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Approach to Determination of Parameters of Probability Density Function of Object Attributes Recognition in Space Photographs Is Considered Within Statistical Method

机译:在统计方法中考虑了对象属性识别概率密度函数参数的确定方法

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High information richness of satellite images is used effectively, only if they are processed promptly. Analysis of recent research shows that existing hardware and software allows only partial automation of object recognition in space photographs. Automation is reduced to visualization of images and measurement of their parameters. Further processing requires parameters of probability density function of object features recognition. To solve certain problems in geological, hydrological, forestry and other types of decryption, parameters of this distribution are estimated according to experimental data. However, this approach is not suitable for recognition of single compact surface objects. Therefore, reference images formed upon three-dimensional models are suggested to determine unknown parameters of probability distribution function. Method of allowable transformations is applied to determine initial conditions of reference images and take their recognition feature distributive law for a distributive law of recognition features of single compact surface objects.
机译:仅当它们迅速处理时,才能有效地使用高信息卫星图像的丰富性。最近的研究分析表明,现有的硬件和软件仅允许在空间照片中的对象识别的部分自动化。自动化减少到图像的可视化和它们参数的测量。进一步处理需要对象特征识别的概率密度函数的参数。为了解决地质,水文,林业等类型的解密中的某些问题,根据实验数据估计该分布的参数。然而,这种方法不适合识别单个紧凑的表面物体。因此,建议在三维模型上形成的参考图像来确定概率分布函数的未知参数。应用允许变换的方法来确定参考图像的初始条件,并采取其识别特征分配法,用于单个紧凑表面对象的分布法。

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