To deal with this issue, we introduce a novel tensor local discriminant embedding (TLDE) methodology for feature extraction for supervised PolSAR image classification. The proposed technique combines the spatial and polarimetric info of each pixel by characterizing the pixel with the patch targeted at this pixel. Then each pixel is represented as a third-order tensor of which the primary 2 modes indicate the spatial info of the patch (i.e., the row and also the column of the patch) and the third mode denotes the polarimetric info of the patch. Primarily based on the label info of samples and therefore the redundance of the spatial and polarimetric info, a supervised tensor-primarily based DR technique, known as TLDE, is introduced to find three projections which project every pixel, that is, the third-order tensor into the low-dimensional feature. Finally, classification is completed based on the extracted options using the closest neighbor classifier and also the support vector machine classifier. The proposed methodology is evaluated on two real PolSAR data sets and therefore the simulated PolSAR knowledge sets with varied number of looks. The experimental results demonstrate that the proposed methodology not only improves the classification accuracy greatly however additionally alleviates the influence of speckle noise on classification.


Did you like this research project?

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


PROJECT TITLE :Supervised Topic Modeling Using Hierarchical Dirichlet Process-Based Inverse Regression: Experiments on E-Commerce Applications - 2018ABSTRACT:The proliferation of e-commerce involves mining client preferences and
PROJECT TITLE : Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-Occurrence Data - 2017 ABSTRACT: Using on-line consumer reviews as electronic word of mouth to help purchase-decision making
PROJECT TITLE : Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach - 2017 ABSTRACT: During this work, we focus on modeling user-generated review and overall rating pairs, and aim to spot semantic aspects
PROJECT TITLE : Silhouette Analysis for Human Action Recognition Based on Supervised Temporal t-SNE and Incremental Learning - 2015 ABSTRACT: This paper develops a person's action recognition method for human silhouette sequences
PROJECT TITLE :Supervised approach for detecting average over popular items attack in collaborative recommender systemsABSTRACT:Recent research has shown the numerous vulnerabilities of collaborative recommender systems in the

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

Project Enquiry