Genetic Algorithm for Improved Transfer Learning Through Bagging Color-Adjusted Models

Computer vision has seen some breakthroughs in the last decade based on some methodological advances, but as well based on the availability of huge datasets like ImageNet for training. However, training data is generally scarce in remote sensing and even more in high-resolution or high-quality remote sensing of sensitive areas. Some efforts have been made to provide labeled public domain data, but aside lowresolution data, these activities are not sufficient yet. In this paper, we propose an alternative approach: we transform satellite images into a representation in which features learnt from Internet photography are more meaningful. We show how learnt colorspace transformations can enable significantly more stable transfer learning from ImageNet. As a consequence, small training datasets suffice allowing for significantly more diverse Earth observation applications. We present experiments on high-resolution remote sensing images of airplanes as featured in the 2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation.