A review and analysis of neural networks for classification of remotely sensed multispectral imagery

A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use

Comparisons of neural networks to standard techniques for image classification and correlation

Neural network techniques for multispectral image classification and spatial pattern detection are compared to the standard techniques of maximum-likelihood classification and spatial correlation. The neural network produced a more accurate classification than maximum-likelihood of a Landsat scene of Tucson, Arizona. Some of the errors in the maximum-likelihood classification are illustrated using decision region and class probability density plots. As expected, the main drawback to the neural network method is the long time required for the training stage. The network was trained using several different hidden layer sizes to optimize both the classification accuracy and training speed, and it was found that one node per class was optimal. The performance improved when 3x3 local windows of image data were entered into the net. This modification introduces texture into the classification without explicit calculation of a texture measure. Larger windows were successfully used for the detection of spatial features in Landsat and Magellan synthetic aperture radar imagery

Searching for patterns in remote sensing image databases using neural networks

We have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery

The effect of lossy image compression on image classification

We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network

First Science Observations with SOFIA/FORCAST: Properties of Intermediate-Luminosity Protostars and Circumstellar Disks in OMC-2

We examine eight young stellar objects in the OMC-2 star forming region based on observations from the SOFIA/FORCAST early science phase, the Spitzer Space Telescope, the Herschel Space Observatory, 2MASS, APEX, and other results in the literature. We show the spectral energy distributions of these objects from near-infrared to millimeter wavelengths, and compare the SEDs with those of sheet collapse models of protostars and circumstellar disks. Four of the objects can be modelled as protostars with infalling envelopes, two as young stars surrounded by disks, and the remaining two objects have double-peaked SEDs. We model the double-peaked sources as binaries containing a young star with a disk and a protostar. The six most luminous sources are found in a dense group within a 0.15 x 0.25 pc region; these sources have luminosities ranging from 300 L_sun to 20 L_sun. The most embedded source (OMC-2 FIR 4) can be fit by a class 0 protostar model having a luminosity of ~50 L_sun and mass infall rate of ~10^-4 solar masses per year.Comment: Accepted by ApJ Letter

Macrophage mediated recognition and clearance of Borrelia burgdorferi elicits MyD88-dependent and -independent phagosomal signals that contribute to phagocytosis and inflammation.

BACKGROUND: Macrophages play prominent roles in bacteria recognition and clearance, including Borrelia burgdorferi (Bb), the Lyme disease spirochete. To elucidate mechanisms by which MyD88/TLR signaling enhances clearance of Bb by macrophages, we studied wildtype (WT) and MyD88 RESULTS: MyD88 CONCLUSION: Our findings show that MyD88 signaling enhances, but is not required, for bacterial uptake or phagosomal maturation and provide mechanistic insights into how MyD88-mediated phagosomal signaling enhances Bb uptake and clearance

Review of Interventional Therapies for Refractory Pediatric Migraine.

This is a review of the latest and seminal evidence in pediatric migraine. It covers the etiology and pathophysiology known today, and then will review treatment options, efficacy and safety, quality of data and indications. Though migraine is usually regarded as an infliction in adults, it is not uncommon in the pediatric population and affects up to 8% of children. Children may experience migraine differently than adults, and present not only with headache but also frequent gastrointestinal symptoms. They are frequently shorter in duration than in adults. Traditional migraine treatment in adults is less effective in children. In this population, adjunct therapies - such as interventional techniques - should be considered when traditional treatment fails, including Botulinum Toxin A (BTA) injections, peripheral nerve and ganglion blocks. BTA injections are FDA approved for migraine prophylaxis in adults, but currently not in children; however, recent evidence shows efficacy and safety in pediatric migraine management. Nerve blocks stop nociceptive afferent fibers through injection of local anesthetics, and it may be associated with the local injection of corticosteroids. Although more common in adults, recent data suggests they are safe and effective in children and adolescents. Blocking the sphenopalatine ganglion can be achieved through nasal approach, and achieves a similar action by blocking the entire ganglion. Interventional techniques may provide a key component in the alleviation of this otherwise debilitating chronic migraine pain. Though most studies have been performed in adults, new studies provide encouraging results for treatment in children

Chiral transition and monopole percolation in lattice scalar QED with quenched fermions

We study the interplay between topological observables and chiral and Higgs transitions in lattice scalar QED with quenched fermions. Emphasis is put on the chiral transition line and magnetic monopole percolation at strong gauge coupling. We confirm that at infinite gauge coupling the chiral transition is described by mean field exponents. We find a rich and complicated behaviour at the endpoint of the Higgs transition line which hampers a satisfactory analysis of the chiral transition. We study in detail an intermediate coupling, where the data are consistent both with a trivial chiral transition clearly separated from monopole percolation and with a chiral transition coincident with monopole percolation, and characterized by the same critical exponent $\nu \simeq 0.65$. We discuss the relevance (or lack thereof) of these quenched results to our understanding of the \chupiv\ model. We comment on the interplay of magnetic monopoles and fermion dynamics in more general contexts.Comment: 29 pages, 13 figures included, LaTeX2e (elsart

Multiband Observations of the Quasar PKS 2326-502 during Active and Quiescent Gamma-Ray States in 2010-2012

Quasi-simultaneous observations of the Flat Spectrum Radio Quasar PKS 2326-502 were carried out in the γ-ray, X-ray, UV, optical, near-infrared, and radio bands. Using these observations, we are able to characterize the spectral energy distribution (SED) of the source during two flaring and one quiescent γ-ray states. These data were used to constrain one-zone leptonic models of the SEDs of each flare and investigate the physical conditions giving rise to them. While modeling one flare required only changes in the electron spectrum compared to the quiescent state, modeling the other flare required changes in both the electron spectrum and the size of the emitting region. These results are consistent with an emerging pattern of two broad classes of flaring states seen in blazars. Type 1 flares are explained by changes solely in the electron distribution, whereas type 2 flares require a change in an additional parameter. This suggests that different flares, even in the same source, may result from different physical conditions or different regions in the jet

Axonal Dynamics of Excitatory and Inhibitory Neurons in Somatosensory Cortex

Electrophysiology-delivery of fluorescent viral vectors-and two-photon microscopy were used to demonstrate the rapidity of axonal restructuring of both excitatory and inhibitory neurons in rodent cortical layer II/III following alterations in sensory experience