Showing papers on "Change detection published in 1999"

Monitoring land-cover changes: A comparison of change detection techniques

Abstract: Six change detection procedures were tested using Landsat MultiSpectral Scanner (MSS) images for detecting areas of changes in the region of the Terminos Lagoon, a coastal zone of the State of Campeche, Mexico. The change detection techniques considered were image differencing, vegetative index differencing, selective principal components analysis (SPCA), direct multi-date unsupervised classification, post-classification change differencing and a combination of image enhancement and post-classification comparison. The accuracy of the results obtained by each technique was evaluated by comparison with aerial photographs through Kappa coefficient calculation. Post-classification comparison was found to be the most accurate procedure and presented the advantage of indicating the nature of the changes. Poor performances obtained by image enhancement procedures were attributed to the spectral variation due to differences in soil moisture and in vegetation phenology between both scenes. Methods based on classif...

Remote Sensing Change Detection: Environmental Monitoring Methods and Applications

Abstract: Applications, Project Formulation, and Analytical Approach. Survey of Multispectral Methods for Land Cover Change Analysis. North American Landscape Characterization: Dataset Development/Data Fusion. Classification-Based Change Detection: Theory and Application to the NALC Dataset. An Evaluation of the CoastWatch Change Detection Protocol in South Carolina. Methods for Detecting Conifer Change with Thematic Mapper Data. Wildfire Detection with Meteorological Satellite Data. Detection of Fires and Power Outages using DMSP-OLS Data. Change Identification using Multitemporal Spatial Mixture Analysis. Seasonal Vegetation Patterns Derived from Advanced Visible/Infrared Imaging Spectrometer Data. Vegetation Change Detection using High Spectral Resolution Vegetation Indices. Radar Remote Sensing of Wetlands. Monitoring Trends in Wetlands Vegetation Using Landsat MSS Time Series. Radar Interferometry for Environmental Change Detection. Change Detection Accuracy Assessment.

Detection of moving cast shadows for object segmentation

Abstract: To prevent moving shadows being misclassified as moving objects or parts of moving objects, this paper presents an explicit method for detection of moving cast shadows on a dominating scene background. Those shadows are generated by objects moving between a light source and the background. Moving cast shadows cause a frame difference between two succeeding images of a monocular video image sequence. For shadow detection, these frame differences are detected and classified into regions covered and regions uncovered by a moving shadow. The detection and classification assume plane background and a nonnegligible size and intensity of the light sources. A cast shadow is detected by temporal integration of the covered background regions while subtracting the uncovered background regions. The shadow detection method is integrated into an algorithm for two-dimensional (2-D) shape estimation of moving objects from the informative part of the description of the international standard ISO/MPEG-4. The extended segmentation algorithm compensates first apparent camera motion. Then, a spatially adaptive relaxation scheme estimates a change detection mask for two consecutive images. An object mask is derived from the change detection mask by elimination of changes due to background uncovered by moving objects and by elimination of changes due to background covered or uncovered by moving cast shadows. Results obtained with MPEG-4 test sequences and additional sequences show that the accuracy of object segmentation is substantially improved in presence of moving cast shadows. Objects and shadows are detected and tracked separately.

A unified approach to shot change detection and camera motion characterization

Abstract: This paper describes an original approach to partitioning of a video document into shots. Instead of an interframe similarity measure which is directly intensity based, we exploit image motion information, which is generally more intrinsic to the video structure itself. The proposed scheme aims at detecting all types of transitions between shots using a single technique and the same parameter set, rather than a set of dedicated methods. The proposed shot change detection method is related to the computation, at each time instant, of the dominant image motion represented by a two-dimensional affine model. More precisely, we analyze the temporal evolution of the size of the support associated to the estimated dominant motion. Besides, the computation of the global motion model supplies by-products, such as qualitative camera motion description, which we describe in this paper, and other possible extensions, such as mosaicing and mobile zone detection. Results on videos of various content types are reported and validate the proposed approach.

