对比多种遥感分割方法，提升城市土地利用分类精度
Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover
- 采用查表法大气校正+深度学习模型进行地物分类
- 动态加权的跨伪监督模型显著提高标签可靠性
- 适用于快速城市化区域的规划决策支持
土地利用与土地覆盖（LULC）制图对城市和资源规划至关重要，是建设智慧可持续城市的关键。本研究评估了先进的LULC制图技术，重点使用查表法（LUT）大气校正处理Cartosat多光谱（MX）传感器影像，并结合监督与半监督学习模型进行地物分类。探索了DeeplabV3+与跨伪监督（CPS）模型，其中CPS通过动态加权机制提升训练中伪标签的可靠性。该综合方法分析了不同城市规划应用下的分类准确性和实用性。以印度海得拉巴为例，通过时间序列的Cartosat MX影像分析，揭示了快速城市化带来的显著变化，包括城市扩张、绿地减少和工业区扩展，验证了这些技术在实际规划中的价值。
原文摘要 · Abstract (English)
Land Use Land Cover (LULC) mapping is essential for urban and resource planning, and is one of the key elements in developing smart and sustainable cities.This study evaluates advanced LULC mapping techniques, focusing on Look-Up Table (LUT)-based Atmospheric Correction applied to Cartosat Multispectral (MX) sensor images, followed by supervised and semi-supervised learning models for LULC prediction. We explore DeeplabV3+ and Cross-Pseudo Supervision (CPS). The CPS model is further refined with dynamic weighting, enhancing pseudo-label reliability during training. This comprehensive approach analyses the accuracy and utility of LULC mapping techniques for various urban planning applications. A case study of Hyderabad, India, illustrates significant land use changes due to rapid urbanization. By analyzing Cartosat MX images over time, we highlight shifts such as urban sprawl, shrinking green spaces, and expanding industrial areas. This demonstrates the practical utility of these techniques for urban planners and policymakers.
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