Environmental Research Institute of Michigan

About: Environmental Research Institute of Michigan is a facility organization based out in Ann Arbor, Michigan, United States. It is known for research contribution in the topics: Synthetic aperture radar & Radar imaging. The organization has 793 authors who have published 1137 publications receiving 44609 citations.

Phase retrieval algorithms: a comparison.

Abstract: Iterative algorithms for phase retrieval from intensity data are compared to gradient search methods. Both the problem of phase retrieval from two intensity measurements (in electron microscopy or wave front sensing) and the problem of phase retrieval from a single intensity measurement plus a non-negativity constraint (in astronomy) are considered, with emphasis on the latter. It is shown that both the error-reduction algorithm for the problem of a single intensity measurement and the Gerchberg-Saxton algorithm for the problem of two intensity measurements converge. The error-reduction algorithm is also shown to be closely related to the steepest-descent method. Other algorithms, including the input-output algorithm and the conjugate-gradient method, are shown to converge in practice much faster than the error-reduction algorithm. Examples are shown.

Reconstruction of an object from the modulus of its Fourier transform.

Abstract: We present a digital method for solving the phase-retrieval problem of optical-coherence theory: the reconstruction of a general object from the modulus of its Fourier transform. This technique should be useful for obtaining high-resolution imagery from interferometer data.

The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat

Abstract: The time trajectories of agricultural data points as seen in Landsat signal space form a pattern suggestive of a tasselled woolly cap. Most of the important crop phenomena can be described using this three dimensional construct: the distribution of signals from bare soil, the processes of green development, yellow development, and shadowing and harvesting. A linear preprocessing transformation which isolates green development, yellow development and soil brightness is used to reduce the dimension of the signal space. Specific measurable pattern elements of the tasselled cap are used to estimate and correct atmospheric haze and moisture effects.

A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap

Abstract: In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space.

Accelerating the convergence of the back-propagation method

Abstract: The utility of the back-propagation method in establishing suitable weights in a distributed adaptive network has been demonstrated repeatedly. Unfortunately, in many applications, the number of iterations required before convergence can be large. Modifications to the back-propagation algorithm described by Rumelhart et al. (1986) can greatly accelerate convergence. The modifications consist of three changes:1) instead of updating the network weights after each pattern is presented to the network, the network is updated only after the entire repertoire of patterns to be learned has been presented to the network, at which time the algebraic sums of all the weight changes are applied:2) instead of keeping ź, the "learning rate" (i.e., the multiplier on the step size) constant, it is varied dynamically so that the algorithm utilizes a near-optimum ź, as determined by the local optimization topography; and3) the momentum factor ź is set to zero when, as signified by a failure of a step to reduce the total error, the information inherent in prior steps is more likely to be misleading than beneficial. Only after the network takes a useful step, i.e., one that reduces the total error, does ź again assume a non-zero value. Considering the selection of weights in neural nets as a problem in classical nonlinear optimization theory, the rationale for algorithms seeking only those weights that produce the globally minimum error is reviewed and rejected.