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Showing papers on "Change detection published in 1994"


Journal Article
TL;DR: In this article, the authors evaluated four change detection techniques using multispectral, multitemporal SPOT data for identifying changes in hardwood forest defoliation caused by gipsy moth, Lymantria dispar L.
Abstract: Monitoring of environmental conditions such as forest defoliation by insects over large areas is facilitated by automated approaches to change detection using remotely sensed data. This study evaluated four change detection techniques using multispectral, multitemporal SPOT data for identifying changes in hardwood forest defoliation caused by gipsy moth, Lymantria dispar L. The change detection techniques considered were principal components analysis, image differencing, spectral-temporal (layered temporal) change classification, and post-classification change differencing. The study area comprised approximately 148 square kilometres in Warren and Shenandoah Counties, Yirginia. Reference information of defoliation were aerial sketch maps developed by the U.S. Forest Service

270 citations


Journal ArticleDOI
TL;DR: Although limited to spectrally-radiometrically defined change classes, results show that the relationship between reflective TM data and forest canopy change is explicit enough to be of operational use in a forest cover change stratification phase prior to a more detailed assessment.
Abstract: Digital procedures to optimize the information content of multitemporal Landsat TM data sets for forest cover change detection are described. Imagery from three different years (1984, 1986, and 1990) were calibrated to exoatmospheric reflectance to minimize sensor calibration offsets and standardize data acquisition aspects. Geometric rectification was followed by atmospheric normalization and correction routines. The normalization consisted of a statistical regression over time based on spatially well-defined and spectrally stable landscape features spanning the entire reflectance range. Linear correlation coefficients for all bitemporal band pairs ranged from 0.9884 to 0.9998. The correction mechanism used a dark object subtraction technique incorporating published values of water reflectance. The association between digital data and forest cover was maximized and interpretability enhanced by converting band-specific reflectance values into vegetation indexes. Bitemporal vegetation index pairs for each time interval (two, four, and six years) were subjected to two change detection algorithms, standardized differencing and selective principal component analysis. Optimal feature selection was based on statistical divergence measures. Although limited to spectrally-radiometrically defined change classes, results show that the relationship between reflective TM data and forest canopy change is explicit enough to be of operational use in a forest cover change stratification phase prior to a more detailed assessment. >

241 citations


Journal ArticleDOI
TL;DR: In this paper, a change detection method is applied to three remotely-sensed indicators of land-surface conditions (vegetation index, surface temperature and spatial structure) in order to improve the capability to detect and categorize subtle forms of land cover change.
Abstract: Change-vector analysis in multi-temporal space is a powerful tool to analyse the nature and magnitude of land-cover change. The change vector compares the difference in the time-trajectory of a biophysical indicator for successive time periods. This change detection method is applied to three remotely-sensed indicators of land-surface conditions—vegetation index, surface temperature and spatial structure—in order to improve the capability to detect and categorize subtle forms of land-cover change. It is tested in a region of West Africa, using multi-temporal Local Area Coverage imagery obtained by the Advanced Very-High Resolution Radiometer on NOAA-9 and NOAA-II orbiting platforms. The three indicators show a low degree of redundancy and detect different land-cover change processes, which operate at different time scales. Change vector analysis is being developed for application to the land-cover change product to be produced using NASA's Moderate-Resolution Imaging Spectroradiometer instrument,...

200 citations


Journal ArticleDOI
TL;DR: It is shown here that this approach based on the so-called ‘asymptotic local’ approach for change detection is of much wider applicability: model reduction can be enforced, biased identification procedures can be used, and finally one can even get rid of identification and use instead some much simpler Monte-Carlo estimation technique prior to change detection.

