发表机构
INGV-EO; Thales Alenia Space Italia; Euro-Mediterranean Center on Climate Change; Università di Catania; INFN, sezione di Catania（意大利国家地球物理与火山学研究所EO部门; 泰雷兹阿莱尼亚宇航公司意大利分部; 欧洲地中海气候变化中心; 卡塔尼亚大学; 意大利国家核物理研究院卡塔尼亚分部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对星上处理场景的局限，提出混合量子AlexNet模型，结合经典卷积与参数化量子电路，实现全球跨传感器火山热活动识别，提升了特征区分度与模型鲁棒性。
AI 中文摘要
随着地球观测（EO）进入大数据时代，每日卫星图像的指数级增长给经典深度学习（DL）模型带来了显著的计算与存储挑战。此外，现有方法往往难以在异构传感器和火山环境间实现泛化，且需要大量标注数据集与充足的计算资源，这些限制对新兴的星上处理（OBP）应用尤为关键——该场景下内存、计算能力与标注数据均存在固有局限。本研究提出一种混合量子AlexNet架构，用于全球尺度跨传感器火山热活动识别。该模型结合经典卷积主干网络以提取高级空间特征，同时将参数化量子电路（PQC）作为变分层；通过将高级图像表示嵌入高维希尔伯特空间，量子层学习任务特定表示以增强特征区分度。实验结果表明，与经典对应模型相比，所提混合量子模型使用更少的可训练参数与更少的训练数据，即可学习到更具区分度的特征表示，进而提升跨传感器迁移能力与异构火山环境下的鲁棒性。
英文摘要
As Earth Observation (EO) enters the Big Data era, the exponential volume of daily satellite imagery poses significant computational and storage challenges for classical Deep Learning (DL) models. Moreover, current approaches often struggle to generalize across heterogeneous sensors and volcanic environments while requiring large labeled datasets and substantial computational resources. These limitations are particularly critical for emerging On-Board Processing (OBP) applications, where memory, computational power, and annotated data are inherently limited. This work proposes a Hybrid Quantum AlexNet architecture for cross-sensor recognition of volcanic thermal activity at the global scale. The proposed model combines a classical convolutional backbone for high-level spatial features extraction with a parameterized quantum circuit (PQC) acting as a variational layer. By embedding high-level image representations into a high-dimensional Hilbert space, the quantum layer learns task-specific representations that enhance feature discrimination. Experimental results demonstrate that the proposed hybrid quantum model learns more discriminative feature representations, leading to improved cross-sensor transferability and robustness across heterogeneous volcanic environments using fewer trainable parameters and reduced training data than its classical counterpart.
