Zones of information in the AVIRIS spectra

To make the best use of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data an investigator needs to know the ratio of signal to random variability or noise (S/N ratio). The signal is land-cover dependent and decreases with both wavelength and atmospheric absorption and random noise comprises sensor noise and intra-pixel variability. The three existing methods for estimating the S/N ratio are inadequate as typical laboratory methods inflate, while dark current and image methods deflate the S/N ratio. We propose a new procedure called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intra-pixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season

Estimating the signal-to-noise ratio of AVIRIS data

To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors

Stand Up And Tell Them You\u27re From Detroit:belonging, Attachment, And Regional Identity Among Suburban Detroiters

Research shows that communities with a broadly embraced regional identity provide residents with a more gratifying social experience. A regional identity often emerges when residents exhibit a sense of belonging and attachment to their community. Detroit provides an interesting canvas to explore these concepts given a long history of tension between the city of Detroit and its suburbs. Despite these challenges, anecdotal evidence of suburban solidarity with the city exists. Using in-depth interviews with long-time residents of suburban Detroit, I explore the meaning of being a Detroiter. Why are some suburbanites eager - and others reluctant - to embrace a Detroiter identity? I found that a regional identity embraced by residents of Suburban Detroit is weak, ambiguous, and, in a few cases, non-existent. Those who lack attachment to the region struggle to articulate any type of cultural or experiential characteristic that binds them with their neighbors. Those who do exhibit attachment to the region, do so with little recognition of the institutional and systemic racism that has plagued the community, particularly as it relates to the region\u27s predominately black central city

Development of a New Measure of Men\u27s Objectification of Women: Factor Structure Test Retest Validity

Objectification involves treating individuals on the basis of their external rather than internal features. This study focused on the continued construction and development of an individual difference measure of men\u27s objectification of women. Our measure was meant to quantify and define the idea of objectification. The first part of this study (Zolot, 2003), completed last year, created the initial item pool of 66 items and a four-factor structure for our measure. In this study we refined our measure based on previous factor analysis and added new items in order to extend and clarify these factors and test ideas about sexual objectification. We investigated the reliability of both the 41 items in our measure and the reliability of our measure over time with a sample of college-aged men. Through this we have produced a 22 item measure with an internal consistency of 0.92 and a test-rest reliability correlation of r (35) = 0.88,p \u3c 0.01, and a condensed 12 item measure with an internal consistency of 0.86 and a test-rest reliability correlation of r (35) = 0.88,p \u3c 0.01. Factor analysis on both of these forms has given us three sub-scales of objectification: internalized sexual objectification, disempathy and commenting about women\u27s bodies, and insulting unattractive women. A proposed test of construct validity is also discussed

Music 2025 : The Music Data Dilemma: issues facing the music industry in improving data management

© Crown Copyright 2019Music 2025ʼ investigates the infrastructure issues around the management of digital data in an increasingly stream driven industry. The findings are the culmination of over 50 interviews with high profile music industry representatives across the sector and reflects key issues as well as areas of consensus and contrasting views. The findings reveal whilst there are great examples of data initiatives across the value chain, there are opportunities to improve efficiency and interoperability

Issues in the design of switched linear systems : a benchmark study

In this paper we present a tutorial overview of some of the issues that arise in the design of switched linear control systems. Particular emphasis is given to issues relating to stability and control system realisation. A benchmark regulation problem is then presented. This problem is most naturally solved by means of a switched control design. The challenge to the community is to design a control system that meets the required performance specifications and permits the application of rigorous analysis techniques. A simple design solution is presented and the limitations of currently available analysis techniques are illustrated with reference to this example

Seasonal LAI in slash pine estimated with LANDSAT TM

The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using LANDSAT Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R(sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of LANDSAT TM data for studying seasonal dynamics in forest canopies

Automatic Categorization of Statute Documents

Automatic classification offers publishers of large document collections the possibility of improved production efficiencies in print and online environments. In this paper we explore the possibility of automating the classification of statutory legal materials through the application of machine learning software designed to generate automatic text categorization. Our investigations focus on a specific methodology. Our plan aimed to train classifications from a pre-classified dataset of statute documents and associated index references. Accordingly, we observed that each index feature I like 'insurance', or 'corporations' appended a set of document locators. These locators make up the local collection for that index feature. The total of all documents in the dataset, whether assigned an index feature or not, makes up the global collection. The fundamental idea was to develop an algorithm based on text features whose frequency in the local collection was high but whose frequency in the global collection was moderate to low. The system would be provided with a set of descriptors taken from the text of statute documents from which it generates, by algorithm, a lexicon. The lexicon is evaluated by domain experts who assess its relationship to the semantic content of the index feature sought to be modeled. Once a satisfying lexicon has been created, machine learning software is used to generate classification rules from the lexicon. The rules in turn .generate classifications for documents in a test collection