  There is a trend to acquire high accuracy land-cover maps using multi-source
classification methods, most of which are based on data fusion, especially
pixel- or feature-level fusions. A probabilistic graphical model (PGM) approach
is proposed in this research for 30 m resolution land-cover mapping with
multi-temporal Landsat and MODerate Resolution Imaging Spectroradiometer
(MODIS) data. Independent classifiers were applied to two single-date Landsat 8
scenes and the MODIS time-series data, respectively, for probability
estimation. A PGM was created for each pixel in Landsat 8 data. Conditional
probability distributions were computed based on data quality and reliability
by using information selectively. Using the administrative territory of Beijing
City (Area-1) and a coastal region of Shandong province, China (Area-2) as
study areas, multiple land-cover maps were generated for comparison.
Quantitative results show the effectiveness of the proposed method. Overall
accuracies promoted from 74.0% (maps acquired from single-temporal Landsat
images) to 81.8% (output of the PGM) for Area-1. Improvements can also be seen
when using MODIS data and only a single-temporal Landsat image as input
(overall accuracy: 78.4% versus 74.0% for Area-1, and 86.8% versus 83.0% for
Area-2). Information from MODIS data did not help much when the PGM was applied
to cloud free regions of. One of the advantages of the proposed method is that
it can be applied where multi-temporal data cannot be simply stacked as a
multi-layered image.
