Nowadays, terrestrial dynamics study is more and more often performed with the help of satellite sensors. Usually, vegetation cover surveys are performed with wide field of view sensors, because of their high temporal...
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ISBN:
(纸本)0819442666
Nowadays, terrestrial dynamics study is more and more often performed with the help of satellite sensors. Usually, vegetation cover surveys are performed with wide field of view sensors, because of their high temporal resolution. However, a high spatial resolution will be appreciable to distinguish each component in a landscape. We propose to create merged images combining both sensors: our fusion method is based on both theories of pyramid algorithms and mathematical morphology. Let call HR (resp. BR) the spatial resolution of the high resolution (resp. coarse) sensor image, for example SPOT 4 HRVIR and VEGETATION. The principle is: 1. To decompose the high resolution image into a low-frequency and several high-frequencies images (HFI). 2. To perform the inverse transform on the HFI images and the coarse resolution sensor data and produce the merged image. Consequently, from a temporal set of VEGETATION data and from a few HRVIR scenes, we are able to create 20m (or less) resolution synthesis data having the temporal repetitivity of the VEGETATION data set.
A simple method which for generalizing the classical orthonormal wavelets is presented using investigation of the simplest Haar scaling function. A new kind of orthonormal wavelets is constructed from a classical orth...
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A simple method which for generalizing the classical orthonormal wavelets is presented using investigation of the simplest Haar scaling function. A new kind of orthonormal wavelets is constructed from a classical orthonormal wavelets using the method proposed in this paper. These new wavelets inherit some basic properties of the corresponding classical wavelets, such as the orthonormality, the order of regularity, the time-frequency localization characteristics, and so on. meanwhile some performances of the new wavelets are improved. The generalized Haar wavelet, the generalized Shannon wavelet and the generalized Meyer wavelet, the generalized Daubechies wavelets are discussed emphatically. Finally, based on the new wavelet system, some fast algorithms for analytic wavelet transform analysis of real signals are studied.
Various aspects of the wavelet approach to nonparametric regression are considered, with the overall aim of extending the scope of wavelet techniques to irregularly spaced data, to regularly spaced datasets of arbitra...
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Various aspects of the wavelet approach to nonparametric regression are considered, with the overall aim of extending the scope of wavelet techniques to irregularly spaced data, to regularly spaced datasets of arbitrary size, to heteroscedastic and correlated data, and to data that contain outliers. The core of the methodology is an algorithm for finding all of the variances and within-level covariances in the wavelet table of a sequence with given covariance structure. If the original covariance matrix is band-limited, then the algorithm is linear in the length of the sequence. The variance calculation algorithm allows data on any set of independent variable values to be treated, by first interpolating to a fine regular grid of suitable length, and than constructing a wavelet expansion of the gridded data. Various thresholding methods are discussed and investigated. Exact risk formulas for the mean square error of the methodology for given design are derived. Good performance is obtained by noise-proportional thresholding, with thresholds somewhat smaller than the classical universal threshold. Outliers in the data can be removed or downweighted, and aspects of such robust techniques are developed and demonstrated in an example. Another natural application is to correlated data, where the covariance of the wavelet coefficients is not due to an initial grid transform but rather is an intrinsic feature. The use of the method in these circumstances is demonstrated by an application to data synthesized in the study of ion channel gating. Our basic approach has many other potential applications, some of which are discussed briefly.
Image fusion technique has gradually been paid more and more attention to for its advantage of integration of information from multisensors, and its application has been developed in many fields such as medicine, remo...
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ISBN:
(纸本)0819430226
Image fusion technique has gradually been paid more and more attention to for its advantage of integration of information from multisensors, and its application has been developed in many fields such as medicine, remote sensing, computer vision, weather forecast, etc. In this paper, some fusion algorithms on pixel level have been programmed and their effects have been analyzed. A new efficient method named after Contrast Modulation-pyramid algorithm ( CMPA) has been developed. The realization of this new algorithm has been designed and researched with Digital Signal Processor ( DSP) and has been programmed with relevant software. The result showed that image fusion would been completed at real-time or quasi-realtime speed.
Mallat's pyramid algorithm relates the scaling coefficients of a function at one level to the scaling and wavelet coefficients at lower levels. In practice, the scaling coefficients are estimated at some level m, ...
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ISBN:
(纸本)0819422134
Mallat's pyramid algorithm relates the scaling coefficients of a function at one level to the scaling and wavelet coefficients at lower levels. In practice, the scaling coefficients are estimated at some level m, and the algorithm is used to produce estimates of the scaling and wavelet coefficients at lower levels. Initial errors propagate to lower level estimates. this paper descries conditions under which this process generates estimates which are uniformly reliable at a particular level, and under which the errors at that level tend uniformly to zero as m increases.
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