🤖 AI Summary
This study addresses the challenge of radiometric inconsistency in multi-mission lunar orbital imagery caused by variations in illumination, sensors, and imaging conditions, which hinders seamless mosaicking. The work proposes the first cross-mission radiometric normalization framework based on conditional generative adversarial networks (cGANs), mapping Chandrayaan-2 Terrain Mapping Camera (TMC) data—augmented with SELENE imagery—as inputs to the Lunar Reconnaissance Orbiter Camera Wide Angle Camera (LROC WAC) reference standard. The approach employs a U-Net generator and a PatchGAN discriminator, combined with a patch-based training scheme and an overlap-aware inference strategy to preserve large-scale structural continuity while effectively eliminating visible seams. Experimental results demonstrate that the method significantly outperforms conventional histogram matching in terms of SSIM, PSNR, and RMSE metrics, substantially improving tonal uniformity and structural consistency across multi-source lunar images.
📝 Abstract
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.