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Sub-pixel mapping of remote sensing images based on sub-pixel/pixel spatial attraction models with anisotropic spatial dependence model

机译:基于具有各向异性空间相关性模型的子像素/像素空间吸引模型的遥感影像子像素映射

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Sub-pixel mapping (SPM) based on the Sub-pixel /Pixel Spatial Attraction Models (SPSAM) is a technique that improve the spatial resolution of remote sensing images. SPSAM-based SPM is based on the spatial dependence theory with isotropic assumption. In SPSAM, the weight calculation of spatial dependence is only relevant with the distance between the sub-pixel and its neighboring pixel. Obviously, the direction of spatial dependence is neglected. In this paper, a revised SPSAM-based SPM with anisotropic spatial dependence model (SPSAMA) is proposed. Sobel operator is utilized to determine the gradient magnitude and direction of coarse proportion image at every pixel. Then the gradient magnitude and direction will be used to reckon the weights of adjacent pixels proportions in the neighborhood. Experimental results demonstrated that the SPSAMA can produce land cover maps with greater accuracy than traditional SPSAM.
机译:基于子像素/像素空间吸引力模型(SPSAM)的子像素映射(SPM)是一种提高遥感图像空间分辨率的技术。基于SPSAM的SPM基于带有各向同性假设的空间相关性理论。在SPSAM中,空间相关性的权重计算仅与子像素与其相邻像素之间的距离有关。显然,空间依赖性的方向被忽略了。本文提出了一种基于各向异性空间依赖模型(SPSAMA)的基于SPSAM的改进SPM。利用Sobel算子确定每个像素处的粗略比例图像的梯度大小和方向。然后,将使用梯度大小和方向来计算邻域中相邻像素比例的权重。实验结果表明,与传统的SPSAM相比,SPSAMA可以生成精度更高的土地覆盖图。

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