基于光照控制的月球表面精确着陆点定位算法

doi:10.7527/S1000-6893.2025.32874

Abstract

Abstract:

The significant illumination variations on the lunar surface pose a substantial challenge to spacecraft landing site localization. Traditional image processing techniques， such as histogram equalization and Retinex-based methods， struggle to adapt to the highly variable lighting conditions prevalent on planetary surfaces. To address the issue of illumination inconsistency between orbital remote sensing images and descent camera images-which often leads to matching failures in vision-based landing site localization pipelines—a novel localization framework that integrates a dedicated illumination control algorithm named LomFormer （Light on Moon） is proposed. Based on a Transformer architecture， LomFormer is designed to actively control the illumination angle in planetary surface images， effectively transforming an image captured under one lighting condition to appear as if it were taken under another. For model training， a multi-angle illumination dataset of the lunar surface was generated by rendering images based on real lunar Digital Elevation Model （DEM） data under various lighting azimuths and elevations. Training and validation results demonstrate that the proposed method achieves promising performance on real data， significantly enhancing the accuracy of lunar image matching and exhibiting strong robustness across diverse illumination scenarios. This research provides a novel perspective on tackling illumination-related challenges in spacecraft landing site localization and introduces a new paradigm for mapping and localization tasks in future autonomous planetary exploration missions.

Key words: planetary exploration, illumination control, image matching, landing-site localization, deep learning

CLC Number:

Shihao SHU, Cai MENG, Xiangzhi BAI, Zhenwei SHI. Precise lunar landing site localization algorithm based on illumination control[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(10): 532874.