Video segmentation for content-based coding

Abstract: To provide multimedia applications with new functionalities, the new video coding standard MPEG-4 relies on a content-based representation. This requires a prior decomposition of sequences into semantically meaningful, physical objects. We formulate this problem as one of separating foreground objects from the background based on motion information. For the object of interest, a 2D binary model is derived and tracked throughout the sequence. The model points consist of edge pixels detected by the Canny operator. To accommodate rotation and changes in shape of the tracked object, the model is updated every frame. These binary models then guide the actual video object plane (VOP) extraction. Thanks to our new boundary postprocessor and the excellent edge localization properties of the Canny operator, the resulting VOP contours are very accurate. Both the model initialization and update stages exploit motion information. The main assumption underlying our approach is the existence of a dominant global motion that can be assigned to the background. Areas that do not follow this background motion indicate the presence of independently moving physical objects. Two alternative methods to identify such objects are presented. The first one employs a morphological motion filter with a new filter criterion, which measures the deviation of the locally estimated optical flow from the corresponding global motion. The second method computes a change detection mask by taking the difference between consecutive frames. The first version is more suitable for sequences with little motion, whereas the second version is better at dealing with faster moving or changing objects. Experimental results demonstrate the performance of our algorithm.

Remotely Sensed Change Detection Based on Artificial Neural Networks

Abstract: A new method for remotely sensed change detection based on artificial neural networks is presented. The algorithm for an automated land-cover change-detection system was developed and implemented based on the current neural network techniques for multispectral image classification. The suitability of application of neural networks in change detection and its related network design considerations unique to change detection were first investigated. A neural-network-based change-detection system using the backpropagation training algorithm was then developed. The trained four-layered neural network was able to provide complete categorical information about the nature of changes and detect land-cover changes with an overall accuracy of 95.6 percent for a four-class (i.e., 16 change classes) classification scheme. Using the same training data, a maximum-likelihood supervised classification produced an accuracy of 86.5 percent. The experimental results using multitemporal Landsat Thematic Mapper imagery of Wilmington, North Carolina are provided. Findings of this study demonstrated the potential and advantages of using neural network in multitemporal change analysis.

Reducing the effects of misregistration on pixel-level change detection

Abstract: A model that compensates for misregistration effects on change detection results shows promise for reducing artefacts and enhancing land change features at or near the pixel scale and for reducing noise caused by misregistered muti-temporal images. Sparse estimates of misregistration across the scene are combinedwithcalculations of spatial brightness gradients toadjust the magnitude of multi-temporal image differences. The model is tested on a multi-temporal Landsat Thematic Mapper image data set for a rapidly urbanizing landscape in southern California.

Fast speaker change detection for broadcast news transcription and indexing.

Abstract: In this paper, we describe a new speaker change detection algorithm designed for fast transcription and audio indexing of spoken broadcast news. We have designed a two-stage algorithm that begins with a gender-independent phone-class recognition pass. We collapse the phoneme inventory to only 4 broad classes and include 4 different models for non-speech, resulting in a small fast decoder that runs in less than 0.1 times real-time. The second stage of the SCD algorithm hypothesizes a speaker change boundary between every phone in the labeled input. The phone level time resolution in our approach permits the algorithm to run quickly while maintaining the same accuracy as a frame level approach. Applying the new algorithms to a large sample of broadcast news programs resulted in improvements in speaker change detection accuracy, speech recognition accuracy, and speed.

Change detection assessment using fuzzy sets and remotely sensed data: an application of topographic map revision

Abstract: This paper explores a methodology for computing the amount of changes that have occurred within an area by using remotely sensed technologies and fuzzy modelling. The discussion concentrates on the formulation of a standard procedure that, using the concept of fuzzy sets and fuzzy logic, can define the likelihood of changes detected from remotely sensed data. Furthermore, an example of how fuzzy visualisation of areas undergoing changes can be incorporated into a decision support system for prioritisation of areas requiring topographic map revision and updating is presented. By adapting the membership function of the fuzzy model to fit the shape of the histogram characterising the change image (derived from any of the common pre-classification methods of change detection), areas can be identified according to their likelihood of having undergone change during the period of observation.