152 citations


Proceedings ArticleDOI
24 Jun 1994
TL;DR: The authors have applied their automated 3D change detection system to a multiple sclerosis study in which each patient had been imaged over 20 times for the purpose of tracking lesion evolution, and described preliminary registration performance analysis using this data.
Abstract: The authors are developing an automated 3D change detection system which accurately registers medical imagery (e.g., MRI or CT) of the same patient from different times for diagnosing pathologies, monitoring treatment, and tracking tissue changes. The system employs a combination of energy-minimization registration techniques to achieve accurate and robust alignment of 3D data sets. The bases for the registration are 3D surfaces extracted from the 3D imagery. Resultant structural changes in the data are identified by using an adaptive segmentation technique to automatically determine tissue morphology. The novel contributions of this work are its end-to-end automation of the change detection process and its high accuracy in monitoring and highlighting such physiological changes. The authors have applied this system to a multiple sclerosis study in which each patient had been imaged over 20 times for the purpose of tracking lesion evolution. This report describes preliminary registration performance analysis using this data. >

53 citations


Journal ArticleDOI
TL;DR: In this paper, the authors report on an experiment to assess the quality of machine-matches for stereo-parallax detection in radar images, and find that there are often ±2 pixel differences between the experienced stereo operator and the best image-matching method, based on a normalized cross-correlation measure.
Abstract: Stereo-parallax measurements for digital elevation excitation, matching for change detection, and creation of stacks of multi-temporal or multi-incidence angle images (“cubes”) of co-registered images all can be supported by automation via computerized image correlation. Precision parallax measurements are traditionally made by an experienced stereo-operator. Automated methods have been investigated, but have not found wide acceptance. Radar mapping, for example in NASA's Magellan mission to planet Venus, has a requirement to develop Digital Elevation Models (DEM) from stereo radar images, and to match multiple coverages from sequential image acquisition cycles. We report on an experiment to assess the quality of machine-matches for stereo-parallax detection in radar images, and find that there are often ±2 pixel differences between the experienced stereo-operator and the best image-matching method, based on a normalized cross-correlation measure. When comparing this to SPOT-images and to scanned aerial photography, we note that errors of machine matching are typically smaller in those images than in radar images, with SPOT data producing automated matches with subpixel differences to manual matches.

40 citations


01 Jan 1994
TL;DR: This work has developed a validation mechanism that implements the first step towards a system for detecting changes in images of aerial scenes, and uses shadows as 3-0 clues to help validate the model.
Abstract: An important application of machine vision is to provide a means to monitor a scene over a period of time and report changes in the content of the scene. We have developed a validation mechanism that implements the first step towards a system for detecting changes in images of aerial scenes. By validation we mean the confirmation of thepresence of model objects in the image. Our system uses a 3-0 site model of the scene as a basis for model validation, and eventually for detecting changes and to update the site model. The scenario for our present validation system consists of adding a new image to a database associated with the site. The validation process is implemented in three steps: registration of the image to the model, or equivalently, determination of the position and orientation of the camera; matching of model features to image features; and validation of the objects in the model. Our system processes the new image monocularly and uses shadows as 3-0 clues to help validate the model. The system has been tested using a hand-generated site model and several images of a 500:l scale model of the site, acpired form several viewpoints.

35 citations


Proceedings ArticleDOI
05 Sep 1994
TL;DR: In this article, a real-time change detection method for multiple object localization from real-world image sequences is presented, where limits, quality and time performances of the described pixel-oriented method are compared with other existing techniques.
Abstract: The aim of this paper is to show a real-time change detection method for multiple object localization from real world image sequences. Limits, quality and time performances of the described pixel-oriented method are outlined comparing it with other existing techniques. Results are presented by applying the the technique described in the architecture of a real-time surveillance system for visual control of an unattended level-crossing. The localization of detected objects is also addressed and tested on real scenes where illumination is not assumed to be constant. >

33 citations


Patent
Rudolf Mester1, Til Aach1
07 Apr 1994
TL;DR: In this paper, an image pick-up unit (3), an arithmetic unit (1), and a storage device (2) are used to detect changes in moving images. But the system is not suitable for the detection of moving objects.
Abstract: A method and system for detecting changes in moving images, wherein the system includes an image pick-up unit (3), an arithmetic unit (1), and a storage device (2). The image pick-up unit (3) records images by pixels. The arithmetic unit (1) files the video signals by pixels in the storage device (2). The arithmetic unit (1) calculates the change in the video signals of the blocks of the image n+1 with reference to the preceding image n. Different values are assigned to the blocks depending on whether the change is larger or smaller than a threshold, i.e., depending on whether the blocks are supposed to be treated further as changed or not changed. The threshold becomes all the lower the more adjacent blocks that have been designated as changed when compared to their thresholds.

