Jerry E. Solomon

Imaging Spectrometry for Earth Remote Sensing

Imaging spectrometry, a new technique for the remote sensing of the earth, is now technically feasible from aircraft and spacecraft. The initial results show that remote, direct identification of surface materials on a picture-element basis can be accomplished by proper sampling of absorption features in the reflectance spectrum. The airborne and spaceborne sensors are capable of acquiring images simultaneously in 100 to 200 contiguous spectral bands. The ability to acquire laboratory-like spectra remotely is a major advance in remote sensing capability. Concomitant advances in computer technology for the reduction and storage of such potentially massive data sets are at hand, and new analytic techniques are being developed to extract the full information content of the data. The emphasis on the deterministic approach to multispectral data analysis as opposed to the statistical approaches used in the past should stimulate the development of new digital image-processing methodologies.

Image processing software for imaging spectrometry data analysis

Abstract The advent of a new generation of remote sensing instruments, called imaging spectrometers, promises to provide scientists a greatly enhanced capability for detailed observations of the earths surface. These instruments collect image data in literally hundreds of spectral channels simultaneously from the near ultraviolet through the short wavelength infrared, and are capable in many cases of providing direct surface materials identification in a manner similar to that used in laboratory reflectance spectroscopy. The volume and complexity of data produced by these instruments offers a significant challenge to traditional multispectral image analysis methods, and in fact requires the development of new approaches to efficiently manage and analyze these data sets. This paper describes a software system specifically designed to provide the science user with a powerful set of tools for carrying out exploratory analysis of imaging spectrometer data utilizing only modest computational resources.

Image processing software for imaging spectrometry

Recent advances in remote sensing have enabled scientists to collect image data in literally hundreds of spectral channels simul-taneously, from the near ultraviolet through the short-wavelength infrared, using imaging spectrometers. In many cases this data is of sufficient resolution to provide a direct surface materials identification. Yet the volume and complexity of the data produced requires new algorithms and approaches beyond those traditionally used for multispectral image analysis, including algorithms for fast image segmentation, spectral identification and mixture analysis. This paper describes a software system specifically designed to provide the scientist with the tools necessary for exploratory analysis of imaging spectrometer data using only modest computa-tional resources.

Automatic classification of protein sequences into structure/function groups via parallel cascade identification: a feasibility study.

AbstractA recent paper introduced the approach of using nonlinear system identification as a means for automatically classifying protein sequences into their structure/function families. The particular technique utilized, known as parallel cascade identification (PCI), could train classifiers on a very limited set of exemplars from the protein families to be distinguished and still achieve impressively good two-way classifications. For the nonlinear system classifiers to have numerical inputs, each amino acid in the protein was mapped into a corresponding hydrophobicity value, and the resulting hydrophobicity profile was used in place of the primary amino acid sequence. While the ensuing classification accuracy was gratifying, the use of (Rose scale) hydrophobicity values had some disadvantages. These included representing multiple amino acids by the same value, weighting some amino acids more heavily than others, and covering a narrow numerical range, resulting in a poor input for system identification. This paper introduces binary and multilevel sequence codes to represent amino acids, for use in protein classification. The new binary and multilevel sequences, which are still able to encode information such as hydrophobicity, polarity, and charge, avoid the above disadvantages and increase classification accuracy. Indeed, over a much larger test set than in the original study, parallel cascade models using numerical profiles constructed with the new codes achieved slightly higher two-way classification rates than did hidden Markov models (HMMs) using the primary amino acid sequences, and combining PCI and HMM approaches increased accuracy.

BioNEMS: Nanomechanical Systems for Single-Molecule Biophysics

Techniques from nanoscience now enable the creation of ultrasmall electronic devices. Among these, nanoelectromechanical systems (NEMS) in particular offer unprecedented opportunities for sensitive chemical, biological, and physical measurements [1]. For vacuum-based applications NEMS provide extremely high force and mass sensitivity, ultimately below the attonewton and single-Dalton level respectively. In fluidic media, even though the high quality factors attainable in vacuum become precipitously damped due to fluid coupling, extremely small device size and high compliance still yield force sensitivity at the piconewton level - i.e., smaller than that, on average, required to break individual hydrogen bonds that are the fundamental structural elements underlying molecular recognition processes. A profound and unique new feature of nanoscale fluid-based mechanical sensors is that they offer the advantage of unprecedented signal bandwidth (»1 MHz), even at piconewton force levels. Their combined sensitivity and temporal resolution is destined to enable real-time observations of stochastic single-molecular biochemical processes down to the sub-microsecond regime [2].

