  Remote sensing image-text retrieval constitutes a foundational aspect of
remote sensing interpretation tasks, facilitating the alignment of vision and
language representations. This paper introduces a prior instruction
representation (PIR) learning paradigm that draws on prior knowledge to
instruct adaptive learning of vision and text representations. Based on PIR, a
domain-adapted remote sensing image-text retrieval framework PIR-ITR is
designed to address semantic noise issues in vision-language understanding
tasks. However, with massive additional data for pre-training the
vision-language foundation model, remote sensing image-text retrieval is
further developed into an open-domain retrieval task. Continuing with the
above, we propose PIR-CLIP, a domain-specific CLIP-based framework for remote
sensing image-text retrieval, to address semantic noise in remote sensing
vision-language representations and further improve open-domain retrieval
performance. In vision representation, Vision Instruction Representation (VIR)
based on Spatial-PAE utilizes the prior-guided knowledge of the remote sensing
scene recognition by building a belief matrix to select key features for
reducing the impact of semantic noise. In text representation, Language Cycle
Attention (LCA) based on Temporal-PAE uses the previous time step to cyclically
activate the current time step to enhance text representation capability. A
cluster-wise Affiliation Loss (AL) is proposed to constrain the inter-classes
and to reduce the semantic confusion zones in the common subspace.
Comprehensive experiments demonstrate that PIR could enhance vision and text
representations and outperform the state-of-the-art methods of closed-domain
and open-domain retrieval on two benchmark datasets, RSICD and RSITMD.
