迈向无缝月球马赛克：利用Chandrayaan-2 TMC数据的深度辐射归一化用于跨传感器轨道影像
Towards Seamless Lunar Mosaics: Deep Radiometric Normalization for Cross-Sensor Orbital Imagery Using Chandrayaan-2 TMC Data
AI总结：
本文提出基于深度学习的辐射归一化框架，利用Chandrayaan-2 TMC数据和SELENE辅助影像，解决多任务轨道影像中的辐射不一致问题，提升月球马赛克的结构连续性和视觉一致性。
摘要：
辐射不一致仍是生成无缝月球马赛克的主要挑战，原因在于光照几何、传感器特性和采集条件的差异。本文提出基于深度学习的辐射归一化框架，主要使用ISRO的Chandrayaan-2地形测绘相机（TMC）数据，辅以SELENE（Kaguya）任务的辅助影像。所提方法采用基于U-Net的生成器和PatchGAN判别器的条件生成对抗网络（cGAN），学习从传统拼接月球影像到由LROC宽角相机（WAC）数据衍生的光致一致参考的非线性辐射映射。采用基于块的训练策略与重叠感知推理，以实现大规模区域马赛克的可扩展处理，同时保持拼接块边界处的结构连续性。定量评估使用结构相似性指数（SSIM）、峰值信噪比（PSNR）和均方根误差（RMSE）显示，与传统直方图归一化技术相比，有显著提升。所提框架实现了增强的色调均匀性、减少的拼接伪影以及多源月球数据集中的结构一致性。这些结果突显了基于学习的辐射归一化在大规模行星马赛克中的有效性，并展示了其在生成高保真月球表面地图方面的潜力。
英文摘要：
Radiometric inconsistencies remain a major challenge in generating seamless lunar mosaics from multi-mission orbital imagery due to variability in illumination geometry, sensor characteristics, and acquisition conditions. This paper presents a deep learning-based radiometric normalization framework for multi-mission lunar mosaics constructed primarily from ISRO's Chandrayaan-2 Terrain Mapping Camera (TMC) data, supplemented with auxiliary imagery from the SELENE (Kaguya) mission. The proposed approach employs a conditional generative adversarial network (cGAN) comprising a U-Net-based generator and a PatchGAN discriminator to learn a nonlinear radiometric mapping from conventionally mosaicked lunar imagery to a photometrically consistent reference derived from LROC Wide Angle Camera (WAC) data. A patch-based training strategy with overlap-aware inference is adopted to enable scalable processing of large-area mosaics while preserving structural continuity across tile boundaries. Quantitative evaluation using Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE) demonstrates consistent improvements over traditional histogram-based normalization techniques. The proposed framework achieves enhanced tonal uniformity, reduced seam artifacts, and improved structural coherence across multi-source lunar datasets. These results highlight the effectiveness of learning-based radiometric normalization for large-scale planetary mosaicking and demonstrate its potential for generating high-fidelity lunar surface maps from heterogeneous orbital imagery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
