Correlation filtering: a terrain correction method for aeromagnetic maps with application

Accurate terrain corrections are important for all gravity surveying. In airborne surveys, a digital model of the terrain is constructed and terrain corrections are calculated at each airborne measurement point. Airborne gravity gradiometry is of high spatial resolution and is particularly sensitive to nearby topographic variations, placing particular requirements on the terrain corrections. A combination of mathematical analysis and simulation studies has led to quantification of the requirements: current on-shore, low-level gradiometer surveys require sub-metre accuracy in navigation and in digital terrain model heights; cell sizes (and therefore also topographic sampling) in the terrain model should be about one-third of the ground clearance. The choice of terrain correction density depends on the application and it is important that the interpreter of the corrected gravity data has the ability to test the impact of changes in this density. Accurate calculation of the gravity gradient field at the airborne sampling points may be achieved by a wide variety of either spatial or harmonic domain methods. Calculation in the harmonic domain is fast but assumes the data represent a periodic function on a planar surface. Padding methods for periodic extension and piecewise continuation away from a plane both add error and slow the calculation. Spatial domain methods are slower but can be sped up by the use of various approximations. In both cases, a clear understanding of accuracy requirements is essential for making an appropriate tradeoff between accuracy and speed.

Requirements for airborne gravity gradient terrain corrections

The main purpose of this work is to put together the benefits of the optical correlator which is VunderLaugt (VLC) correlator method for face recognition applications. With this aim in mind, we compare the performances of VLC correlator based on the fast Fourier transform (FFT) with the optical Fourier Transform (NO_FT) based on Fraunhofer diffraction. To achieve this goal, numerical implementation of the optical FT is needed. In this paper, we suggest and validate an all-numerical implementation of a VLC correlator with optical FT. Different tests using the Pointing Head Pose Image Database (PHPID) and considering faces with vertical and horizontal rotations were performed. The receiving operating characteristics (ROC) curves show that the optical FT simulating the Fraunhofer diffraction leads to better performances than the FFT.

Implementation of optical correlator for face recognition applications

Remote sensing works on the principle of the inverse problem. While the object or phenomenon of interest (the state) may not be directly measured, there exists some other variable that can be detected and measured (the observation), which may be related to the object of interest through the use of a data-derived computer model. The common analogy given to describe this is trying to determine the type of animal from its footprints. For example, while it is impossible to directly measure temperatures in the upper atmosphere, it is possible to measure the spectral emissions from a known chemical species (such as carbon dioxide) in that region. The frequency of the emission may then be related to the temperature in that region via various thermodynamic relations. The images captured by satellite using remote sensing technique will analyzed. To obtain the image data, satellite sends signals to the object and then captures the reflection of the object surface. This data must be treated correctly though to acquire the images of the surface. The area of interest in given image, called region of interest can be defined. After defining the region of interest apply the Lucy Richardson, Wiener and Blind Deconvolution to refine the region of interest in image to get the useful information from captured image.

Comparative analysis of image restoration of remote sensing images using lucy richardson,wiener and blind deconvolution

The Fraunhoffer diffraction analysis of cloud-covered satelite imagery has shown that the diffraction pattern follows approximately cosine squared distribution. The overshooting tops of clouds and the shadows cast by them contribute much to the diffraction of light, particularly in the high-frequency range. Indeed, cloud-covered imagery can be distinguished from cloud-free imagery on the basis of rate of decay of the diffracted light power in the high-frequency band.

Fraunhoffer Diffraction Analysis of Satellite Cloud Imagery

The present work is aimed at rapid reduction of the gravity and magnetic fields observed over an uneven surface to a horizontal plane. The approach suggested is to estimate the Fourier transform of the potential field over an imaginary horizontal plane lying entirely above the ground surface and impose boundary conditions; namely, the solution must satisfy the observed field over the ground surface and vanish over an infinite hemisphere. The desired Fourier transform is obtained in an iterating manner. A 2D FFT algorithm can considerably reduce the computational burden. The FFT approach cannot be used unless the discrete data is available on a rectangular grid. If the observations are scattered, interpolation to the nearest grid point will have to be carried out. Interpolation introduces marginal increase in the rms error. The iterating approach is about 10 times faster than the least squares approach. >

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P. S. Naidu

Papers

Fraunhoffer Diffraction Analysis of Satellite Cloud Imagery

Fast reduction of potential fields measured over an uneven surface to a plane surface