My Account | Careers | Downloads | Blog

PROJECT TITLE :

Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise-Based Markov Random Field Model

ABSTRACT:

The multimodal change detection (CD) problem in remote sensing imaging is addressed using a Bayesian statistical approach. Furthermore, the multimodal CD problem is formulated as an unsupervised Markovian problem. This Markovian model uses a pixel pairwise modelling observation field and a pair of bitemporal heterogeneous satellite images as its primary originality. To avoid this, we can use modelling techniques that allow us to rely on a robust visual cue that is nearly invariant to the imaging (multi-) modality. We first utilise a preliminary iterative estimating technique that takes into consideration the diversity of laws in the distribution mixture and predicts the parameters of the Markovian mixture model in order to leverage this observation cue in a stochastic likelihood model. An optimization procedure based on the previously calculated parameters is used to construct the MAP solution of the change detection map, a stochastic optimization process. Experiments and comparisons with various imaging modalities show that the proposed approach is robust.


Did you like this research project?

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

PROJECT TITLE : Unsupervised Spectral Feature Selection with Dynamic Hyper-graph Learning ABSTRACT: In order to produce interpretable and discriminative results from unsupervised spectral feature selection (USFS) methods, an embedding
PROJECT TITLE : Unsupervised Ensemble Classification with Sequential and Networked Data ABSTRACT: Ensemble learning, a paradigm of machine learning in which multiple models are combined, has shown promising performance in a variety
PROJECT TITLE : Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM ABSTRACT: In this paper, we present a novel unsupervised feature learning architecture that consists of a multi-clustering integration
PROJECT TITLE : Unsupervised Domain Adaptation via Discriminative Manifold Propagation ABSTRACT: It is possible to successfully leverage rich information from a labeled source domain into an unlabeled target domain through the
PROJECT TITLE : Deep Ladder-Suppression Network for Unsupervised Domain Adaptation ABSTRACT: The objective of unsupervised domain adaptation, also known as UDA, is to learn a classifier for a target domain that is not labeled

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

Project Enquiry