Multisource Data Reconstruction-Based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval

Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then use these features to construct supervised information (e.g., pseudo-labels) to train hashing networks. Existing methods usually regard RS images as natural images to extract unisource features. However, these features only contain partial information about ground objects and cannot produce reliable pseudo-labels. In addition, existing methods only generate a pseudo single-label to annotate each RS image, which cannot accurately represent multiple scenes in a RS image. To address these drawbacks, this paper proposes a new Multisource data reconstruction-based deep unsupervised Hashing method, called MrHash,…

• ES
  European Space Agency

  Award: 59333
• NN
  National Natural Science Foundation of China

  Awards: 62272130, 62101371
• NR
  National Remote Sensing Centre

  Award: 59333
• SS
  Shenzhen Science and Technology Innovation Program

  Awards: JCYJ20200109113014456, KCXFZ20211020163403005, JCYJ20210324120208022