Figures/Tables 9

Fig.1

Fig.2

Table 1

Fig.3

Fig.4

Fig.5

Fig.6

Table 2

Fig.7

References 47

[1]	PIZER S M， AMBURN E P， AUSTIN J D， et al. Adaptive histogram equalization and its variations［J］. Computer Vision， Graphics， and Image Processing， 1987， 39（3）： 355-368.
[2]	JOBSON D J， RAHMAN Z， WOODELL G A. A multiscale retinex for bridging the gap between color images and the human observation of scenes［J］. IEEE Transactions on Image Processing， 1997， 6（7）： 965-976.
[3]	ZHANG Q W L， YANG M H， LI Q L， et al. Lunar farside volcanism 2.8 billion years ago from Chang’e-6 basalts［J］. Nature， 2025， 643（8071）： 356-360.
[4]	CUI Z X， YANG Q， ZHANG Y Q， et al. A sample of the Moon’s far side retrieved by Chang’e-6 contains 2.83-billion-year-old basalt［J］. Science， 2024， 386（6728）： 1395-1399.
[5]	LIU D Y， YE Z， XU Y S， et al. A mismatch removal method based on global constraint and local geometry preservation for lunar orbiter images［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2024， 17： 10221-10236.
[6]	LI X J， YUE Z Y， PENG M， et al. Characterization of lobate scarps at the western rim of the Apollo basin using high-resolution DEMs generated using generative adversarial network （GAN） based approach［J］. Icarus， 2025， 435： 116562.
[7]	GOU S， YUE Z Y， LIN Y T， et al. Thin basaltic regolith at the Chang’e-6 landing site［J］. Earth and Planetary Science Letters， 2025， 655： 119266.
[8]	彭齐浩，赵腾起，刘传凯，等. 一种鲁棒的月面宽基线图像特征匹配方法［J］. 光学学报， 2023， 43（24）： 2410001.
	PENG Q H， ZHAO T Q， LIU C K， et al. A robust feature matching method for wide-baseline lunar images［J］. Acta Optica Sinica， 2023， 43（24）： 2410001 （in Chinese）.
[9]	邸凯昌，刘召芹，刘斌，等. 多源数据的嫦娥四号着陆点定位［J］. 遥感学报， 2019， 23（1）： 177-184.
	DI K C， LIU Z Q， LIU B， et al. Chang’e-4 lander localization based on multi-source data［J］. Journal of Remote Sensing， 2019， 23（1）： 177-184 （in Chinese）.
[10]	刘传凯，王沼翔，雷俊雄，等. 基于松弛极线约束的月面复杂仿射变换图像匹配方法［J］. 航空学报， 2024， 45（2）： 328659.
	LIU C K， WANG Z X， LEI J X， et al. An epipolar relaxation constrained matching algorithm of large-affined images for lunar rover with large span distance in a single movement［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（2）： 328659 （in Chinese）.
[11]	王镓，吴伟仁，李剑，等. 基于视觉的嫦娥四号探测器着陆点定位［J］. 中国科学：技术科学， 2020， 50（1）： 41-53.
	WANG J， WU W R， LI J， et al. Vision based Chang’e-4 landing point localization［J］. Scientia Sinica （Technologica）， 2020， 50（1）： 41-53 （in Chinese）.
[12]	王镓，李达飞，何锡明，等. 基于多源影像的“祝融号”火星车高精度定位［J］. 深空探测学报， 2022， 9（1）： 62-71.
	WANG J， LI D F， HE X M， et al. High precision localization of Zhurong rover based on multi-source images［J］. Journal of Deep Space Exploration， 2022， 9（1）： 62-71 （in Chinese）.
[13]	LIU J J， REN X， YAN W， et al. Descent trajectory reconstruction and landing site positioning of Chang’e-4 on the lunar farside［J］. Nature Communications， 2019， 10： 4229.
[14]	CHEN H， HU X Y， GLÄSER P， et al. CNN-based large area pixel-resolution topography retrieval from single-view LROC NAC images constrained with SLDEM［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2022， 15： 9398-9416.
[15]	陈泽宇，李兆津，吴波，等. 基于多视单目影像光度法的月球南极像素级三维重建［J］. 遥感学报， 2025， 29（2）： 415-428.
	CHEN Z Y， LI Z J， WU B， et al. Multi-view photoclinometry from monocular images for pixel-wise 3D mapping of the lunar South Pole region［J］. National Remote Sensing Bulletin， 2025， 29（2）： 415-428 （in Chinese）.
[16]	CHEN C， YE Z， XU Y S， et al. Large-scale block bundle adjustment of LROC NAC images for lunar south pole mapping based on topographic constraint［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2024， 17： 2731-2746.
[17]	焦春林，高满屯，史仪凯. 基于立体视觉的3D地形拼接［J］. 计算机工程与应用， 2008， 44（23）： 206-208.
	JIAO C L， GAO M T， SHI Y K. 3D terrain registration based on stereo vision［J］. Computer Engineering and Applications， 2008， 44（23）： 206-208 （in Chinese）.
[18]	LI Y， ZENG Y Y. Lunar image matching based on impact crater morphology and distribution for landing localization［C］∥IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium. Piscataway： IEEE Press， 2024： 6146-6149.
[19]	王镓，辛鑫，万文辉，等. 基于多源影像的探测器月面着陆点定位与精度验证［J］. 空间科学学报， 2021， 41（2）： 320-328.
	WANG J， XIN X， WAN W H， et al. Location and accuracy validation of lunar landing point based on multi-source images［J］. Chinese Journal of Space Science， 2021， 41（2）： 320-328 （in Chinese）.
[20]	COLTUC D， BOLON P， CHASSERY J M. Exact histogram specification［J］. IEEE Transactions on Image Processing， 2006， 15（5）： 1143-1152.
[21]	CHEN H T， WANG Y H， GUO T Y， et al. Pre-trained image processing transformer［C］∥2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2021： 12294-12305.
