Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images

DOI10.1109/TIP.2013.2259833zbMath1373.94781OpenAlexW2030728252WikidataQ51211562 ScholiaQ51211562MaRDI QIDQ5373469

Badri Narayan Subudhi, Lorenzo Bruzzone, Ashish Ghosh

Publication date: 27 October 2017

Published in: IEEE Transactions on Image Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tip.2013.2259833

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Detection theory in information and communication theory (94A13)

Related Items (1)

A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images

This page was built for publication: Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images