25 citations


Patent
27 Jul 1994
TL;DR: In this paper, an image sequence providing means 11 provides an image (image sequence) to a feature extracting means 1. The feature extracting calculates the feature vector for each picture element or previously decided small area in the applied image sequence.
Abstract: PURPOSE:To distinguishedly detect only a moving object by calculating the probability of an abnormal change from the occurance probability distribution of a feature vector and the feature vector calculated from a still state and a normal change state from a timewise image sequence. CONSTITUTION:An image sequence providing means 11 provides an image (image sequence) to a feature extracting means 1. The feature extracting means 1 calculates the feature vector for each picture element or previously decided small area in the applied image sequence. A probability distribution calculating means 2 calculates the occurance probability distribution of the feature vector in the time-space area of that picture element or small area. A change probability calculating means 3 calculates the probability of change presence/absence from the feature vector of a change detecting object image and the probability distribution stored in the probability distribution calculating means 2. A change presence/absence discriminating means 4 discriminates the change presence/ absence from the calculated probability value. A result display means 12 displays the result of the change detection by the change discriminating means 4 for a user. Thus, the abnormal change and normal change of the mobile object can be distinguishedly detected.

15 citations


Patent
10 Feb 1994
TL;DR: In this paper, a moving picture processor is equipped with an image input part 1 which converts the chrominance components (RGB) of the moving picture into digital image data and then separates the data into a brightness signal (Y) and a 1st and a 2nd hue signal (C1 and C2) between the separated frames, and a scene change decision is made according to the scene detection results of the 1st, 2nd, and 3rd scene change detection parts 2-4.
Abstract: PURPOSE:To detect an accurate scene and extract effective index information from moving picture materials. CONSTITUTION:The moving picture processor is equipped with an image input part 1 which converts the chrominance components (RGB) of the moving picture into digital image data and then separates the data into a brightness signal (Y) and a 1st and a 2nd hue signal (C1 and C2), frame by frame, a 1st scene change detection part 2 which detects a scene change according to the brightness signal (Y)' between the separated frames. a 2nd scene change detection part 3 which detects a scene change according to the 1st hue signal (C1) between the separated frames, a 3rd scene change detection part 4 which detects a scene change according to the 2nd hue signal (C2) between the separated frames, and a moving picture scene change decision part 5 which decides a scene change of the moving picture according to the scene detection results of the 1st, 2nd, and 3rd scene change detection parts 2-4.

Journal ArticleDOI
TL;DR: It is shown that the use of a fixed correlation threshold for change detection is insufficient to deal with a wider range of scene domains as the best-matched correlation coefficient of an image is contrast dependent.

Patent
25 Feb 1994
TL;DR: In this paper, a transition detection delay unit for each address bit of the memory is proposed, which is responsive to a change in an associated address bit to provide a clock output pulse of predetermined duration.
Abstract: An address change detection system detects a change in an address input in a memory to initiate a read or write operation. The address change detection system uses a transition detection delay unit for each address bit of the memory. The transition detection delay unit is responsive to a change in an associated address bit to provide a clock output pulse of predetermined duration. The transition detection delay unit comprises a latch which is coupled to the associated address bit, and a pair of Delay Ring Segment Buffers, each coupled to a respective output of the latch. The output of the Delay Ring Segment Buffer is provided to cascaded NAND gates to form the output of the transition detection delay unit. The outputs of all of the transition detection delay units are provided to an OR gate, the output of which provides an indication of an address change. The address change detection circuit can also be used to detect a change in at least one of a plurality of binary valued inputs for applications other than memory systems.

