| 一种自适应三维核回归的遥感时空融合方法 |
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| 引用本文： | 卓国浩,吴波,朱欣然.一种自适应三维核回归的遥感时空融合方法[J].武汉大学学报(信息科学版),2018,43(4):563-570. |
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| 作者姓名： | 卓国浩 吴波 朱欣然 |
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| 作者单位： | 1.福州大学空间数据挖掘与信息共享教育部重点实验室, 福建 福州, 350002 |
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| 基金项目： | 国家自然科学基金41571330福建省自然科学基金2015J01163海西政务大数据应用协同创新中心基金2015750401 |
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| 摘 要： | 时空融合是解决遥感数据高重访周期与高空间分辨率矛盾的一种有效方法。发展了一种综合利用遥感数据空间与光谱信息的三维自适应核回归反射率模型（three-dimensional adaptively local steering kernel regression fusion model，3DSKRFM），通过提取每个像元的三维控制核（steering kernel）的局部信息，使时空融合过程中的权重自适应调节，提高遥感时空融合的精度。利用两组ETM+和MODIS（moderate-resolution imaging spectroradiometer）数据进行实验测试，结果表明3DSKRFM相比STARFM和2DSKRFM模型具有两方面的优势:一是充分利用遥感影像多波段的优势，提高融合精度；二是具有更强的鲁棒性，满足实际影像时空融合的需求。
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| 关 键 词： | 时空融合 三维核回归 自适应 融合精度 遥感影像 |
| 收稿时间： | 2016-08-17 |
Spatio-Temporal Reflectance Fusion Based on 3D Steering Kernel Regression Techniques |
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| Institution: | 1.Key Laboratory of Spatial Data Mining & Information Sharing, Ministry, Education, Fuzhou University, Fuzhou 350002, China2.School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China |
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| Abstract: | Spationtemporal fusion is an effective way to overcome contradictions between high temporal resolution and high spatial resolution of remote sensing, which has a wide range of applications in the city change monitoring, global warming, forest ecology and other environmental issues. STARFM model is a kind of classical and widely used remote sensing Spationtemporal fusion model, but it has two disadvantages. 1) STARFM model uses a fixed-size window to find similar pixels. Because there are both texture-poor areas and texture-abundant areas in an image, the window size should be taken into consideration in Spationtemporal fusion model. 2) STARFM is an isotropic-based algorithm used to determine similar pixels, but images often exhibit heterogeneous isotropic reflectances, especially in the edges of materials. The paper introduces a three-dimensional adaptively local steering kernel regression fusion model (3DSKRFM) to extract local information for each pixel, that is, using the band information of remote sensing data as the third dimension information of the steering kernel, and then using the three-dimensional gradient covariance matrix to obtain the image local geometry information, to achieve its adaptive weight. As a result, it can improve precision of spatiotemporal fusion of remote sensing image. Two datasets associated with ETM+ and MODIS images of Poyang Lake and Fuzhou region are adopted and fusion results of three relational models are compared from the perspective of the quantitative and qualitative in the experiments and the experiments show that 3DSKRFM model not only have the best fusion result, but also have the best ability to deal with noisy image when compared with STARFM and 2DSKRFM models. |
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