More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

Classification and identification of the materials lying over or beneath the earth's surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS), and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also…

• NN
  National Natural Science Foundation of China

  Awards: 41722108, 91638201
• AR
  AXA Research Fund
• JS
  Japan Society for the Promotion of Science

  Award: KAKENHI 18K18067