Proceedings ArticleDOI
05 Dec 1994
TL;DR: This work has developed a validation mechanism that implements the first step towards a system for detecting changes in images of aerial scenes, using a 3-D site model of the scene as a basis for model validation, and eventually for detectingChanges and to update the site model.
Abstract: An important application of machine vision is to provide a means to monitor a scene over a period of time and report changes in the content of the scene. We have developed a validation mechanism that implements the first step towards a system for detecting changes in images of aerial scenes. By validation we mean the confirmation of the presence of model objects in the image. Our system uses a 3-D site model of the scene as a basis for model validation, and eventually for detecting changes and to update the site model. The scenario for our present validation system consists of adding a new image to a database associated with the site. The validation process is implemented in three steps: registration of the image to the model, or equivalently, determination of the position and orientation of the camera; matching of model features to image features; and validation of the objects in the model. Our system processes the new image monocularly and uses shadows as 3-D clues to help validate the model. The system has been tested using a hand-generated site model and several images of a 500:1 scale model of the site, acquired form several viewpoints. >


Proceedings ArticleDOI
30 Dec 1994
TL;DR: In this article, a neural network is trained to combine structural measures (e.g., edges, corners, and texture) to combine the different change measures in an appropriate manner.
Abstract: Various methods for automatic change detection in multi-temporal LANDSAT-TM images are described. In contrast to most previous work in change detection, which has operated at a pixel level, we operate at a parcel level (within a minimum size of 25 Ha). This makes it easier to employ structural measures (e.g. based on edges, corners, and texture) as well as correlation methods since these approaches cannot be calculated at each pixel independently. A neural network is trained to combine the different change measures in an appropriate manner.

01 Jan 1994
TL;DR: An automated 3D change detection system which accurately registers medical imagery of the same patient from different times for diagnosing pathologies, monitoring treatment, and tracking tissue changes, and is applied to a rigid registration problem, namely head registration for multiple sclerosis change detection.
Abstract: We are developing an automated 3D change detection system which accurately registers medical imagery (e.g., MRI or CT) of the same patient from different times for diagnosing pathologies, monitoring treatment, and tracking tissue changes. The system employs a combination of energyminimization registration techniques to achieve ac~ curate and robust alignment of 3D data sets. The bases for the registration are 3D surfaces extracted from the 3D imagery. Resultant changes in the data are identified by differencing registered normalized intensity images or comparing measurements of the same segmented tissue over time. The contributions of this work are (1) automation the registration process, (2) high registration accuracy, and (3) registration stability in the presence of noise, outliers, and data deviations. We have applied this system to a rigid registration problem, namely head registration for multiple sclerosis change detection, and are exploring other rigid and flexible registration applications.

Proceedings ArticleDOI
29 Jun 1994
TL;DR: It is proved that this algorithm is asymptotically optimal in a certain class of sequential change detection/isolation algorithms.
Abstract: The problem of optimal detecting and isolating faults in systems with random disturbances is discussed. The fault (change) detection problem has received extensive research attention. On the contrary, the change isolation is still an unsolved problem. A system with abrupt changes and random disturbances is considered. A joint criterion of optimality for the detection/isolation problem is introduced and a change detection/isolation algorithm is designed. It is proved that this algorithm is asymptotically optimal in a certain class of sequential change detection/isolation algorithms. The theoretical results are illustrated by examples of navigation system integrity monitoring.

