可靠的视图选择对于基于卫星影像的深度多视图立体（MVS）重建至关重要。然而，现有策略主要依赖采集元数据或简化的几何准则，因此无法充分表征复杂几何、辐射和时间条件下卫星视图的异构可靠性。本文提出了一种可靠性感知的视图选择框架，通过联合考虑几何强度、辐射一致性、RPC 导出的几何稳定性以及时间可靠性来评估候选源视图。与传统的基于角度的几何准则不同，所提出的框架刻画了 RPC 导出的极线方向在图像域内的空间变化，从而为识别更可靠的多视图配置提供互补的几何信息。在 US3D 基准上的实验表明，与传统元数据驱动和基于模拟的选择策略相比，该框架在 MAE 和 PAG1 指标上提升了重建性能，MAE 降低高达 9.9%，1 米精度网格百分比（PAG1）提升高达 8.5%。RMSE 表现出区域依赖性行为，在 JAX 区域降低而在 OMA 区域升高。在 MVS3D 基准上的额外评估以及使用两种卫星 MVS 架构进行的跨骨干网络实验进一步支持了所提框架在评估的城市环境和重建架构中的可迁移性。

Reliable view selection is essential for accurate deep multi-view stereo (MVS) reconstruction from satellite imagery. However, existing strategies mainly rely on acquisition metadata or simplified geometric criteria and therefore cannot adequately characterize the heterogeneous reliability of satellite views under complex geometric, radiometric, and temporal conditions. This paper proposes a reliability-aware view selection framework that evaluates candidate source views by jointly considering geometric strength, radiometric consistency, RPC-derived geometric stability, and temporal reliability. Unlike conventional angle-based geometric criteria, the proposed framework characterizes the spatial variation in RPC-derived epipolar orientations across the image domain, thereby providing complementary geometric information for identifying more reliable multi-view configurations. Experiments on the US3D benchmark show that the proposed framework improves reconstruction performance in terms of MAE and PAG1 relative to conventional metadata-driven and simulation-based selection strategies, reducing MAE by up to 9.9% and increasing the Percentage of Accurate Grids at 1 m (PAG1) by up to 8.5%. RMSE exhibits region-dependent behavior, decreasing in JAX but increasing in OMA. Additional evaluations on the MVS3D benchmark and cross-backbone experiments using two satellite MVS architectures further support the transferability of the proposed framework across the evaluated urban environments and reconstruction architectures.