SCIENCE@home

TDNet: Bidirectional LSTM Gated Triple-Decoder Network for Remote Sensing Change Detection

Cui Zhang,Zhouyang Sha,Hailong Wang

Published 2025 in IEEE Transactions on Geoscience and Remote Sensing

ABSTRACT

Remote sensing change detection (CD) identifies land-use/land-cover changes by analyzing multitemporal imagery. Mainstream methods employ Siamese multilevel encoders and a single multilevel decoder: encoders extract multiscale features, while the decoder fuses them to generate change maps. However, the effective exploitation of multiscale features in existing methods remains limited, often falling short in fully unleashing the discriminative potential at each scale and in adequately achieving synergistic cross-scale fusion. To address this limitation, we propose the bidirectional long short-term memory (LSTM) gated triple-decoder network (TDNet), which introduces two innovations: 1) triple-decoder architecture: we abandon the single-decoder paradigm and construct three cascaded decoder branches of varying depths (two-four layers). Each branch specializes in processing features at its corresponding level—shallow, middle, or deep—fully unleashing the discriminative potential at every scale and 2) bidirectional LSTM-based multiscale feature interaction module (BLMIM): leveraging LSTM’s gating mechanism, BLMIM extends the bidirectional LSTM to handle multiscale feature interactions: it adaptively retains upstream features via the forget gate, filters current-layer information via the input gate, and controls contributions to subsequent layers via the output gate. This yields interpretable multiscale interactions and, to the authors’ knowledge, constitutes the first adaptation of LSTM to supervised CD. Comprehensive experiments on LEVIR-CD, CDD, and WHU-CD demonstrate that TDNet surpasses state-of-the-art methods, achieving $F1$ improvements of 0.60%, 0.49%, and 0.50%, respectively.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    IEEE Transactions on Geoscience and Remote Sensing

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/TGRS.2025.3644652
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-48 of 48 references · Page 1 of 1

CITED BY

Showing 1-13 of 13 citing papers · Page 1 of 1