References

1. 1.
   Cao H, Wang Y Y, Chen J, Jiang D S, Zhang X P, Tian Q and Wang M N. 2021. Swin-Unet: Unet-like pure transformer for medical image segmentation. arXiv: 2105.05537
2. 2.
   Chen F X. 2008. Image segmentation technology based on region growing method. Science and Technology Information, (15): 58-59
3. 3.
   Chen G P, Li L, Zhang J X and Dai Y. 2023. Rethinking the unpretentious U-Net for medical ultrasound image segmentation. Pattern Recognition, 142: 109728
4. 4.
   Chen J N, Lu Y Y, Yu Q H, Luo X D, Adeli E, Wang Y, Lu L, Yuille A L and Zhou Y Y. 2021. TransUNet: transformers make strong encoders for medical image segmentation. arXiv: 2102.04306
5. 5.
   Chen L C, Zhu Y K, Papandreou G, Schroff F and Adam H. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv: 1802.02611
6. 6.
   Ding S F, Qi B J and Tan H Y. 2011. An overview on theory and algorithm of support vector machines. Journal of University of Electronic Science and Technology of China, 40(1): 2-10
7. 7.
   Duan R L, Li Q X and Li Y H. 2005. Summary of image edge detection. Optical Technique, 31(3): 415-419
8. 8.
   Fang K N, Wu J B, Zhu J P and Xie B C. 2011. A review of technologies on random forests. Statistics and Information Forum, 26(3): 32-38
9. 9.
   Gou S, Yue Z Y, Di K C and Niu S L. 2021. Rampart craters in the Isidis Planitia, Mars: remote sensing analysis and environment implications. National Remote Sensing Bulletin, 25(7): 1374-1384
10. 10.
    Han S Q and Wang L. 2002. A survey of thresholding methods for image segmentation. Systems Engineering and Electronics, 24(6): 91-94, 102
11. 11.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE: 770-778
12. 12.
    Hendrycks D and Gimpel K. 2023. Gaussian error linear units (GELUs). arXiv:1606.08415
13. 13.
    Jain A K. 2008. Data clustering: 50 years beyond K-means//Daelemans W, Goethals B and Morik K, eds. Machine Learning and Knowledge Discovery in Databases. Antwerp: Springer: 3-4
14. 14.
    Jia Y T, Su Z J, Wan G, Liu L and Liu J. 2023. AE-TransUNet+: an enhanced hybrid transformer network for detection of lunar south small craters in LRO NAC images. IEEE Geoscience and Remote Sensing Letters, 20: 6007405
15. 15.
    Jia Y T, Wan G, Li W H, Li C, Liu J, Cong D W and Liu L. 2024. EDR-TransUnet: integrating enhanced dual relation-attention with transformer U-Net for multiscale rock segmentation on mars. IEEE Transactions on Geoscience and Remote Sensing, 62: 4601416
16. 16.
    Li H C, Qiu L W, Li Z, Meng B, Huang J B and Zhang Z M. 2021. Automatic rocks segmentation based on deep learning for planetary rover images. Journal of Aerospace Information Systems, 18(11): 755-761
17. 17.
    Lin T Y, Goyal P, Girshick R, He K M and Dollár P. 2020. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2): 318-327
18. 18.
    Liu H Q, Yao M B, Xiao X M and Xiong Y G. 2023. RockFormer: a U-shaped transformer network for Martian rock segmentation. IEEE Transactions on Geoscience and Remote Sensing, 61: 4600116
19. 19.
    Liu Z, Lin Y T, Cao Y, Hu H, Wei Y X, Zhang Z, Lin S and Guo B N. 2021. Swin transformer: hierarchical vision transformer using shifted windows. arXiv: 2103.14030
20. 20.
    Liu Z Q, Wan W H, Peng M, Zhao Q, Xu B, Liu B, Liu Y L, Di K C, Li L C, Yu T Y, Wang B F, Zhou J L and Chen H M. 2014. Remote sensing mapping and localization techniques for teleoperation of Chang’e-3 rover. Journal of Remote Sensing (in Chinese), 18(5): 971-980
21. 21.
    Long L H, Zhu Y T, Yan J W, Liu J J and Wang Z Y. 2023. New building extraction method based on semantic segmentation. National Remote Sensing Bulletin, 27(11): 2593-2602
22. 22.
    Lv W K, Wei L H, Zheng D, Liu Y and Wang Y M. 2023a. MarsNet: automated rock segmentation with transformers for Tianwen-1 mission. IEEE Geoscience and Remote Sensing Letters, 20: 3506605
23. 23.
    Lv Z Y, Huang H T, Sun W W, Lei T, Benediktsson J A and Li J H. 2023b. Novel enhanced UNet for change detection using multimodal remote sensing image. IEEE Geoscience and Remote Sensing Letters, 20: 2505405
24. 24.
    Ma C H, Li Y, Lv J Y, Xiao Z X, Zhang W M and Mo L S. 2024. Automated rock detection from mars rover image via Y-shaped dual-task network with depth-aware spatial attention mechanism. IEEE Transactions on Geoscience and Remote Sensing, 62: 4600418
25. 25.
