Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach
Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach
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DOI：
10.1080/01431160903475415
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发表时间：
2010-02
影响因子：
3.4
通讯作者：
Y. Ban;Hongtao Hu;I. Rangel
中科院分区：
文献类型：
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作者：
Y. Ban;Hongtao Hu;I. Rangel
本研究的目的是评估 Quickbird 多光谱 (MS) 数据、多时相 RADARSAT Fine-Beam C-HH 合成孔径雷达 (SAR) 数据以及 Quickbird MS 和 RADARSAT SAR 的融合，以进行城市土地利用/土地覆盖测绘。 Quickbird 多光谱图像的一个场景于 2002 年 7 月 18 日获得，并于 2002 年 5 月至 8 月期间获得了 5 个日期的 RADARSAT 细波束 SAR 图像。 Quickbird MS 图像和 RADARSAT SAR 数据使用基于对象和基于规则的方法进行分类。结果表明，基于对象和基于知识的方法在提取城市土地覆盖类别方面是有效的。为了识别 16 个土地覆盖类别，对 Quickbird MS 数据进行基于对象和规则的分类，总体分类准确度为 87.9%（kappa：0.868）。为了识别 11 个土地覆盖类别，RADARSAT SAR 数据基于对象和基于规则的分类产生的总体准确度为：86.6%（kappa：0.852）。 Quickbird 分类和 RADARSAT SAR 分类的决策级融合能够利用光学和 SAR 数据的最佳分类，从而显着提高了多个土地覆盖类别的分类精度（牧场 25%、大豆 19%、油菜籽 17%），尽管 16 个土地覆盖类别的总体分类精度仅略微提高到 89.5%（kappa：0.885）。
The objective of this research is to evaluate Quickbird multi-spectral (MS) data, multi-temporal RADARSAT Fine-Beam C-HH synthetic aperture radar (SAR) data and fusion of Quickbird MS and RADARSAT SAR for urban land-use/land-cover mapping. One scene of Quickbird multi-spectral imagery was acquired on 18 July 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August 2002. Quickbird MS images and RADARSAT SAR data were classified using an object-based and rule-based approach. The results demonstrated that the object-based and knowledge-based approach was effective in extracting urban land-cover classes. For identifying 16 land-cover classes, object-based and rule-based classification of Quickbird MS data yielded an overall classification accuracy of 87.9% (kappa: 0.868). For identifying 11 land-cover classes, object-based and rule-based classification of RADARSAT SAR data yielded an overall accuracy: 86.6% (kappa: 0.852 ). Decision level fusion of Quickbird classification and RADARSAT SAR classification was able to take advantage of the best classifications of both optical and SAR data, thus significantly improving the classification accuracies of several land-cover classes (25% for pasture, 19% for soybeans, 17% for rapeseeds) even though the overall classification accuracy of 16 land-cover classes increased only slightly to 89.5% (kappa: 0.885).