用多重约束提升火星地形识别精度
Enhancing Martian Terrain Recognition with Deep Constrained Clustering
- 融合空间、深度和时间约束，引导深度聚类
- 在150类中提升聚类同质性16.7%，准确率升至89.86%
- 适合行星地质分析与火星探测任务研究者
火星地形识别对理解地貌、古气候及宜居性至关重要。尽管深度聚类在火星漫游车图像中已展现学习语义一致特征嵌入的潜力，但光照、尺度与旋转的自然变化仍带来分类挑战。为此，我们提出深度约束聚类与度量学习（DCCML）算法，利用多种约束引导聚类过程：基于邻近图块空间与深度相似性的软必须链接约束，以及来自立体相机对和时序相邻图像的硬约束。在好奇号漫游车数据集（150类）上的实验表明，DCCML使同质聚类提升16.7%，达维斯-鲍尔丁指数从3.86降至1.82，检索准确率由86.71%提升至89.86%。该改进显著增强了火星地质特征的精确分类能力，推动了对火星地貌的分析与理解。
原文摘要 · Abstract (English)
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.
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