MsEdF：一种用于遥感图像描述的多流编码器-解码器框架
MsEdF: A Multi-stream Encoder-decoder Framework for Remote Sensing Image Captioning
AI总结：
本文提出MsEdF框架，通过优化编码器-解码器的空域表示和语言生成，提升遥感图像描述的准确性，实验表明其在三个基准数据集上优于现有方法。
摘要：
遥感图像包含复杂的空间模式和语义结构，使描述模型难以准确描述。编码器-解码器架构已成为RSIC的常用方法，但现有方法多依赖单流架构，限制了对图像的准确描述。本文提出一种新的多流编码器-解码器框架（MsEdF），通过优化编码器-解码器的空域表示和语言生成，提升RSIC性能。编码器融合两个互补的图像编码器信息，通过多尺度和结构不同的线索集成促进特征多样性。为提高上下文感知描述的捕捉能力，我们在解码器侧通过堆叠GRU架构和元素级聚合方案优化输入序列的语义建模。在三个基准RSIC数据集上的实验表明，MsEdF优于多个基线模型。
英文摘要：
Remote sensing images contain complex spatial patterns and semantic structures, which makes the captioning model difficult to accurately describe. Encoder-decoder architectures have become the widely used approach for RSIC by translating visual content into descriptive text. However, many existing methods rely on a single-stream architecture, which weakens the model to accurately describe the image. Such single-stream architectures typically struggle to extract diverse spatial features or capture complex semantic relationships, limiting their effectiveness in scenes with high intraclass similarity or contextual ambiguity. In this work, we propose a novel Multi-stream Encoder-decoder Framework (MsEdF) which improves the performance of RSIC by optimizing both the spatial representation and language generation of encoder-decoder architecture. The encoder fuses information from two complementary image encoders, thereby promoting feature diversity through the integration of multiscale and structurally distinct cues. To improve the capture of context-aware descriptions, we refine the input sequence's semantic modeling on the decoder side using a stacked GRU architecture with an element-wise aggregation scheme. Experiments on three benchmark RSIC datasets show that MsEdF outperforms several baseline models.
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