Abstract
In modern systems of remote sensing two-dimensional fast Fourier transform (FFT) has been widely used for digital processing of satellite images and subsequent image filtering. This article provides a parallel version two-dimensional fast Fourier transform algorithm, analog of the Cooley-Tukey algorithm and its implementation for processing the satellite image of Krasnoyarsk and its suburban areas.
Highlights
Earth remote sensing is closely related to digital image processing, as the aerospace images that are rich in detail are commonly represented in digital form of a raster type
With this butterfly we can split the source signal and the Fourier transform with elements into four sub-signals each with the number of elements 2s 1 2s 1 [4]. This reduces the number of multiplications and additions of complex numbers required to compute fast Fourier transform (FFT)
To test the running time of an algorithm for calculating two-dimensional fast Fourier transform algorithm, analog of the Cooley-Tukey algorithm, a simulation program was written in the C++ programming language [4]
Summary
Earth remote sensing is closely related to digital image processing, as the aerospace images that are rich in detail are commonly represented in digital form of a raster type. The traditionally applied algorithm for computing two-dimensional fast Fourier transform (FFT) is the sequential application of a one-dimensional FFT, first for all rows, for all columns. The article describes a version two-dimensional fast Fourier transform algorithm, analog of the Cooley-Tukey algorithm, with the reduced number of complex operations compared to the traditionally used algorithm.
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