讲座主题：Analysis of SENTINEL-2 image series based on neural networks

专家姓名：Fedorov Roman K

工作单位：Russian Academy of Sciences

讲座时间：2025年06月04日 10:30-11:30

讲座地点：数学院大会议室341

主办单位：烟台大学数学与信息科学学院

内容摘要：

This study presents a neural network-based analysis of Sentinel-2 satellite image series, focusing on land cover classification and temporal change detection. The methodology involves six key stages: (1) image markup, where satellite images are cataloged and annotated using polygon objects; (2) image preparation, including resolution standardization (10x10 m) and terrain feature extraction; (3) sample creation, generating 64x64-pixel tensor samples from non-background pixels; (4) clustering and balancing to homogenize sample distribution across classes; (5) model training using Random Forest, ConvNet, ResNet50, and LSTM architectures; and (6) large-scale classification of over 22,000 images (May–September, multi-year) on a GPU cluster (3090/4090/A100). The output, saved in GeoTIFF format, enables analysis of crop dynamics and land transitions. The workflow emphasizes automation, parallel processing, and multi-temporal evaluation, demonstrating scalable applications for environmental monitoring.

主讲人介绍：

Fedorov R.K. 的全名为 Roman K. Fedorov（罗曼·K·费多罗夫），是俄罗斯科学院西伯利亚分院计算中心（IDSCT SB RAS）的研究人员，主要研究方向为遥感图像处理与人工智能应用。其工作聚焦卫星影像的智能解析与大规模计算，成果直接服务于农业监测及生态变迁分析，体现了较强的工程落地能力。Fedorov R.K. 作为主要作者之一，参与开发了基于神经网络的哨兵-2（Sentinel-2）卫星影像时序分析技术。该技术通过六阶段流程实现高效处理，包括影像标注、多模态特征融合（光谱+纹理+地形）、动态样本生成、聚类平衡、多模型训练（如ResNet50、LSTM）及大规模并行分类，最终应用于农作物动态监测与地表覆盖变迁分析。