Proceedings ArticleDOI
08 Aug 1994
TL;DR: The aim of the research described is to prototype algorithms which would form a suitable basis for an operational, automated map update system based on edge matching and object analysis which could be applied with or without a ground map and with various levels of user intervention.
Abstract: The aim of the research described is to prototype algorithms which would form a suitable basis for an operational, automated map update system. Using forestry in the U.K as an example, algorithms were developed based on edge matching and object analysis which could be applied with or without a ground map and with various levels of user intervention. These were tested using Thematic Mapper data and digitised ground cover maps on the Elchies Forest near Elgin, Scotland. Where possible, results were augmented using evidential reasoning to reduce false alarms based on specific knowledge of forestry practice. Results indicate that even with the minimum of user intervention major areas of change can be detected. Increasing user intervention can reduce processing complexity and time, but is less suitable for an automated system. The paper presents the overall framework in which the techniques might be exploited. >

Proceedings ArticleDOI
08 Aug 1994
TL;DR: A novel image processing system which performs constant false-alarm rate (CFAR) change detection and model-based feature classification on temporally separated and precision registered images is described.
Abstract: Describes a novel image processing system which performs constant false-alarm rate (CFAR) change detection and model-based feature classification on temporally separated and precision registered images. The authors present results of algorithms performed on 0.5 m to 2 m resolution scanned aerial photographs of the Yuma, Arizona region to identify changes that correspond to man-made targets and new roads and to classify false changes. >

Proceedings ArticleDOI
13 Nov 1994
TL;DR: A theory is paid to a theory which provides a common framework for designing fast and noise-robust methods for the three tasks of interest, and an application is presented which deals with intruder detection in a railway-crossing area.
Abstract: A real-time visual surveillance system is based on three main image processing phases, devoted to extract information about the observed scene: change detection, focus of attention, feature-extraction. In this paper attention is paid to a theory (i.e., binary statistical morphology) which provides a common framework for designing fast and noise-robust methods for the three tasks of interest. The main theoretical novelty is to establish a link between binary statistical morphology and voting methods. An application is presented which deals with intruder detection in a railway-crossing area. >

Proceedings ArticleDOI
08 Aug 1994
TL;DR: Segmentation based change detection in ERS-1 images of agricultural and salt playa scenes is discussed and the segmentation algorithm, RWSEG, is briefly described.
Abstract: Segmentation based change detection in ERS-1 images of agricultural and salt playa scenes is discussed. The segmentation algorithm, RWSEG, is briefly described. The authors primarily concentrate on structural rather than radiometric change. Structural change is analysed by overlaying segment boundaries. This reveals a surprising amount of structural similarity in the image sequences. However, this simplistic approach does not allow for boundaries being slightly offset due to speckle and gradual gradients. >

Proceedings ArticleDOI
25 Feb 1994
TL;DR: In this article, the authors proposed Dual Difference Filtering (DDF), a symmetric method that generalizes the concept of interpolation for detecting subtle localized changes in a sequence of background scenes.
Abstract: Many digital imaging applications require the detection of subtle localized changes in a sequence of background scenes. Often the principle limitation to the process is uncontrollable pointing changes in an electro-optic sensor, which result in apparent image displacements in the sequence. The interpolation of one of the images followed by subtraction from another has served as a mainstay for change detection. This method is extremely suboptimal within the general context of linear filtering. Conventional registration/subtraction is replaced in this report by dual difference filtering (DDF), a symmetric method that generalizes the concept of interpolation. Over a wide range of images, DDFs have been shown to increase the signal to clutter ratio for small moving targets by an average of 31 dB, compared to older, interpolative methods. A fundamental optimization equation for DDFs is derived, and a solution is presented for a spatial spectrum typical of imagery. DDFs are shown to permit the identification of subtle differences in image sequences that were not detectable with previous methods. It is also shown that, in principle, all first-order aliasing errors can be eliminated by using DDFs. Applications include medical imaging, autonomous and cued surveillance, remote sensing, and astronomy.

