On the Use of Attention Map for Land Cover Mapping

The use of machine learning technology with remote sensing image analysis, especially for the land cover mapping requires experts and huge resources because every pixel in the training set must be labeled. This task is time-consuming and tedious. Therefore, a better strategy is to only identify what classes are present in an image without specifying where they are. In this way, a large number of remote sensing images can be labeled quickly. To achieve this goal, we employed the attention layer to create the attention map. The attention map is then further segmented to produce the final l and c over m ap where every pixel in an image will be labeled. We have tested the performance of our proposed algorithm with UC Merced Dataset and achieved 79.7 % in identifying the presence of land cover classes and 71.2 % accuracy in the labeling of all pixels.