Dual Adversarial Networks for Land-Cover Classification

River basin scene classification as an important application in the field of land-cover recognition has been arousing extensive concern. Traditional land-cover classification methods with multi-feature extractions on specific scene perform well on a single river basin, however, poorly address inter-basin classification owing to the varied texture shown in satellite images cross river basins (e.g., topography and climates). Current transfer learning approaches with domain adaptation, which can shorten the discrepancies between two river basins, pay less attention to diversity of multi-feature extractions given by remote sensing images, which may lead to negative transfer. To better address the above challenges, this paper proposes a model known as Dual Adversarial Networks for Land-cover Classification (DANLC). Our DANLC architecture consists of two domain adversarial networks in a paralleled structure, namely RGB and texture networks, for multi-feature extractions, which are able to capture the underlying representation of satellite images from different perspectives and get invariable transfer component. Results demonstrate the outstanding performance of our model in both the classification effect and robustness compared with traditional methods and state-of-the-art transfer learning approaches.