In-Situ Imagery Generation of the Lunar Surface Based on ECSA-SinGAN

The limited availability of in-situ images of the lunar surface significantly hinders the performance improvement of intelligent algorithms, such as scientific target point-of-interest recognition. To address the low diversity of images generated by traditional data augmentation methods under small-sample conditions, we propose a single-image generative adversarial method based on a blending mechanism of effective channel attention and spatial attention (ECSA-SinGAN). First, an effective channel attention module is introduced to assign different weights to each channel, enhancing the feature representation of important channels. Second, a spatial attention module is employed to assign varying weights to different spatial locations within the image, thereby improving the representation of target regions. Finally, based on a blending mechanism, lunar surface in-situ images are generated step by step, following a pyramidal hierarchy for multi-scale feature extraction. Experimental results show that the proposed method reduces MS-SSIM by 41% compared with SinGAN under identical image quality conditions in the lunar surface in-situ image augmentation task. The method preserves the original image style while significantly improving data diversity, making it effective for small-sample lunar surface in-situ image augmentation.