利用SAR、多光谱和高光谱影像进行城市弱势住区的多传感器制图：以阿根廷科尔多瓦为例
Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in Córdoba, Argentina
浏览论文内容
中文总结 AI 辅助
本研究提出多传感器深度学习框架，整合SAR、多光谱、高光谱影像，以阿根廷科尔多瓦为例制图非正规住区，发现晚期融合结合高光谱数据效果最优，还可解释城市脆弱性模式。
中文摘要 AI 辅助
非正规住区是快速扩张城市面临的重大城市挑战，但由于其外观异质性和官方清单不完整，从地球观测（EO）数据中识别它们仍然困难。本研究提出了一种用于阿根廷科尔多瓦贫民窟可能性制图的多传感器深度学习（DL）框架，整合了高分辨率PlanetScope多光谱（MS）影像、COSMO-SkyMed（CSK）合成孔径雷达（SAR）数据和中等分辨率PRISMA高光谱（HS）观测数据。该问题被表述为基于斑块级别的分类任务，以官方的Registro Nacional de Barrios Populares（ReNaBaP，国家平民社区登记册）清单作为参考，模型通过四个地理分区的折叠进行评估。系统比较了仅使用SAR和仅使用MS的基线模型、它们结合PRISMA HS支持的配置，以及早期融合（EF）、中期融合（MF）和晚期融合（LF）策略。结果显示，LF+HS在分类性能和空间选择性之间提供了最佳整体平衡，而PRISMA与更高分辨率的MS和SAR表征一起贡献了互补的光谱信息。除了针对ReNaBaP的标准评估外，还使用了一个外部市政脆弱性层来解释官方多边形之外的检测结果，表明几个明显的假阳性与更广泛的弱势城市区域重叠。热分析进一步显示，在热浪事件期间，ReNaBaP住区的地表温度明显高于其直接周边区域，表明存在局部地表热放大。综合来看，这些结果表明，多传感器EO融合既可以支持ReNaBaP住区的制图，也可以支持更广泛城市脆弱性模式的解释。
英文摘要
Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains difficult because of their heterogeneous appearance and incomplete official inventories. This work presents a multi-sensor deep learning (DL) framework for slum-likelihood mapping in Córdoba, Argentina, integrating high-resolution PlanetScope multispectral (MS) imagery, COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) data, and medium-resolution PRISMA hyperspectral (HS) observations. The problem is formulated as a patch-level classification task using the official Registro Nacional de Barrios Populares (ReNaBaP) inventory as reference, and the models are evaluated through four geographically partitioned folds. SAR-only and MS-only baselines, their configurations with PRISMA HS support, and early fusion (EF), middle fusion (MF), and late fusion (LF) strategies are systematically compared. Results show that LF+HS provides the best overall balance between classification performance and spatial selectivity, while PRISMA contributes complementary spectral information alongside the higher-resolution MS and SAR representations. Beyond the standard evaluation against ReNaBaP, an external municipal vulnerability layer is used to interpret detections outside the official polygons, showing that several apparent false positives overlap broader vulnerable urban areas. Thermal analysis further shows that ReNaBaP settlements exhibit significantly higher surface temperatures than their immediate surroundings during a heatwave event, indicating localised surface-heat amplification. Taken together, these results suggest that multi-sensor EO fusion can support both the mapping of ReNaBaP settlements and the interpretation of broader urban vulnerability patterns.
发表机构
- University of Pavia（帕维亚大学）
- Instituto Gulich（古利奇研究所）
- University of Sannio（萨尼奥大学）
机构由 AI 辅助整理，请以论文原文为准。
