Adaptively weighted decision fusion in 30 m land-cover mapping with Landsat and MODIS data

Abstract

© 2015 Taylor & Francis. Although the combined use of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data in land-cover classification has been widely adopted, the majority of such use of Landsat and MODIS data is done at the pixel level or feature input level in land-cover classification. We propose in this research a new method to make integrated use of different satellite data by adaptively weighted decision-level fusion. Training and validation samples were collected independently. Training samples were obtained from 329 regions and validation samples from 439 randomly distributed single-point positions. A Support Vector Machine (SVM) classifier was applied to the Landsat 8 data for classification and probability estimation. A Random Forests (RF) classifier was applied to the MODIS time-series data for probability estimation. Weight values were computed based on decision credibility, and reliability values were computed based on data quality. Three decision fusion procedures were performed. In the first procedure, decisions obtained from a Landsat 8 pixel and its corresponding MODIS pixel were fused for improvements (FUSION1). In the second, decisions obtained from the spatial neighbours of the Landsat 8 pixel were added to FUSION1 (FUSION2). In the third, decision fusion only among the Landsat 8 pixel and its spatial neighbours was performed (FUSION3) for comparison. Overall accuracies for the results with Landsat data only, FUSION1, FUSION2, and FUSION3 are 74.0%, 79.3%, 80.6%, and 75.6%, respectively. As a comparison, we also experimented on the use of Landsat and MODIS data by concatenating their features directly. Two classifiers, SVM and RF, were trained and validated on the concatenated features. The overall accuracies were 72.9% and 75.4%, respectively. Results show that the proposed method can utilize information selectively, so that considerable improvements can be obtained and fewer errors introduced. Moreover, it can be easily extended to handle more than two types of data source.