Search papers, labs, and topics across Lattice.
This paper introduces a deep learning framework using a conditional GAN (cGAN) with a U-Net generator and PatchGAN discriminator to address radiometric inconsistencies in lunar mosaics constructed from Chandrayaan-2 TMC and SELENE data. The cGAN learns a nonlinear mapping to normalize the mosaics against a photometrically consistent LROC WAC reference. Results demonstrate improved tonal uniformity and reduced artifacts compared to histogram-based methods, enabling the creation of higher-fidelity lunar surface maps.
Lunar mosaics riddled with radiometric inconsistencies? A deep learning approach can seamlessly blend multi-mission orbital imagery, outperforming traditional methods.
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.
