Next Article in Journal
The Effect of Environmental Conditions on the Quality of UAS Orthophoto-Maps in the Coastal Environment
Next Article in Special Issue
End Point Rate Tool for QGIS (EPR4Q): Validation Using DSAS and AMBUR
Previous Article in Journal
Simulation of the Urban Jobs–Housing Location Selection and Spatial Relationship Using a Multi-Agent Approach
Previous Article in Special Issue
Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Mapping Using FOSS and Open Data
Article

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

by and *,†
Department of Civil and Environmental Engineering, University of Trento, via Mesiano 77, 38123 Trento, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 17; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010017
Received: 2 December 2020 / Revised: 27 December 2020 / Accepted: 31 December 2020 / Published: 6 January 2021
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. View Full-Text
Keywords: image segmentation; image reconstruction; variational model; histogram matching; satellite imagery; free and open source software image segmentation; image reconstruction; variational model; histogram matching; satellite imagery; free and open source software
Show Figures

Figure 1

MDPI and ACS Style

Case, N.; Vitti, A. Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis. ISPRS Int. J. Geo-Inf. 2021, 10, 17. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010017

AMA Style

Case N, Vitti A. Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis. ISPRS International Journal of Geo-Information. 2021; 10(1):17. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010017

Chicago/Turabian Style

Case, Nicola, and Alfonso Vitti. 2021. "Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis" ISPRS International Journal of Geo-Information 10, no. 1: 17. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010017

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop