提出混合语义嵌入方法，让遥感图像生成更可控且多样。
Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding
- 用分层语义嵌入协调局部语义布局，无需额外信息。
- 引入语义精炼网络，减少语义混淆和几何坍缩。
- 适合需要高质量合成数据的遥感下游任务使用。
遥感图像语义合成已取得显著进展，但现有方法在语义可控性与多样性之间仍面临严峻挑战。本文提出一种混合语义嵌入引导的生成对抗网络（HySEGGAN），实现可控且高效的遥感图像合成。该方法利用单一源的分层信息，提出一种混合语义嵌入方法，通过细粒度局部语义布局表征遥感目标的几何结构，无需额外信息。同时引入语义精炼网络（SRN），结合新颖的损失函数实现细粒度语义反馈，有效缓解语义混淆并防止几何模式坍缩。实验表明，该方法在语义可控性与多样性间达到良好平衡。此外，HySEGGAN显著提升合成图像质量，在多个数据集上作为数据增强技术在下游任务中取得领先性能。
原文摘要 · Abstract (English)
Significant advancements have been made in semantic image synthesis in remote sensing. However, existing methods still face formidable challenges in balancing semantic controllability and diversity. In this paper, we present a Hybrid Semantic Embedding Guided Generative Adversarial Network (HySEGGAN) for controllable and efficient remote sensing image synthesis. Specifically, HySEGGAN leverages hierarchical information from a single source. Motivated by feature description, we propose a hybrid semantic Embedding method, that coordinates fine-grained local semantic layouts to characterize the geometric structure of remote sensing objects without extra information. Besides, a Semantic Refinement Network (SRN) is introduced, incorporating a novel loss function to ensure fine-grained semantic feedback. The proposed approach mitigates semantic confusion and prevents geometric pattern collapse. Experimental results indicate that the method strikes an excellent balance between semantic controllability and diversity. Furthermore, HySEGGAN significantly improves the quality of synthesized images and achieves state-of-the-art performance as a data augmentation technique across multiple datasets for downstream tasks.
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