A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method
A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method
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DOI：
10.1109/tgrs.2017.2692802
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发表时间：
2017-08-01
影响因子：
8.2
通讯作者：
Zhang, Liangpei
中科院分区：
文献类型：
--
作者：
Cheng, Qing;Liu, Huiqing;Zhang, Liangpei
遥感仪器在空间分辨率和时间频率之间的权衡限制了我们有效监测空间和时间动态的能力。时空数据融合技术通过融合多个具有不同优势或特征的传感器的观测数据，可以获得高空间分辨率和高时间频率的遥感数据。在本文中，我们开发的空间和时间非局部滤波器为基础的融合模型（STNLFFM），以提高预测能力和精度，特别是对复杂的变化景观。STNLFFM方法提供了一种新的转换关系，从同一传感器在不同的日期获得的粗分辨率反射率数据的帮助下，精细分辨率的反射率图像之间，并充分利用遥感图像序列中的高度时空冗余，以产生最终的预测。在科伦巴利灌溉区研究站点和下格维迪尔集水区研究站点对所提出的方法进行了测试。结果表明，该方法可以提供一个更准确和鲁棒的预测，特别是异质景观和时间动态的地区。
The tradeoff in remote sensing instruments that balances the spatial resolution and temporal frequency limits our capacity to monitor spatial and temporal dynamics effectively. The spatiotemporal data fusion technique is considered as a cost-effective way to obtain remote sensing data with both high spatial resolution and high temporal frequency, by blending observations from multiple sensors with different advantages or characteristics. In this paper, we develop the spatial and temporal nonlocal filter-based fusion model (STNLFFM) to enhance the prediction capacity and accuracy, especially for complex changed landscapes. The STNLFFM method provides a new transformation relationship between the fine-resolution reflectance images acquired from the same sensor at different dates with the help of coarse-resolution reflectance data, and makes full use of the high degree of spatiotemporal redundancy in the remote sensing image sequence to produce the final prediction. The proposed method was tested over both the Coleambally Irrigation Area study site and the Lower Gwydir Catchment study site. The results show that the proposed method can provide a more accurate and robust prediction, especially for heterogeneous landscapes and temporally dynamic areas.