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基于LBV变换的遥感影像多步骤分类法研究 总被引:1,自引:0,他引:1
面对与日俱增的遥感数据,如何快速准确地进行影像分类成为遥感领域亟待解决的问题。本文在前人的基础上提出了一种基于LBV变换的遥感影像多步骤分类方法。L:地物的总辐射水平,集中反映了裸地的信息;B:地物的可见-红外光辐射平衡,是地面水分状况和水体存在的一个良好指标;V:地物辐射随波段变化的方向和速度向量,能集中反映地面植被状况。多步骤分类法遵循由易到难的原则,能克服类别之间的互相影响,从而提高分类精度。文章结合LBV变换和多步骤分类法的优点对影像进行了分类,结果表明该方法简单易行,且能达到良好的分类精度。 相似文献
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Paleocurrent indicator data collected in field work were used to study the early Cenozoic regional paleodrainage patterns in the Hob Xil basin in northern Tibetan plateau. The paleocurrent directions of the Eocene Fenghuoshan Group obviously show that the flows were northward with a unidirectional dispersal pattern. This probably reflects the uplift of the Qiangtang terrain during the initial basin deposition period and indicates that the Tanggula Moutains occurred as topographic highlands at least in the Eocene. Paleoflows of the Oligocene Yaxicuo Group were dominantly oriented to the north and then flowed eastwards during its late deposition. This regional variability of paleodrainage patterns of the Yaxicuo Group is interpreted to record the dispersal style of sediments from transverse rivers to longitudinal river systems. It is inferred that the Oligocene uplift of the Kunlun Mountains obstructed by northward paleoflows and created longitudinal river systems parallel to the orogenic belts. The temporal and spatial changes of the paleodrainage patterns suggest that the northern boundary of the Tibetan plateau during the early Cenozoic was situated in the Hoh Xil area and its uplift has progressed northwards through time. 相似文献
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On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms 总被引:2,自引:1,他引:1
The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model (Y−E
Y
= (X−E
X
) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E
Y
and E
X
. Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation
matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix
X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem
by using the nonlinear Euler–Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative
algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition.
For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert
cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing
the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment
of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift
to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335–342). The differences between the standard least-squares
and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion
matrix of the estimated parameters. 相似文献
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