融合深度学习与地理信息系统，提升遥感图像分析精度与效率
Fusion of Deep Learning and GIS for Advanced Remote Sensing Image Analysis
- 用CNN和LSTM结合GIS处理遥感数据的时空特征
- 优化后分类准确率从78%升至92%，预测误差降至6%
- 适合环境监测、城市规划等需要时空分析的场景
本文提出一种创新框架，将卷积神经网络（CNN）与长短期记忆网络（LSTM）深度融合地理信息系统（GIS），以提升遥感图像分析的准确性与效率。针对高维数据、复杂模式及时间序列处理难题，采用粒子群优化（PSO）与遗传算法（GA）优化模型参数。实验表明，分类准确率由78%提升至92%，预测误差从12%降低至6%，时间序列分析准确率由75%增至88%。GIS的引入增强了空间分析能力，深化了对地理要素关系的理解。结果证明，深度学习与GIS及优化策略的结合可显著推动遥感应用发展，为环境监测、城市规划与资源管理提供新路径。
原文摘要 · Abstract (English)
This paper presents an innovative framework for remote sensing image analysis by fusing deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, with Geographic Information Systems (GIS). The primary objective is to enhance the accuracy and efficiency of spatial data analysis by overcoming challenges associated with high dimensionality, complex patterns, and temporal data processing. We implemented optimization algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), to fine-tune model parameters, resulting in improved performance metrics. Our findings reveal a significant increase in classification accuracy from 78% to 92% and a reduction in prediction error from 12% to 6% after optimization. Additionally, the temporal accuracy of the models improved from 75% to 88%, showcasing the frameworks capability to monitor dynamic changes effectively. The integration of GIS not only enriched the spatial analysis but also facilitated a deeper understanding of the relationships between geographical features. This research demonstrates that combining advanced deep learning methods with GIS and optimization strategies can significantly advance remote sensing applications, paving the way for future developments in environmental monitoring, urban planning, and resource management.
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