Annual land-cover mapping based on multi-temporal cloud-contaminated landsat images

Landsat images, which have fine spatial resolution, are an important data source for land-cover mapping. Multi-temporal Landsat classification has become popular because of the abundance of free-access Landsat images that are available. However, cloud cover is inevitable due to the relatively low temporal frequency of the data. In this paper, a novel approach for multi-temporal Landsat land-cover classification is proposed. The land cover for each Landsat acquisition date was first classified using a Support Vector Machine (SVM) and then the classification results were combined using different strategies, with missing observations allowed. Three strategies, including the majority vote (MultiSVM-MV), Expectation Maximisation (MultiSVM-EM) and joint SVM probability (JSVM), were used to merge the multi-temporal classification maps. The three algorithms were then applied to a region of the path/row 143/31 scene using 2010 Landsat-5 Thematic Mapper (TM) images. The results demonstrated that, for these three algorithms, the average overall accuracy (OA) improved with the increase in temporal depth; also, for a given temporal depth, the performance of JSVM was clearly better than that of MultiSVM-MV and MultiSVM-EM, and the performance of MultiSVM-EM was slightly better than that of MultiSVM-MV. The OA values for the three classification results, which use all epochs, were 70.28%, 72.40% and 74.80% for MultiSVM-MV, MultiSVM-EM and JSVM, respectively. In comparison, two other annual composite image-based classification methods, annual maximum Normalised Difference Vegetation Index (NDVI) composite image-based classification and annual best-available-pixel (BAP) composite image-based classification, gave OA values of 68.08% and 69.92%, respectively, meaning that our method produced a better performance. Therefore, the novel multi-temporal Landsat classification method presented in this paper can deal with the cloud-contamination problem and produce accurate annual land-cover mapping using multi-temporal cloud-contaminated images, which is of importance for regional and global land-cover mapping.