Spatially adaptive smoothing parameter selection for Markov random field based sub-pixel mapping of remotely sensed images
Spatially adaptive smoothing parameter selection for Markov random field based sub-pixel mapping of remotely sensed images
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DOI：
10.1080/01431161.2012.703347
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发表时间：
2012-12
影响因子：
3.4
通讯作者：
Xiaodong Li;Yun Du;F. Ling
中科院分区：
文献类型：
--
作者：
Xiaodong Li;Yun Du;F. Ling
亚像素映射是一种以比遥感图像像素大小更精细的空间分辨率来提供土地覆盖类别的空间分布的过程。传统的基于马尔可夫随机场的亚像素映射(MRF_SPM)采用基于整幅图像的固定平滑参数来平衡空间能量和光谱能量。然而，遥感像素的光谱在空间上总是可变的。采用固定的平滑参数会忽略每个像素谱提供的局部特性，可能会导致均匀区域内的平滑不足和类边界之间的过度平滑。针对MRF_SPM模型参数固定的局限性，提出了一种空间自适应参数选择方法。由于像元类别比例是每个粗像元中土地覆盖类别的类型和比例的指示器，在该方法中，使用提供像元类别比例作为每个像素谱的局部属性的分数图像来约束平滑参数。因此，平滑参数在空间上自适应于遥感图像的每个像素光谱。采用合成图像和IKONOS多光谱图像。结果表明，与硬分类方法和采用固定平滑参数的非空间自适应MRF_SPM相比，空间自适应MRF_SPM将平滑参数限制在每个像素谱上，得到的亚像素图不仅精度更高，而且形状和边界在视觉上更接近参考图。
Sub-pixel mapping is a process to provide the spatial distributions of land cover classes with finer spatial resolution than the size of a remotely sensed image pixel. Traditional Markov random field-based sub-pixel mapping (MRF_SPM) adopts a fixed smoothing parameter estimated based on the entire image to balance the spatial and spectral energies. However, the spectra of the remotely sensed pixels are always spatially variable. Adopting a fixed smoothing parameter disregards the local properties provided by each pixel spectrum, and may probably lead to insufficient smoothing in the homogeneous region and over-smoothing between class boundaries simultaneously. This article proposes a spatially adaptive parameter selection method for the MRF_SPM model to overcome the limitation of the fixed parameter. As pixel class proportions are indicators of the type and proportion of land cover classes within each coarse pixel, in the proposed method, fraction images providing pixel class proportions as local properties of each pixel spectrum are employed to constrain the smoothing parameter. Consequently, the smoothing parameter is spatially adaptive to each pixel spectrum of the remotely sensed image. Synthetic images and IKONOS multi-spectral images were employed. Results showed that compared with the hard classification method and the non-spatially adaptive MRF_SPM adopting a fixed smoothing parameter, the spatially adaptive MRF_SPM with the smoothing parameter constrained to each pixel spectrum yielded sub-pixel maps not only with higher accuracy but also with shapes and boundaries visually reconstructed more closely to the reference map.