一种基于模板匹配的线性特征混合像元的亚像元定位的新方法
New Template Matching Method for Subpixel Mapping of Linear Feature Mixed Pixels
DOI: 10.12677/JISP.2019.83024, PDF,    国家自然科学基金支持
作者: 陆海强, 朱 伟：嘉兴市恒创电力设备有限公司，浙江 嘉兴；刘照欣*：杭州电子科技大学计算机学院，浙江 杭州
关键词: 混合像元；亚像元定位；模板匹配；相关系数；直线拟合；Mixed Pixel； Subpixel Mapping； Template Matching； Correlation Coefficient； Line Fitting
摘要: 通过分析基于模板匹配的含线特征混合像元亚像元定位算法存在的问题，提出改进的模板匹配新方法。在相关系数选择模板的基础上提出两种新的模板选择方法：1) 对含线特征混合像元八邻域拟合直线进一步确定模板，在一定程度上缩小了模板选择的范围；2) 为避免模板选择的不确定性，对含线特征混合像元八邻域及模板分别进行直线拟合，计算直线相关性，得出最佳模板。进一步地，结合像元引力解决包含三种及以上地物混合的含线特征像元的亚像元定位问题。实验结果表明所提出的方法能提高亚像元定位精度。
Abstract: By analyzing the problems of subpixel mapping algorithm based on template matching, a linear feature sub-pixel mapping algorithm based on improved template matching is proposed. Based on the correlation coefficient selection template, two new template selection methods are proposed: 1) Further determining the template by fitting a line with linear feature mixed pixels eight neigh-borhood, to some extent, reduces the scope of template selection; 2) In order to avoid the uncer-tainty of template selection, the best template is obtained through line fitting on the mixed pixels eight neighborhoods and the template and calculating the linear correlation. Furthermore, com-bined with pixel gravity, the sub-pixel mapping problem of linear feature pixel containing three or more ground objects is solved. The experiment results show that the proposed method can improve the sub-pixel mapping accuracy.
文章引用：陆海强, 刘照欣, 朱伟. 一种基于模板匹配的线性特征混合像元的亚像元定位的新方法[J]. 图像与信号处理, 2019, 8(3): 180-193. https://doi.org/10.12677/JISP.2019.83024

参考文献

[1] Tong, X., Xu, X., Plaza, A., et al. (2016) A New Genetic Method for Subpixel Mapping Using Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 4480-4491.
[Google Scholar] [CrossRef] 
[2] Bioucas-Dias, J.M., Plaza, A., et al. (2012) Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2, 354-379.
[Google Scholar] [CrossRef] 
[3] 陈晋, 马磊, 陈学泓. 混合像元分解技术及其进展[J]. 遥感学报, 2016, 20(5): 1102-1109.
[4] 赵春晖, 成宝芝, 杨伟超. 利用约束非负矩阵分解的高光谱解混算法[J]. 哈尔滨工程大学学报, 2012, 33(3): 377-382.
[5] Athinkson, P.M. (1997) Mapping Sub-Pixel Boundaries from Remotely Sensed Images. Innovations in GIS, 4, 166-180.
[6] Xu, X., Zhong, Y. and Zhang, L. (2014) A Sub-Pixel Mapping Method Based on an Attraction Model for Multiple Shifted Remotely Sensed Images. Neurocomputing, 134, 79-91.
[Google Scholar] [CrossRef] 
[7] Wu, S., Chen, Z., Ren, J., et al. (2018) An Improved Subpixel Mapping Algorithm Based on a Combination of the Spatial Attraction and Pixel Swapping Models for Multispectral Remote Sensing Imagery. IEEE Geoscience and Remote Sensing Letters, 15, 1-5.
[Google Scholar] [CrossRef] 
[8] 许雄, 钟燕飞, 张良培, 等. 基于空间自相关BP神经网络的遥感影像亚像元定位[J]. 测绘学报, 2011, 40(3): 307-311.
[9] 吴柯, 牛瑞卿, 沈焕峰, 等. 结合超分辨率重建的神经网络亚像元定位方法[J]. 中国图象图形学报, 2010, 15(11): 1681-1687.
[10] 王群明. 基于软硬属性转换的遥感图像亚像元定位算法[J]. 测绘学报, 2016, 45(4): 503-503.
[11] Wang, P., Wang, L. and Chanussot, J. (2016) Soft-then-Hard Subpixel Land Cover Mapping Based on Spatial-Spectral Interpolation. IEEE Geoscience and Remote Sensing Letters, 13, 1851:1854.
[Google Scholar] [CrossRef] 
[12] 赵辽英, 范明阳, 厉小润, 等. 基于目标优化的高光谱图像亚像元定位[J]. 中国图象图形学报, 2016, 21(6): 823-833.
[13] Chen, S., Li, X. and Zhao, L. (2017) Subpixel Mapping Method of Hyperspec-tral Images Based on Modified Binary Quantum Particle Swarm Optimization. Journal of Electrical and Computer Engineering, No. 3, 1-17.
[Google Scholar] [CrossRef] 
[14] Zhong, Y. and Zhang, L. (2013) Sub-Pixel Mapping Based on Artificial Immune Systems for Remote Sensing Imagery. Pattern Recognition, 46, 2902-2926.
[Google Scholar] [CrossRef] 
[15] Xu, X., Zhong, Y. and Zhang, L. (2013) Adaptive Subpixel Mapping Based on a Multiagent System for Remote-Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, 52, 787-804.
[Google Scholar] [CrossRef] 
[16] Ge, Y., Chen, Y., Stein, A., et al. (2016) Enhanced Subpixel Mapping with Spatial Distribution Patterns of Geographical Objects. IEEE Transactions on Geoscience and Remote Sensing, 54, 2356-2370.
[Google Scholar] [CrossRef] 
[17] Fisher, P. (1997) The Pixel: A Snare and a Delusion. International Journal of Remote Sensing, 18, 679-685.
[Google Scholar] [CrossRef] 
[18] Chang, C.I. (2017) Adaptive Linear Spectral Mixture Analysis. IEEE Transac-tions on Geoscience and Remote Sensing, 55, 1240-1253.
[Google Scholar] [CrossRef] 
[19] Heinz, D.C. and Chang, C.I. (2002) Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyper-spectral Imagery. IEEE Transactions on Geoscience and Remote Sensing, 39, 529-545.
[Google Scholar] [CrossRef] 
[20] 王丽姣, 厉小润, 赵辽英. 快速实现基于单形体体积生长的端元提取算法[J]. 光学学报, 2014, 34(11): 308-314.
[21] Erturk, A., Gullu, M.K., Cesmeci, D., et al. (2014) Spatial Resolution Enhancement of Hyper-spectral Images Using Unmixing and Binary Particle Swarm Optimization. IEEE Geoscience and Remote Sensing Letter, 11, 2100-2104.
[Google Scholar] [CrossRef] 
[22] Ling, F. and Fu, B. (2009) Super-Resolution Mapping of Urban Buildings with Remotely Sensed Imagery Based on Prior Shape Information. Joint Urban Remote Sensing Event, Shanghai, 20-22 May 2009, 1-5.
[Google Scholar] [CrossRef] 
[23] Rwanga, S.S. and Ndambuki, J.M. (2017) Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 8, 611-622.
[Google Scholar] [CrossRef]