Showing papers on "Change detection published in 1982"

Automatic interaction detection

Abstract: INTRODUCTION Automatic Interaction Detection (AID) is a family of methods for handling regression-type data in a way that is almost free of the usual assumptions necessary to process the data using linear hypothesis methods. In AID, one has a dependent variable Y which one wishes to predict, and a vector of predictors X from which to predict Y. The predictors are all categorical (i.e. either nominal or ordinal), and generally take on only a few possible values. Interval predictors may be reduced to this form by grouping their possible values into classes, and then using the (ordinal) class variable as the predictor. Various different methods within the AID family have been devised for situations in which the dependent variable Y is: (a) a scalar interval variable, (b) a scalar nominal variable, (c) a vector of interval variables. Other possibilities such as an ordinal Y or a vector of nominal Y are easy to fit into the general conceptual framework of AID. The name AID suggests that the function of the technique is to discover whether the linear hypothesis model predicting Y from X contains only main effects, or whether interactions also occur. This is indeed one of the things that AID can do, but it has a number of other uses as well, which we consider overshadow this use in importance. Before going into a detailed study of the aims and methods of AID, it may help to consider a simple example.

Urban change detection procedures using Landsat digital data

Abstract: Landsat multispectral scanner data was applied to an urban change detection problem in Denver, CO. A dichotomous key yielding ten stages of residential development at the urban fringe was developed. This heuristic model allowed one to identify certain stages of development which are difficult to detect when performing digital change detection using Landsat data. The stages of development were evaluated in terms of their spectral and derived textural characteristics. Landsat band 5 (0.6-0.7 micron) and texture data produced change detection maps which were approximately 81 percent accurate. Results indicated that the stage of development and the spectral/textural features affect the change in the spectral values used for change detection. These preliminary findings will hopefully prove valuable for improved change detection at the urban fringe.

Updating Landsat-derived land-cover maps using change detection and masking techniques

Abstract: The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.

Recent Measurements Of Earth Background Spatial Radiance Variations

Abstract: IBM, Westlake Village, California 91361Spatial radiance variations, when combined with sensor or scene motion, have been recognized as a significant source of clutter forstaring infrared surveillance sensors. In this paper, data acquired using the NASA Daedalus multi -spectral scanner is examined. On threeflights of this airborne high resolution and large extent sensor system, a variety of earth background scenes was collected. Significant spatialradiance variations were measured in mountainous and agricultural earth backgrounds. Data was also collected to allow quantification of theradiance gradient at important interfaces such as between cloud and terrain.IntroductionThe application of staring infrared focal planes to short -term change detection has led to an increased interest in the form and structureof the earth background. Short -term change detection (or motion detection) of low intensity objects implies that even small spatial varia-tions in radiance when observed by an imperfect platform can lead to false reports (either isolated false points or false tracks). Backgroundvariations which result in false reports or increased system noise are called clutter. It is important to emphasize that this resultant clutter is aproduct of the background, the platform motion, the sensor system, and the motion detection algorithm. Without motion there would beno spatial clutter effects in a staring sensor. In this paper, only the background aspects of clutter will be discussed. Terms such as high clut-ter or low clutter are relative and actual observed levels of clutter depend on the other three components.Examples of sensor system sensitivity to spatial variations can be seen in surveillance systems which compare data from time sample totime sample for change or motion detection. Small instabilities in the viewing platform or slight image motion can lead to background spa-tial variations (such as edges) being interpreted as temporal change. In these systems, the power spectra of the spatial frequencies of theearth background can be interpreted as a noise source. Atreatment of the various noise sources for mosaic sensors is discussed in Reference 1.Daedalus sensor measurementsThe NASA Ames Daedalus Multi-Spectral Scanner (MSS) was used to help quantify background spatial radiance variations. With thissensor a wide area survey of clutter sources is possible, allowing future scheduling of other sensors for detailed study at high resolution andin specific bands.The Daedalus is a High Altitude Multispectral Scanner currently deployed on a NASA U -2 aircraft from Ames Research Center. Thescanner is an eleven channel system, which is entirely digital with ten channels in the visible /near -visible spectral region and one channel inthe thermal infrared. The scanner has a capability of either 50 m resolution and 33 kilometer swath width or 25 m resolution and 15 kilom-eter swath width from an altitude of 65,000 feet. Characteristics of the sensor and the filters available are shown in Table 1.Table 1. Daedalus Sensor Characteristics

State change detecting circuit

Abstract: PURPOSE:To make the buffer or the like consisting of plural inverters unnecessary to facilitate making the device small-size, by using a monostable multivibrator to delay the output signal of a state change detecting circuit. CONSTITUTION:Contact information A from a data terminal is inputted to state change detecting circuits 5 and 6 and a selector 7 through a filter 2; and if a state change is detected by a state change detecting circuit 2, the state change detection signal is inputted as an interrupt signal to a data processing device 15 by a detection signal G, and the data processing device 15 reads contact information from the selector 7 by this state change detection signal. The output signal of the state change detecting circuit 5 in this state change detecting circuit is delayed by a monostable multivibrator 17.

Bayes Adaptive Estimation of the Point of Shift to the Wear-Out Phase of Reliability Systems.

Abstract: : A new family of life distributions, called the wear-out distributions, is developed on the basis of a failure rate function, which is a constant up to the change-point and strictly increasing afterwards. Properties of these wear-out distributions are derived and a Bayes adaptive procedure is developed for the estimation of the change point. Recursive formulae are given for the determination of the posterior probability that the change has occurred and of its Bayes estimator. The results of numerical simulations are given to illustrate the properties of the adaptive procedure. (Author)