This paper summarizes the development and application of spatial statistical models in satellite optical remote sensing. The focuses on a conceptual model that includes measurement sampling processes inherent We organized this into five main sections: introducing basis sensing, including sampling; variation, variation through object-based data model; advances modelling; machine learning explain...

The above testing procedures are all motivated by the spatial autoregressive model of residual errors. So before moving on to spatial regression analyses of areal data, it is appropriate to consider certain alternative measures of spatial association that are also based on spatial weights matrices. By far the most important of these for our purposes are the so-called G-statistics, developed by ...

With the help of GPS and measuring instrument of soil moisture, soil moisture was measured and analyzed. As using Geo-statistics to the study of spatial variability of soil moisture and use ArcGIS 9.0, get the spatial distribution map of soil water property with Kriging interpolation. The research result showed that all soil spatial characters are normal distribution and the spatial distributio...

Orthofermi statistics is characterized by an exclusion principle which is more “exclusive” than Pauli’s exclusion principle: an orbital state shall not contain more than one particle, no matter what the spin direction is. The wavefunction is antisymmetric in spatial indices alone with arbitrary symmetry in the spin indices. Orthobose statistics is corresponding Bose analog: the wavefunction is ...

Image noise ltering has been widely perceived as an estimation problem in the spatial domain. In this paper, we deal with it as an estimation problem in an uncorrelated transform domain. This idea leads to a generalization of the adaptive LMMSE estimator for ltering noisy images. In our proposed method, the transform-domain local statistics obtained from the noisy image are exploited. Due to th...

| Image noise ltering has been widely perceived as an estimation problem in the spatial domain. We deal with it as an estimation problem in an uncorrelated transform domain. This idea leads to a generalization of the adaptive LMMSE estimator for ltering noisy images. In our proposed method, the transform-domain local statistics obtained from the noisy image are exploited. Due to the fact that t...