利用深度约束聚类增强火星地形识别
Enhancing Martian Terrain Recognition with Deep Constrained Clustering
AI总结：
针对火星探测车图像中地形受亮度、尺度等变化影响难以精准分类的问题，提出融合多类约束的DCCML深度约束聚类算法，在好奇号数据集上有效提升了聚类同质性、检索准确率等指标，助力火星地貌分析。
摘要：
火星地形识别对于深化我们对火星地形、地貌、古气候和宜居性的认识至关重要。尽管深度聚类方法在从火星探测车图像中学习语义同质的特征嵌入方面展现出潜力，但亮度、尺度和旋转的自然变化给精准地形分类带来了重大挑战。为解决这些局限，我们提出了带度量学习的深度约束聚类算法（Deep Constrained Clustering with Metric Learning，DCCML），这是一种利用多种约束类型引导聚类过程的新算法。DCCML融合了从相邻图像块的空间和深度相似性推导而来的软必连约束，以及来自立体相机对和时间相邻图像的硬约束。在拥有150个聚类的好奇号（Curiosity）探测车数据集上的实验评估表明，DCCML使同质聚类数量提升了16.7%，同时将Davies-Bouldin指数从3.86降至1.82，并将检索准确率从86.71%提升至89.86%。这一改进实现了火星地质特征的更精准分类，提升了我们分析和理解这颗行星地貌的能力。
英文摘要：
Martian terrain recognition is pivotal for advancing our understanding of topography, geomorphology, paleoclimate, and habitability. While deep clustering methods have shown promise in learning semantically homogeneous feature embeddings from Martian rover imagery, the natural variations in intensity, scale, and rotation pose significant challenges for accurate terrain classification. To address these limitations, we propose Deep Constrained Clustering with Metric Learning (DCCML), a novel algorithm that leverages multiple constraint types to guide the clustering process. DCCML incorporates soft must-link constraints derived from spatial and depth similarities between neighboring patches, alongside hard constraints from stereo camera pairs and temporally adjacent images. Experimental evaluation on the Curiosity rover dataset (with 150 clusters) demonstrates that DCCML increases homogeneous clusters by 16.7 percent while reducing the Davies-Bouldin Index from 3.86 to 1.82 and boosting retrieval accuracy from 86.71 percent to 89.86 percent. This improvement enables more precise classification of Martian geological features, advancing our capacity to analyze and understand the planet's landscape.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