Proceedings ArticleDOI
05 Feb 1994
TL;DR: In this article, the authors developed a change detection algorithm called Minimal Time-Change Detection Algorithm (MT-CDA) which detects the instant of change as quickly as possible with false-alarm probability below a certain specified level.
Abstract: An aerospace vehicle may operate throughout a wide range of flight environmental conditions that affect its dynamic characteristics. Even when the control design incorporates a degree of robustness, system parameters may drift enough to cause its performance to degrade below an acceptable level. The object of this paper is to develop a change detection algorithm so that we can build a highly adaptive control system applicable to aircraft systems. The idea is to detect system changes with minimal time delay. The algorithm developed is called Minimal Time-Change Detection Algorithm (MT-CDA) which detects the instant of change as quickly as possible with false-alarm probability below a certain specified level. Simulation results for the aircraft lateral motion with a known or unknown change in control gain matrices, in the presence of doublet input, indicate that the algorithm works fairly well as theory indicates though there is a difficulty in deciding the exact amount of change in some situations. One of MT-CDA distinguishing properties is that detection delay of MT-CDA is superior to that of Whiteness Test. >

Proceedings ArticleDOI
05 Dec 1994
TL;DR: The basic MSE concept is reviewed and the approach using three operational concepts taken from the RADIUS project, quick-look, detection and counting and focussed change detection is illustrated.
Abstract: Over the last several years the concept of model-supported exploitation (MSE) has evolved to a point where relatively simple computer vision algorithms can extract significant intelligence information from aerial images in a robust and reliable manner. Information extraction is enabled by the use of detailed 3D site models which provide an extensive context for the application of image analysis algorithms. This paper reviews the basic MSE concept and illustrates the approach using three operational concepts taken from the RADIUS project, quick-look, detection and counting and focussed change detection. >

Patent
28 Sep 1994
TL;DR: In this paper, the reproduction timing information of a video at the time of recording video data in the scene change detection of the video is used to realize intermittent recording fitted to a minute scene change.
Abstract: PURPOSE: To realize intermittent recording fitted to a minute scene change by recording reproduction timing information of a video at the time of recording video data in the scene change detection of the video. CONSTITUTION: Video data is inputted from an input terminal 101 to a video data write means 102 and a scene change detection means 103. Sound data is inputted from an input terminal 104 to the scene change detection means 103 and a sound data write means 105. The scene change detection means 103 detects the scene change and gives the detection signal 106 to a write control means 107. The write control means 107 outputs a video data write signal 108 and reproduction timing information 109. The video data write means 102 executes a signal processing required for recording on video data 110 from the scene change detection means 103 and writes it into a semiconductor memory 111. Reproduction timing information 109 is inputted to a control information write means 112 and it is written into the semiconductor memory 111.

Proceedings ArticleDOI
09 Jun 1994
TL;DR: In this article, an Environmental Research Institute of Michigan (ERIM) Rail SAR data collection was used to evaluate the performance of incoherent change detection on targets in foliage using a number of frequency bands which simulate the high-band Stanford Research Institute, International (SRI) ultra wideband radar (UWBR), the Loral miniature SAR (MSAR) (500-800 MHz), and the Naval Air Warfare Center (NAWC) P-3 upgraded UWBR (200-900 MHz) sensor.
Abstract: The effectiveness of applying incoherent change detection to multipass SAR images and targets in foliage is affected by the operating radar frequency band. Incoherent change detection is achieved by taking the weighted difference of the magnitude of two well registered passes of SAR imagery. Items which change between two passes, such as a target present in the first pass and not present in the second pass, will appear in the weighted difference image. With well-registered wideband SAR imagery, images can be divided into frequency bands and evaluated using incoherent change detection. An Environmental Research Institute of Michigan (ERIM) Rail SAR experiment provides such a data collection. The Rail SAR is characterized by polarimetric, wideband (400 MHz - 1.3 GHz), multipass (with and without targets), well-registered SAR images. The ERIM Rail SAR data is divided into a number of frequency bands which simulate the high- band Stanford Research Institute, International (SRI) ultra- wideband radar (UWBR) (350-550 MHz), the Loral miniature SAR (MSAR) (500-800 MHz), and the Naval Air Warfare Center (NAWC) P-3 upgraded UWBR (200-900 MHz) sensor. This paper shows how these sensors work on targets in foliage using incoherent change detection and provides an experimental measurement of upper-bound performance.

