Autocorrelation and regularization in digital images. I. Basic theory
Autocorrelation and regularization in digital images. I. Basic theory
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DOI：
10.1109/36.3050
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发表时间：
1988-07
影响因子：
8.2
通讯作者：
D. Jupp;A. Strahler;C. Woodcock
中科院分区：
文献类型：
--
作者：
D. Jupp;A. Strahler;C. Woodcock
空间结构发生在遥感图像中，当成像场景包含离散的对象，是可识别的，因为它们的光谱特性是更均匀的内部比它们之间和其他场景元素。引入的空间结构是明显的统计措施，如自协方差函数和变差函数与场景相关联，它是可能的，制定这些措施明确的场景组成的简单物体的规则形状。数字图像由具有相关联的点扩散函数（PSF）的仪器感测场景产生。由于存在对PSF的平均，因此由仪器在图像数据中引起的称为正则化的效应将影响图像数据的可观察的自协方差和变差函数。它示出了图像的自协方差或变差函数是如何与重叠函数卷积的底层场景协方差的组成，重叠函数本身是PSF的卷积。这种关系的函数形式提供了一个分析基础的场景推断和最终反演的场景模型参数从图像数据。>
Spatial structure occurs in remotely sensed images when the imaged scenes contain discrete objects that are identifiable in that their spectral properties are more homogeneous within than between them and other scene elements. The spatial structure introduced is manifest in statistical measures such as the autocovariance function and variogram associated with the scene, and it is possible to formulate these measures explicitly for scenes composed of simple objects of regular shapes. Digital images result from sensing scenes by an instrument with an associated point spread function (PSF). Since there is averaging over the PSF, the effect, termed regularization, induced in the image data by the instrument will influence the observable autocovariance and variogram functions of the image data. It is shown how the autocovariance or variogram of an image is a composition of the underlying scene covariance convolved with an overlap function, which is itself a convolution of the PSF. The functional form of this relationship provides an analytic basis for scene inference and eventual inversion of scene model parameters from image data. >