[22]	LI Y W， FAN Y C， XIANG X Y， et al. Efficient and explicit modelling of image hierarchies for image restoration［C］∥2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2023： 18278-18289.
[23]	ZHANG J L， ZHANG Y L， GU J J， et al. Accurate image restoration with attention retractable transformer［EB/OL］. arXiv preprint： 2210.01427， 2022.
[24]	ZAMIR S W， ARORA A， KHAN S， et al. Restormer： Efficient transformer for high-resolution image restoration［C］∥2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2022： 5718-5729.
[25]	LIANG J Y， CAO J Z， SUN G L， et al. SwinIR： Image restoration using swin transformer［C］∥2021 IEEE/CVF International Conference on Computer Vision Workshops （ICCVW）. Piscataway： IEEE Press， 2021： 1833-1844.
[26]	GUO H， LI J M， DAI T， et al. MambaIR： A simple baseline forImage restoration withState-space model［C］∥Computer Vision-ECCV 2024. Cham： Springer， 2025： 222-241.
[27]	GUO H， GUO Y， ZHA Y H， et al. MambaIRv2： Attentive state space restoration［EB/OL］. arXiv preprint： 2411.15269， 2024.
[28]	CAI Y H， LIN J， LIN Z D， et al. MST++： Multi-stage spectral-wise transformer for efficient spectral reconstruction［C］∥2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops （CVPRW）. Piscataway： IEEE Press， 2022： 744-754.
[29]	ZHANG L， LANG Z Q， WANG P， et al. Pixel-aware deep function-mixture network for spectral super-resolution［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2020， 34（7）： 12821-12828.
[30]	XIONG Z W， SHI Z， LI H Q， et al. HSCNN： CNN-based hyperspectral image recovery from spectrally undersampled projections［C］∥2017 IEEE International Conference on Computer Vision Workshops （ICCVW）. Piscataway： IEEE Press， 2018： 518-525.
[31]	SHI Z， CHEN C， XIONG Z W， et al. HSCNN+： Advanced CNN-based hyperspectral recovery from RGB images［C］∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops （CVPRW）. Piscataway： IEEE Press， 2018： 939-947.
[32]	CAI Y H， BIAN H， LIN J， et al. Retinexformer： One-stage retinex-based transformer for low-light image enhancement［C］∥2023 IEEE/CVF International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2024： 12470-12479.
[33]	ZHANG Y H， GUO X J， MA J Y， et al. Beyond brightening low-light images［J］. International Journal of Computer Vision， 2021， 129（4）： 1013-1037.
[34]	XU X G， WANG R X， FU C W， et al. SNR-aware low-light image enhancement［C］∥2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2022： 17693-17703.
[35]	WANG R X， ZHANG Q， FU C W， et al. Underexposed photo enhancement using deep illumination estimation［C］∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2020： 6842-6850.
[36]	SUN S Q， REN W Q， GAO X W， et al. Restoring images in adverse weather conditions via Histogram transformer［C］∥Computer Vision-ECCV 2024. Cham： Springer， 2025： 111-129.
[37]	HAPKE B. Theory of reflectance and emittance spectroscopy［M］. 2nd ed. Cambridge： Cambridge University Press， 2012.
[38]	LIU D Y， CHENG W M， QIAN Z， et al. Boundary delineator for Martian crater instances with geographic information and deep learning［J］. Remote Sensing， 2023， 15（16）： 4036.
[39]	DETONE D， MALISIEWICZ T， RABINOVICH A. SuperPoint： Self-supervised interest point detection and description［C］∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops （CVPRW）. Piscataway： IEEE Press， 2018： 224-236.
[40]	SARLIN P E， DETONE D， MALISIEWICZ T， et al. SuperGlue： Learning feature matching with graph neural networks［C］∥2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2020： 4937-4946.
[41]	China National Space Administration. Chang’e-2 CCD stereoscopic camera DEM-20 m dataset［EB/OL］. ［2025-07-02］. .
[42]	LIU J J， REN X， YAN W， et al. A 76 m per pixel global color image dataset and map of Mars by Tianwen-1［J］. Science Bulletin， 2024， 69（14）： 2183-2186.
[43]	Lunar Reconnaissance Orbiter Camera. Lunar reconnaissance orbiter camera （LROC）［EB/OL］. ［2025-07-02］. .
[44]	PASZKE A， GROSS S， MASSA F， et al. PyTorch： An imperative style， high-performance deep learning library［EB/OL］. arXiv preprint： 1912.01703， 2019.
[45]	HUYNH-THU Q， GHANBARI M. Scope of validity of PSNR in image/video quality assessment［J］. Electronics Letters， 2008， 44（13）： 800-801.
[46]	WANG Z， BOVIK A C， SHEIKH H R， et al. Image quality assessment： From error visibility to structural similarity［J］. IEEE Transactions on Image Processing， 2004， 13（4）： 600-612.
[47]	ZHANG R， ISOLA P， EFROS A A， et al. The unreasonable effectiveness of deep features as a perceptual metric［C］∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2018： 586-595.