    Mao A Q, Mohri M and Zhong Y T. 2023. Cross-entropy loss functions: theoretical analysis and applications. arXiv:2304.07288
26. 26.
    Min L T, Fan Z M, Xie X X and Lü Q Y. 2023. Feature reassembly and self-attention for oriented object detection in remote sensing images. National Remote Sensing Bulletin, 27(12): 2716-2725
27. 27.
    Morid M A, Borjali A and Del Fiol G. 2021. A scoping review of transfer learning research on medical image analysis using ImageNet. Computers in Biology and Medicine, 128: 104115
28. 28.
    Nair V and Hinton G E. 2010. Rectified linear units improve restricted Boltzmann machines//Proceedings of the 27th International Conference on Machine Learning. Haifa: Omnipress: 807-814
29. 29.
    Ogohara K and Gichu R. 2022. Automated segmentation of textured dust storms on mars remote sensing images using an encoder-decoder type convolutional neural network. Computers and Geosciences, 160: 105043
30. 30.
    Qian S, Chen Z H, Lin M Q and Zhang C B. 2015. Saliency detection based on conditional random field and image segmentation. Acta Automatica Sinica, 41(4): 711-724
31. 31.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation. arXiv: 1505.04597
32. 32.
    Rozenberszki D, Litany O and Dai A. 2024. UnScene3D: unsupervised 3D instance segmentation for indoor scenes//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE: 19957-19967
33. 33.
    Shelhamer E, Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651
34. 34.
    Shen Y, Pan D, Cao H T, Yuan B F, Jia Y, He L B and Zou M. 2025. Terrain classification and rock abundance analysis at Utopia Planitia using Zhurong image data based on deep learning algorithms. Journal of Terramechanics, 117: 101022
35. 35.
    Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
36. 36.
    Song B G, Shao P, Shao W, Zhang X D and Dong T. 2025. A two-branch building extraction network integrating main body and edge separation and multi-scale information extraction. National Remote Sensing Bulletin, 29(8): 1-13
37. 37.
    Sudre C H, Li W Q, Vercauteren T, Ourselin S and Cardoso M J. 2017. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations//Proceedings of the 3rd International Workshop, DLMIA 2017, and 7th International Workshop on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Québec City: Springer: 240-248
38. 38.
    Vaswani A, Shazeer A, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł and Polosukhin I. 2017. Attention is all you need//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc.: 6000-6010
39. 39.
    Xie E Z, Wang W H, Yu Z D, Anandkumar A, Alvarez J M and Luo P. 2021. SegFormer: simple and efficient design for semantic segmentation with transformers//Proceedings of the 35th International Conference on Neural Information Processing Systems. Virtual Event: Curran Associates Inc.: 12077-12090
40. 40.
    Xie J, Yan K, Kang Z Z, Xu X J, Xue B, Yang J F and Tao J Y. 2021. Verification study for the mineral and rock identification using multispectral camera of the “Zhurong” Mars Rover on the earth. National Remote Sensing Bulletin, 25(7): 1385-1399
41. 41.
    Xiong Y G, Xiao X M, Yao M B, Liu H Q, Yang H and Fu Y G. 2023. MarsFormer: Martian rock semantic segmentation with transformer. IEEE Transactions on Geoscience and Remote Sensing, 61: 4600612
42. 42.
    Yang C, Zhao H S, Bruzzone L, Benediktsson J A, Liang Y C, Liu B, Zeng X G, Guan R C, Li C L and Ouyang Z Y. 2020. Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning. Nature Communications, 11(1): 6358
43. 43.
    Yi Q, Zhang Y H, Zong Y T and Dai Y B. 2023. A survey of hyperspectral image classification algorithms based on convolutional neural networks. Electronics Optics and Control, 30(3): 70-77
44. 44.
    Yu C Q, Wang J B, Gao C X, Yu G, Shen C H and Sang N. 2020. Context prior for scene segmentation//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE: 12413-12422
45. 45.
    Yuan Y H, Chen X L and Wang J D. 2020. Object-contextual representations for semantic segmentation//Vedaldi A, Bischof H, Brox T and Frahm J M, eds. 16th European Conference Computer Vision-ECCV 2020. Glasgow: Springer: 173-190
46. 46.
    Zhao H S, Shi J P, Qi X J, Wang X G and Jia J Y. 2017. Pyramid scene parsing network. arXiv:1612.01105
47. 47.
    Zheng S X, Lu J C, Zhao H S, Zhu X T, Luo Z K, Wang Y B, Fu Y W, Feng J F, Xiang T, Torr P H S and Zhang L. 2021. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE: 6877-6886
48. 48.
    Zheng W, Kang G W, Chen W F and Li X W. 2008. Unsupervised segmentation of remote sensing images based on fuzzy Markov random field model. Journal of Remote Sensing (in Chinese), 12(2): 246-252
49. 49.
    Zheng Z, Zhong Y F, Wang J J and Ma A L. 2020. Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE: 4095-4104