Exploiting Full-Waveform Lidar Data and Multiresolution Wavelet Analysis for Vertical Object Detection and Recognition

A current challenge in performing airport obstruction surveys using airborne lidar is lack of reliable, automated methods for extracting and attributing vertical objects from the lidar data. This paper presents a new approach to solving this problem, taking advantage of the additional data provided byfull-waveform systems. The procedure entails first deconvolving and georeferencing the lidar waveformdata to create dense, detailed point clouds in which the vertical structure of objects, such as trees, towers, and buildings, is well characterized. The point clouds are then voxelized to produce high-resolution volumes of lidar intensity values, and a 3D wavelet decomposition is computed. Verticalobject detection and recognition is performed in the wavelet domain using a multiresolution template matching approach. The method was tested using lidar waveform data and ground truth collected for project areas in Madison,Wisconsin. Preliminary results demonstrate the potential of the approach

Optimal image deconvolution by range and noise moment constraints

Image deconvolution, also known as image restoration, is concerned with the estimation of an uncorrupted image from a noisy, degraded one. The degradation of this image may be caused by defects of optical lenses, nonlinearity of the electro-optical sensor, relative motion between an object and camera, wrong focus, etc. By assuming a degradation model, one can formulate and develop a restoration algorithm. In this thesis, the developed algorithms are iterative deconvolution methods based on noise moment and pixel range constraints. The moments were used to ensure that noise associated with the deconvolution solution satisfies predetermined statistics. The pixel range constraints were also used to ensure the solution is within predetermined pixel value bounds. This addresses the critical issue of noise amplification at those frequencies where the point-spread function (the blurring function) contains frequency nulls. The solution’s dependence on the number of moments is examined and the performance of the deconvolution approach is compared with existing and well established deconvolution methods such as Wiener filtering and inverse filtering

Satellite and Aerial Image Deconvolution Using an EM Method with Complex Wavelets

In this paper, we present a new deconvolution method, able to deal with noninvertible blurring functions. To avoid noise amplification, a prior model of the image to be reconstructed is used within a Bayesian framework. We use a spatially adaptive prior, defined with a complex wavelet transform in order to preserve shift invariance and to better restore variously oriented features. The unknown image is estimated by an EM technique, whose E step is a Landweber update iteration, and the M step consists of denoising the image, which is achieved by wavelet coefficient thresholding. The new algorithm has been applied to high resolution satellite and aerial data, showing better performance than existing techniques when the blurring process is not invertible, like motion blur for instance

Satellite and Aerial Image Deconvolution Using an EM Method with Complex Wavelets

In this paper, we present a new deconvolution method, able to deal with noninvertible blurring functions. To avoid noise amplification, a prior model of the image to be reconstructed is used within a Bayesian framework. We use a spatially adaptive prior, defined with a complex wavelet transform in order to preserve shift invariance and to better restore variously oriented features. The unknown image is estimated by an EM technique, whose E step is a Landweber update iteration, and the M step consists of denoising the image, which is achieved by wavelet coefficient thresholding. The new algorithm has been applied to high resolution satellite and aerial data, showing better performance than existing techniques when the blurring process is not invertible, like motion blur for instance