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Open AccessArticle

An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes

by 1, 2,* and 3
1
Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong
2
Department of Geography and Resource Management, Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong
3
Department of Geography and Resource Management, Institute of Future Cities, Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 324; https://doi.org/10.3390/rs11030324
Received: 14 January 2019 / Revised: 26 January 2019 / Accepted: 30 January 2019 / Published: 6 February 2019
(This article belongs to the Section Remote Sensing Image Processing)
Studies of land surface dynamics in heterogeneous landscapes often require satellite images with a high resolution, both in time and space. However, the design of satellite sensors often inherently limits the availability of such images. Images with high spatial resolution tend to have relatively low temporal resolution, and vice versa. Therefore, fusion of the two types of images provides a useful way to generate data high in both spatial and temporal resolutions. A Bayesian data fusion framework can produce the target high-resolution image based on a rigorous statistical foundation. However, existing Bayesian data fusion algorithms, such as STBDF (spatio-temporal Bayesian data fusion) -I and -II, do not fully incorporate the mixed information contained in low-spatial-resolution pixels, which in turn might limit their fusion ability in heterogeneous landscapes. To enhance the capability of existing STBDF models in handling heterogeneous areas, this study proposes two improved Bayesian data fusion approaches, coined ISTBDF-I and ISTBDF-II, which incorporate an unmixing-based algorithm into the existing STBDF framework. The performance of the proposed algorithms is visually and quantitatively compared with STBDF-II using simulated data and real satellite images. Experimental results show that the proposed algorithms generate improved spatio-temporal-resolution images over STBDF-II, especially in heterogeneous areas. They shed light on the way to further enhance our fusion capability. View Full-Text
Keywords: bayesian estimation framework; remote sensing; high spatiotemporal resolution; image fusion; spatial unmixing; heterogeneous landscape bayesian estimation framework; remote sensing; high spatiotemporal resolution; image fusion; spatial unmixing; heterogeneous landscape
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MDPI and ACS Style

Xue, J.; Leung, Y.; Fung, T. An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes. Remote Sens. 2019, 11, 324. https://doi.org/10.3390/rs11030324

AMA Style

Xue J, Leung Y, Fung T. An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes. Remote Sensing. 2019; 11(3):324. https://doi.org/10.3390/rs11030324

Chicago/Turabian Style

Xue, Jie; Leung, Yee; Fung, Tung. 2019. "An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes" Remote Sens. 11, no. 3: 324. https://doi.org/10.3390/rs11030324

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