模型	R_PSNR	S_SSIM	S_LPIPS
Restormer （基线算法）	19.83	0.632 8	0.290 8
MST++	19.04	0.615 7	0.301 6
RetinexFormer	19.96	0.634 2	0.292 3
Histoformer	18.26	0.595 1	0.321 3
MambaIR	18.67	0.601 3	0.291 0
LomFormer （本文）	20.52	0.645 8	0.286 8

输入	结果	型号	误差/m	集束调整后误差/m
原始输入	失败	嫦娥三号
LomFormer （本文）	成功	嫦娥三号	10.2	2.6
原始输入	失败	嫦娥四号
LomFormer （本文）	成功	嫦娥四号	4.6	1.8
原始输入	失败	嫦娥五号
LomFormer （本文）	成功	嫦娥五号	13.3	2.9

Precise lunar landing site localization algorithm based on illumination control

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 47

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Yuzhuo MA, Kan REN, Tao LI, Qian CHEN. Improving remote sensing image semantic segmentation based on distance loss [J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(8): 332780-332780.
[2]	Jun HUANG, Jing ZHANG, Shiqian WENG. Airborne electro-optical target recognition algorithms [J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(6): 332601-332601.
[3]	Leyan LI, Rennong YANG, Anxin GUO, Qi SONG, Jialiang ZUO. Beyond-visual-range air combat threat prediction and dynamic evasion method based on all-domain fire field theory [J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(4): 332205-332205.
[4]	Zicheng FENG, Wenlong ZHANG, Donghui LIU, Qifeng YU. Robust infrared target tracking algorithm for anti-UAV in complexbackgrounds [J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(4): 332264-332264.
[5]	Shaohua DUAN, Chunjie ZHANG, Chuankai LIU, Xiaolong ZHENG, Jitao ZHANG. AFAR-Net: Autoregressive and feedback-driven adaptive rectangular convolution network for pansharpening [J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(10): 532432-532432.
[6]	Jiepan LI, Wei HE, Minghao TANG, Jin XIONG. Pre-disaster footprint-guilded building damage change detection in spaceborne remote sensing imagery [J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(10): 532845-532845.
[7]	Ye TAO, Jinhui TANG, Zhen YAN, Chen ZHOU, Chong WANG. A trajectory imputation method integrating representation transformation and pattern regression [J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(1): 332106-332106.
[8]	Jianyu XU, Li ZHOU, Zhanxue WANG, Jie SHI, Hao SHI. Calculation method for hypersonic plume infrared radiation based on a fast line-by-line calculation model [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(8): 630778-630778.
[9]	Lingjie MENG, Hongguang LI, Xinjun LI. SAR image simulation method guided by geomorphic category information [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(7): 331003-331003.
[10]	Zhihao ZHAO, Zhaohua YANG, Yun WU, Yuanjin YU. Single-photon counting imaging denoising method based on deep learning in low-light environment [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 630531-630531.
[11]	Yiquan WU, Kang TONG. Research advances on deep learning-based small object detection in UAV aerial images [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 30848-030848.
[12]	Zijian XIANG, Zhenyu MA, Xixiang YANG. Inversion of structural performance parameters of composite materials based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(24): 231877-231877.
[13]	Tianqi FAN, Zhengxia ZOU, Zhenwei SHI. Typical remote sensing target detection with data synthesis based on reinforcement learning [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(23): 631955-631955.
[14]	Kui LIU, Hao SUN, Han WU, Kefeng JI, Gangyao KUANG. Dynamic brightness reconstruction for UAV visible-infrared fusion object detection [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(23): 631968-631968.
[15]	Bo PENG, Jikang BAI, Weiwen CHEN, Xiangtao ZHENG, Jianjun LEI, Xiaoqiang LU. Research progress for UAV search and rescue methods based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(23): 632761-632761.