The diversity of remote sensing (RS) image modalities has expanded alongside advancements in RS technologies. A plethora of optical, multispectral, and hyperspectral RS images offer rich geographic class information. The ability to swiftly access multiple RS image modalities is crucial for fully harnessing the potential of RS imagery. In this work, an innovative method, called Deep Shared Proxy Construction Hashing (DSPCH), is introduced for cross-modal hyperspectral scene target retrieval using accessible RS images such as optical and sketch. Initially, a shared proxy hash code is generated in the hash space for each land use class. Subsequently, an end-to-end deep hash network is built to generate hash codes for hyperspectral pixels and accessible RS images. Furthermore, a proxy hash loss function is designed to optimize the proposed deep hashing network, aiming to generate hash codes that closely resemble the corresponding proxy hash code. Finally, two benchmark datasets are established for cross-modal hyperspectral and accessible RS image retrieval, allowing us to conduct extensive experiments with these datasets. Our experimental results validate that the novel DSPCH method can efficiently and effectively achieve RS image cross-modal target retrieval, opening up new avenues in the field of cross-modal RS image retrieval.

中文翻译：

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用于跨模态遥感图像快速目标检索的深度共享代理构造哈希


随着 RS 技术的进步，遥感 （RS） 图像模态的多样性也随之扩大。大量的光学、多光谱和高光谱 RS 图像提供了丰富的地理类别信息。快速访问多种 RS 图像模态的能力对于充分利用 RS 图像的潜力至关重要。在这项工作中，引入了一种称为深度共享代理构建哈希 （DSPCH） 的创新方法，用于使用可访问的 RS 图像（如光学和草图）进行跨模态高光谱场景目标检索。最初，在哈希空间中为每个土地利用类生成共享代理哈希码。随后，构建端到端深度哈希网络，为高光谱像素和可访问的 RS 图像生成哈希码。此外，设计了一个代理哈希损失函数来优化所提出的深度哈希网络，旨在生成与相应的代理哈希码非常相似的哈希码。最后，建立了两个基准数据集，用于跨模态高光谱和可访问的 RS 图像检索，使我们能够对这些数据集进行广泛的实验。我们的实验结果验证了新型 DSPCH 方法可以高效且有效地实现 RS 图像跨模态目标检索，为跨模态 RS 图像检索领域开辟了新的途径。