发表机构
Computer Vision Center, Universitat Autònoma de Barcelona; Fairfield University; Michigan Technological University; University of Kerala; ESPOL Polytechnic University; University of Mississippi（巴塞罗那自治大学计算机视觉中心; 费尔菲尔德大学; 密歇根理工大学; 喀拉拉大学; ESPOL理工大学; 密西西比大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对火星滑坡分割中形态多变与前景稀疏的挑战，本文提出结合上下文渐进层扩展与Transformer推理的U形网络TransCPLES，在多模态数据集MMLSv2上取得最佳性能。
AI 中文摘要
火星上的自动滑坡分割是理解其地表过程的重要任务之一，并将有助于未来的太空探索。然而，由于滑坡形态高度多变、前景区域通常稀疏或不规则，且轨道观测结合了异质的光谱和地形线索，这仍然是一个相对未被充分探索的开放挑战。在此背景下，本研究通过广泛评估现代神经分割模型，探讨了深度学习解决火星滑坡分割的能力。据我们所知，这是该领域首次提供如此全面探索的研究。我们进一步提出了TransCPLES，一种U形网络，将上下文渐进层扩展特征提取与基于Transformer的上下文推理相结合，使模型能够捕捉局部地貌模式和更广泛的空间依赖性，以实现更可靠的滑坡描绘。在MMLSv2（一个七波段多模态火星滑坡数据集）上的实验表明，当在地理上不同的样本上进行评估时，TransCPLES取得了最佳的整体性能，在不同滑坡范围上具有一致的描绘，稳定的前景判别，以及与几种最先进的基于卷积、注意力和Transformer的分割模型相比，在准确性和计算成本之间取得了有利的平衡。通过这项工作，我们希望提供一个有用的参考，并鼓励在行星遥感深度学习方面的进一步研究和发展。代码将在发表后提供。
英文摘要
Automated landslide segmentation on Mars is one of the important tasks for understanding its surface processes, and all will aid in future space exploration. However, it remains a relatively underexplored open challenge because landslide morphology is highly variable, foreground regions are often sparse or irregular, and orbital observations combine heterogeneous spectral and topographic cues. In this context, this work investigates the capability of deep learning to address Martian landslide segmentation through an extensive assessment of modern neural segmentation models. To the best of our knowledge, this is the first study to provide such a comprehensive exploration in this domain. We further propose TransCPLES, a U-shaped network that couples Contextual Progressive Layer Expansion feature extraction with Transformer-based contextual reasoning, enabling the model to capture local geomorphic patterns and broader spatial dependencies for more reliable landslide delineation. Experiments on MMLSv2, a seven-band multimodal Martian landslide dataset, show that TransCPLES achieves the best overall performance when evaluated on geographically distinct samples, with consistent delineation across different landslide extents, stable foreground discrimination, and a favorable balance between accuracy and computational cost compared with several state-of-the-art convolutional, attention-based, and Transformer-based segmentation models. With this work, we hope to provide a useful reference and encourage further research and development in deep learning for planetary remote sensing. Code will be available after publication.
Comments21 pages, 12 figures
