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- 2018
一种自适应三维核回归的遥感时空融合方法
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Abstract:
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
| [1] | Wu B, Huang B, Cao K, et al. Improving Spatiotemporal Reflectance Fusion Using Image Inpainting and Steering Kernel Regression Techniques[J]. <em>International Journal of Remote Sensing,</em> 2017, 38(3):706-727 |
| [2] | Huang B, Zhang H. Spatio-Temporal Reflectance Fusion via Unmixing:Accounting for Both Phenological and Land-Cover Changes[J]. <em>International Journal of Remote Sensing</em>, 2014, 35(16):6213-6233 |
| [3] | Dai Changda, Lei Liping. The Information Characteristics of Thematic Mapper Data and the Optimal Band Combination[J]. <em>J Remote Sens</em>, 1989(4):282-292(戴昌达, 雷莉萍. TM图像的光谱信息特征与最佳波段组合[J]. 遥感学报, 1989(4):282-292) |
| [4] | Markham B L, Storey J C, Williams D L, et al. Landsat Sensor Performance:History and Current Status[J]. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2004, 42(12):2691-2694 |
| [5] | Gao Kun, Liu Ying, Wang Lijing, et al. A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model[J]. <em>Spectroscopy and Spectral Analysis</em>, 2015,35(10):2846-2850(高昆, 刘莹, 王丽静,等. 基于高斯马尔科夫模型的高光谱异常目标检测算法研究[J]. 光谱学与光谱分析, 2015, 35(10):2846-2850) |
| [6] | Gao F, Masek J, Schwaller M, et al. On the Blending of the Landsat and MODIS Surface Reflectance:Predicting Daily Landsat Surface Reflectance[J]. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2006, 44(8):2207-2218 |
| [7] | Zhu X, Chen J, Gao F, et al. An Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions[J]. <em>Remote Sensing of Environment</em>, 2010, 114(11):2610-2623 |
| [8] | Xie D, Zhang J, Zhu X, et al. An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions[J]. <em>Sensors</em>, 2016, 16(2),doi:10.3390/s16020207 |
| [9] | Hansen M C, Roy D P, Lindquist E, et al. A Method for Integrating MODIS and Landsat Data for Systematic Monitoring of Forest Cover and Change in the Congo Basin[J]. <em>Remote Sensing of Environment</em>, 2008, 112(5):2495-2513 |
| [10] | Walker J J, De Beurs K M, Wynne R H. Dryland Vegetation Phenology Across an Elevation Gradient in Arizona, USA, Investigated with Fused MODIS and Landsat Data[J]. <em>Remote Sensing of Environment</em>, 2014, 144:85-97 |
| [11] | Hilker T, Wulder M A, Coops N C, et al. Generation of Dense Time Series Synthetic Landsat Data Through Data Blending with MODIS Using a Spatial and Temporal Adaptive Reflectance Fusion Model[J]. <em>Remote Sensing of Environment</em>, 2009, 113(9):1988-1999 |
| [12] | Shen H, Wu P, Liu Y, et al. A Spatial and Temporal Reflectance Fusion Model Considering Sensor Observation Differences[J]. <em>International Journal of Remote Sensing</em>, 2013, 34(12):4367-4383 |
| [13] | Takeda H, Milanfar P. Locally Adaptive Kernel Regression for Space-Time Super-Resolution[J]. <em>Super-Resolution Imaging, Digital Imaging and Computer Vision</em>, 2010:63-69 |
| [14] | Wang Z, Bovik A C, Sheikh H R, et al. Image Quality Assessment:from Error Visibility to Structural Similarity[J]. <em>IEEE Transactions on Image Processing</em>, 2004, 13(4):600-612 |
| [15] | Eskicioglu A M, Fisher P S. Image Quality Measures and Their Performance[J]. <em>IEEE Transactions on Communications</em>, 1995, 43(12):2959-2965 |
| [16] | Huang B, Wang J, Song H, et al. Generating High Spatiotemporal Resolution Land Surface Temperature for Urban Heat Island Monitoring[J]. <em>IEEE Geoscience and Remote Sensing Letters</em>, 2013, 10(5):1011-1015 |
| [17] | Schmidt M, Lucas R, Bunting P, et al. Multi-resolution Time Series Imagery for Forest Disturbance and Regrowth Monitoring in Queensland, Australia[J]. <em>Remote Sensing of Environment</em>, 2015(158):156-168 |
| [18] | Takeda H, Farsiu S, Milanfar P. Kernel Regression for Image Processing and Reconstruction[J]. <em>IEEE Transactions on Image Processing</em>, 2007, 16(2):349-366 |
| [19] | Masek J G, Huang C, Wolfe R, et al. North American Forest Disturbance Mapped from a Decadal Landsat Record[J]. <em>Remote Sensing of Environment</em>, 2008, 112(6):2914-2926 |
| [20] | Sheikh H R, Sabir M F, Bovik A C. A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms[J]. <em>IEEE Transactions on Image Processing,</em> 2006, 15(11):3440-3451 |