Parallel cascade identification as a means for automatically classifying protein sequences into structure/function groups

Abstract. Current methods for automatically classifying protein sequences into structure/function groups, based on their hydrophobicity profiles, have typically required large training sets. The most successful of these methods are based on hidden Markov models, but may require hundreds of exemplars for training in order to obtain consistent results. In this paper, we describe a new approach, based on nonlinear system identification, which appears to require little training data to achieve highly promising results.

Characterization of a human chromosome 22 enriched bacterial artificial chromosome sublibrary

Selection of chromosomal sublibraries from total human genomic libraries is critical for chromosome-based physical mapping approaches. We have previously reported a method of screening total human genomic library using flow sorted chromosomal DNA as a hybridization probe and selection of a human chromosome 22-enriched sublibrary from a total human bacterial artificial chromosome (BAC) library (Nucleic Acids Res 1995; 23: 1838-39). We describe here further details of the method of construction as well as characterization of the chromosome 22-enriched sublibrary thus constructed. Nearly 40% of the BAC clones that have been mapped by fluorescence in situ hybridization (FISH) analysis were localized to chromosome 22. By screening the sublibrary using chromosome 22-specific hybridization probes, we estimated that the sublibrary represents at least 2.5 x coverage of chromosome 22. This is in good agreement with the results from FISH mapping experiments. FISH map data also indicate that chromosome 22-specific BACs in the sublibrary represent all the subregions of chromosome 22.

Parallel cascade identification and its application to protein family prediction

Parallel cascade identification is a method for modeling dynamic systems with possibly high order nonlinearities and lengthy memory, given only input/output data for the system gathered in an experiment. While the method was originally proposed for nonlinear system identification, two recent papers have illustrated its utility for protein family prediction. One strength of this approach is the capability of training effective parallel cascade classifiers from very little training data. Indeed, when the amount of training exemplars is limited, and when distinctions between a small number of categories suffice, parallel cascade identification can outperform some state-of-the-art techniques. Moreover, the unusual approach taken by this method enables it to be effectively combined with other techniques to significantly improve accuracy. In this paper, parallel cascade identification is first reviewed, and its use in a variety of different fields is surveyed. Then protein family prediction via this method is considered in detail, and some particularly useful applications are pointed out.

Parallel cascade recognition of exon and intron DNA sequences.

AbstractMany of the current procedures for detecting coding regions on human DNA sequences combine a number of individual techniques such as discriminant analysis and neural net methods. Recent papers have used techniques from nonlinear systems identification, in particular, parallel cascade identification (PCI), as one means for classifying protein sequences into their structure/function groups. In the present paper, PCI is used in a pilot study to distinguish exon (coding) from intron (noncoding; interspersed within genes) human DNA sequences. Only the first exon and first intron sequences with known boundaries in genomic DNA from the βT-cell receptor locus were used for training. Then, the parallel cascade classifiers were able to achieve classification rates of about 89% on novel sequences in a test set, and averaged about 82% when results of a blind test were included. In testing over a much wider range of human nucleotide sequences, PCI classifiers averaged 83.6% correct classifications. These results indicate that parallel cascade classifiers may be useful components in future coding region detection programs.

Imaging Spectrometry: Technology And Applications

The development of imaging spectrometer instrument systems over the past six years represents a major advance in multispectral remote sensing technology. These instruments, which collect hundreds of narrow, contiguous spectral bands simultaneously, provide remote sensing scientists with extremely detailed data related to the earths surface. In addition to the technology advances represented by this type of remote sensing instrumentation, there is a concommitant requirement for technology development in the area of computational analysis of these high volume data sets. This paper reviews some critical aspects of both the instrument and processing technology involved with imaging spectrometry for remote sensing applications.