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.

翻译：遥感图像包含复杂的空间模式和语义结构，这使得描述模型难以准确刻画。编码器-解码器架构已成为遥感图像描述的主流方法，其通过将视觉内容转换为描述性文本来实现。然而，现有方法多依赖单流架构，削弱了模型准确描述图像的能力。此类单流架构通常难以提取多样化的空间特征或捕获复杂的语义关系，在类内相似度高或上下文模糊的场景中效果受限。本文提出一种新颖的多流编码器-解码器框架，通过优化编码器-解码器架构的空间表征与语言生成能力来提升遥感图像描述性能。编码器融合了两个互补图像编码器的信息，通过整合多尺度及结构差异的视觉线索以增强特征多样性。为提升上下文感知描述的捕获能力，我们在解码器端采用基于逐元素聚合策略的堆叠GRU架构，以优化输入序列的语义建模。在三个遥感图像描述基准数据集上的实验表明，MsEdF在多项指标上优于现有基线模型。