Proceedings ArticleDOI
30 Dec 1994
TL;DR: In this article, a change analysis with weight of significance between two multi-temporal multi-spectral images is proposed, where image data are projected onto a feature space in which the assigned change is emphasized, and temporal changes between two images are detected with suppression of irrelevant changes.
Abstract: A new method is proposed for change analysis with weight of significance between two multi-temporal multi-spectral images.This method gives us areas which indicate the assigned temporal change, for example, from vegetation to bare soil. Image dataare projected onto a feature space in which the assigned change is emphasized, and temporal changes between two images aredetected with suppression ofirrelevant changes. The validity ofthe method is confirmed by numerical simulation. The methodis successfully applied to actual multi-temporal and multi-spectral images. 1. INTRODUCTION Change analysis is one of the most important processings for monitoring environment. It provides us with information aboutchange, for example, of natural environment to urban area. In change analysis, it is required to analyze the particular changerather than to detect all types of change. There have been a lot of methods reported for change detection[1]. Most of them,however, detect all types of change without specifying the change[2], or analyze the changes categorically using supervisedclassification of two multi-temporal images[3][4]. In the former, the change to be analyzed is detected indirectly using cluster-ing, and in the latter, classification error is serious. A direct method has been required.We propose a method PACE (Particular Change Extractor) for analyzing temporal change with weight of significance. Themethod PACE enables us to select a particular type of change such as from vegetation to bare soil, and extracts areas in whichspectral pattern has the particular change between two multi-temporal and multi-spectral images. The method consists of threeprocedures; data projection, change detection and suppression of irrelevant changes. In the first two, we produce a feature spacederived from an assigned change so that the change is emphasized. Canonical correlation analysis[5] is used for it. We projecta set of image data onto the feature space. The difference between two transformed image data indicates temporal changes. As

Proceedings ArticleDOI
09 Jun 1994
TL;DR: In this article, a split-aperture detection technique was proposed to suppress background clutter by separating the synthetic aperture into subapertures during image formation, in the hope that clutter returns will be nearly isotropic over small variations of aspect angle and will look similar in each image, while man-made objects will provide anisotropic returns over the same angular variation and therefore will appear brighter in one image.
Abstract: A new technique, developed at Lincoln Laboratory, utilizes algorithms developed for two-pass change detection to exploit the differences in aspect angle dependency between target scatterers and clutter scatterers. This technique, referred to as split- aperture detection, involves forming several aspect-angle-diverse SAR images from a single flight pass over a given area by separating the synthetic aperture into sub-apertures during image formation. Change detection algorithms are then applied to these aspect-angle-diverse looks, in the hopes that clutter returns will be nearly isotropic over small variations of aspect angle and will look similar in each image, while man-made objects will provide anisotropic returns over the same angular variation and, therefore, will appear brighter in one image. In this case, the change detection algorithms will significantly suppress the background clutter (thereby significantly reducing the number of false alarms) while enhancing target detectability.

Patent
28 Oct 1994
TL;DR: In this paper, a feature point which sequentially expresses the position of an object from an image frame which has been picked up by a high-speed image- pickup means and judging whether the feature point is proper or not is extracted.
Abstract: PURPOSE: To automatically detect the momentary position change of an object by extracting a feature point which sequentially expresses the position of the object from an image frame which has been picked up by a high-speed image- pickup means and judging whether the feature point is proper or not. CONSTITUTION: A semiconductor wafer is picked up by a high-speed camera at a speed of, e.g. 200frames/sec, and an image signal 14 is input to an image processing part 11. A size measuring part 17 and a measuring-frame intersecting- point measuring part 18 measure the coordinate value of intersecting points of edges 27, 28 for the wafer with a measuring frame 30 sequentially on the basis of an image frame. A comprehensive judgment part 19 compares positions of the edges 27, 28 with fundamental image data 23. It judges that a large dislocation has been generated in the conveyance position of the wafer when a comparison difference exceeds a permissible range. It outputs an operating signal 20 to an image recording part 33 or an instruction signal 21 to the outside. In this manner, whether a position change has been generated in the wafer or not can be detected and recorded automatically. COPYRIGHT: (C)1996,JPO