Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model

2006 ◽  
Vol 15 (11) ◽  
pp. 3431-3439 ◽  
Author(s):  
I. Ben Ayed ◽  
N. Hennane ◽  
A. Mitiche
Keyword(s):  
Image Segmentation ◽  
Observation Model ◽  
Variational Image

scholarly journals Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nicola Case ◽  
Alfonso Vitti
Keyword(s):  
Image Segmentation ◽  
Variational Model ◽  
Great Level ◽  
Radiometric Analysis ◽  
Variational Segmentation ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Original Procedure ◽  
Variational Image ◽  
Homogeneous Regions

Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic strips of missing data in the imagery bands. Within the vast and interdisciplinary image reconstruction application field, many works have been presented in the last few decades to tackle the specific Landsat 7 gap-filling problem. This work proposes another contribution in this field presenting an original procedure based on a variational image segmentation model coupled with radiometric analysis to reconstruct damaged images acquired in a multi-temporal scenario, typical in satellite remote sensing. The key idea is to exploit some specific features of the Mumford–Shah variational model for image segmentation in order to ease the detection of homogeneous regions which will then be used to form a set of coherent data necessary for the radiometric reconstruction of damaged regions. Two reconstruction approaches are presented and applied to SLC-off Landsat 7 data. One approach is based on the well-known histogram matching transformation, the other approach is based on eigendecomposition of the bands covariance matrix and on the sampling from Gaussian distributions. The performance of the procedure is assessed by application to artificially damaged images for self-validation testing. Both of the proposed reconstruction approaches had led to remarkable results. An application to very high resolution WorldView-3 data shows how the procedure based on variational segmentation allows an effective reconstruction of images presenting a great level of geometric complexity.

A Nonconservative Flow Field for Robust Variational Image Segmentation

2010 ◽  
Vol 19 (2) ◽  
pp. 478-490 ◽  
Author(s):  
P. Ghosh ◽  
L. Bertelli ◽  
B. Sumengen ◽  
B.S. Manjunath
Keyword(s):  
Image Segmentation ◽  
Flow Field ◽  
Variational Image

Nonlocal variational image segmentation models on graphs using the Split Bregman

2014 ◽  
Vol 21 (3) ◽  
pp. 289-299
Author(s):  
Ke Lu ◽  
Qian Wang ◽  
Ning He ◽  
Daru Pan ◽  
Weiguo Pan
Keyword(s):  
Image Segmentation ◽  
Split Bregman ◽  
Variational Image ◽  
Segmentation Models

Variational image segmentation using boundary functions

1998 ◽  
Vol 7 (9) ◽  
pp. 1269-1282 ◽  
Author(s):  
G.A. Hewer ◽  
C. Kenney ◽  
B.S. Manjunath
Keyword(s):  
Image Segmentation ◽  
Boundary Functions ◽  
Variational Image
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