# Change Captioning in Remote Sensing: Evolution to SAT-Cap -- A Single-Stage Transformer Approach

arXiv: https://arxiv.org/abs/2501.08114 · HTML: https://mlsanity.com/paper/2501.08114/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [ReasonCD: A Multimodal Reasoning Large Model for Implicit Change-of-Interest Semantic Mining](https://mlsanity.com/paper/2512.19354/) (2025)
- [Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction](https://mlsanity.com/paper/2605.15558/) (2026)
- [HIMEC: Directional Change Representation and Fixed-Interface Decoding for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2608.12502/) (2026)

## Abstract (arXiv)

Change captioning has become essential for accurately describing changes in multi-temporal remote sensing data, providing an intuitive way to monitor Earth's dynamics through natural language. However, existing change captioning methods face two key challenges: high computational demands due to multistage fusion strategy, and insufficient detail in object descriptions due to limited semantic extraction from individual images. To solve these challenges, we propose SAT-Cap based on the transformers model with a single-stage feature fusion for remote sensing change captioning. In particular, SAT-Cap integrates a Spatial-Channel Attention Encoder, a Difference-Guided Fusion module, and a Caption Decoder. Compared to typical models that require multi-stage fusion in transformer encoder and fusion module, SAT-Cap uses only a simple cosine similarity-based fusion module for information integration, reducing the complexity of the model architecture. By jointly modeling spatial and channel information in Spatial-Channel Attention Encoder, our approach significantly enhances the model's ability to extract semantic information from objects in multi-temporal remote sensing images. Extensive experiments validate the effectiveness of SAT-Cap, achieving CIDEr scores of 140.23% on the LEVIR-CC dataset and 97.74% on the DUBAI-CC dataset, surpassing current state-of-the-art methods. The code and pre-trained models will be available online.