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A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

机译:光学和雷达遥感数据融合在土地利用制图和监测中的应用综述

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摘要

The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300–3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales.
机译:遥感任务可提供的大量补充数据可以极大地帮助人们努力准确地确定土地利用并量化土地利用管理或强度的细微变化。这项研究回顾了112项关于融合光学和雷达数据的研究,这些数据提供了独特的光谱和结构信息,用于土地覆盖和使用评估。与我们的预期相反,只有50项研究专门针对土地利用,五项评估了土地利用变化,而大多数研究针对土地覆盖。在32项研究中评估了融合对土地利用分析的优势,并且绝大多数(28项研究)得出结论,与使用单一数据源相比,融合改善了结果。研究地点很小,通常为300-3000 km 2或个别地块,缺乏结果和地点之间准确性的比较。尽管使用了多种融合技术,但在传统的分类算法(例如,高斯最大似然分类)中,预先分类融合后再进行像素级输入是很常见的,但是在该方法对土地利用的适用性方面通常没有具体理由主题正在研究中。该研究领域的进展要求开发强大的融合技术以绘制复杂的土地利用和土地变化图,并需要系统的程序来评估较大空间尺度上融合的益处。

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