An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images

Authors:
L. Bruzzone;D. F. Prieto
Affiliations:
Dept. of Inf. & Commun. Technol., Trento Univ.;-
Venue:
IEEE Transactions on Image Processing
Year:
2002

Citing 0
Cited 15

Image thresholding based on the EM algorithm and the generalized Gaussian distribution

Pattern Recognition
A composed supervised/unsupervised approach to improve change detection from remote sensing

Pattern Recognition Letters
An application of MAP-MRF to change detection in image sequence based on mean field theory

EURASIP Journal on Applied Signal Processing
An unsupervised context-sensitive change detection technique based on modified self-organizing feature map neural network

International Journal of Approximate Reasoning
Gray level difference-based transition region extraction and thresholding

Computers and Electrical Engineering
Robust estimation of 3-D line segments from satellite images for model building and change detection

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Objects based change detection in a pair of gray-level images

Pattern Recognition
A context-sensitive technique robust to registration noise for change detection in VHR multispectral images

IEEE Transactions on Image Processing
A novel technique for subpixel image classification based on support vector machine

IEEE Transactions on Image Processing
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

Information Sciences: an International Journal
Comparison between different illumination independent change detection techniques

Proceedings of the 2011 International Conference on Communication, Computing & Security
Solving geometric co-registration problem of multi-spectral remote sensing imagery using SIFT-based features toward precise change detection

ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Gradual land cover change detection based on multitemporal fraction images

Pattern Recognition
Change Detection of Remote Sensing Images with Semi-supervised Multilayer Perceptron

Fundamenta Informaticae
An improved MRF-based change detection approach for multitemporal remote sensing imagery

Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

A novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semiparametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov random field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semiparametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach