Next Article in Journal
An Automated Processing Algorithm for Flat Areas Resulting from DEM Filling and Interpolation
Previous Article in Journal
Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(11), 374; https://doi.org/10.3390/ijgi6110374

Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion

1
Electronic and Information School, Wuhan University, Wuhan 430072, China
2
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
Remote Sensing and Information Engineering School, Wuhan University, Wuhan 430079, China
4
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 13 October 2017 / Revised: 8 November 2017 / Accepted: 15 November 2017 / Published: 21 November 2017
View Full-Text   |   Download PDF [23175 KB, uploaded 21 November 2017]   |  

Abstract

Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM) has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes. View Full-Text
Keywords: spatio-temporal fusion; multi-dictionary learning; sparse representation; MODIS; Landsat; Bayes; maximum a posterior spatio-temporal fusion; multi-dictionary learning; sparse representation; MODIS; Landsat; Bayes; maximum a posterior
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

He, C.; Zhang, Z.; Xiong, D.; Du, J.; Liao, M. Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion. ISPRS Int. J. Geo-Inf. 2017, 6, 374.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top