Detection of partial land cover change associated with the migration of inter-class transitional zones

Abstract: Post-classification comparison techniques are frequently used in studies of change detection. If the classifications used were derived with the use of conventional 'hard' classification techniques, change detection is constrained to the identification of complete changes in class label. This may be inappropriate in circumstances when the land cover conversion is operating at a scale finer than the spatial resolution of the sensor which acquired the imagery and for the detection of land cover modifications. By basing the change detection on the comparison of fuzzy classifications it should be possible to identify partial changes, including both land cover conversion and modification. Fuzzy classifications of Advanced Very High Resolution Radiometer (AVHRR) data were used to identify changes in the apparent position of the forest-savanna transition in West Africa. A comparison of the classifications revealed the variations in the nature and magnitude of land cover change. It was apparent that the migration ...

Adaptive detection and localization of moving objects in image sequences

Abstract: In this paper we address two important problems in motion analysis: the detection of moving objects and their localization. A statistical approach is adopted in order to formulate these problems. For the first, the inter-frame difference is modelized by a mixture of two zero-mean generalized Gaussian distributions, and a Gibbs random field is used for describing the label set. A new method to determine the regularization parameter is proposed, based on a voting technique. This method is also modelized using a statistical framework. The solution of the second problem is based on the observation of only two successive frames. Using the results of change detection an adaptive statistical model for the couple of image intensities is identified. For each problem two different multiscale algorithms are evaluated, and the labeling problem is solved using either iterated conditional modes (ICM) or highest confidence first (HCF) algorithms. For illustrating the efficiency of the proposed approach, experimental results are presented using synthetic and real video sequences.

Robust gradual scene change detection

Abstract: Content-based temporal sampling of video is an efficient method for representing the visual information contained in the video sequence by using only a small subset of the video frames, which can be detected by a scene change detection method. Scene changes include not only the abrupt transitions, but also gradual transitions such as fade, dissolve. Robust detection of gradual transitions has been considered to be difficult. A high-performance gradual scene change detection method is presented in this paper. The proposed method is based on the video edit model. It can be derived that, for dissolve transition without violent motions, the variance, gradient magnitude and double chromatic difference (DCD) of image sequence during dissolve will show parabolic shapes. These features can be used for robust detection of dissolve transition. We approve that in term of recall rate the variance and gradient magnitude have the same efficiency. So, we simply use variance sequence to select possible positions of dissolve transitions and then DCD is used for further confirmation.

Land-cover change detection enhanced with generalized linear models

Abstract: This paper explores the use of generalized linear models (GLMs) for enhancing standard methods of satellite-based land-cover change detection. It starts by generalizing satellite-based change-detection algorithms in a modelling context and then gives an overview of GLMs. It goes onto describe how GLMs can fit into the context of existing change-detection methods. By way of example, using a change detection over two locations in North Carolina, USA, using Landsat Thematic Mapper data, it shows how the models provide a quantitative approach to image-based change detection. The application of GLMs requires special consideration of the spatial correlation of geographical data and how this effects the use of GLMs. The paper describes the use of preliminary variogram analysis on the image data for initial sampling considerations. For the binary response (change/no-change) derived from the reference data, a 'joint-count' test is used to assess their independence. Finally, the model error term is checked through ...

A two-stage neural network approach for process variance change detection and classification

Abstract: Statistical process control charts (SPCC) have become one of the most commonly used tools for monitoring process variability in today's manufacturing environment. Meanwhile, neural networks have been gradually recommended as alternatives to SPCC due to their superior performances, especially in the case of monitoring process mean and unnatural patterns. Little attention has been given to the use of neural networks for monitoring the process variance. This paper describes a neural network approach to monitor process variance changes and to predict change-magnitudes. The performances of the proposed neural network monitoring scheme are compared to those of SPCC for a sample size of five and for individual observations. Simulation results show that the performance of the proposed method is comparable to that of SPCC in terms of average run lengths. In addition, the proposed neural network scheme has the capability to estimate the magnitude of the variance change by combining with a bootstrap resampling schem...

Integrated method for scene change detection

Abstract: A system and method for detecting scene changes in a sequence of video frames utilizing a combination of a plurality of difference metrics including an interframe difference metric, a histogram difference metric and an interframe variance difference metric, as well as adaptive threshold level selection methods to dynamically select appropriate threshold levels for each of the difference metrics. The interframe and histogram difference metrics are used to identify abrupt scene changes and the interframe variance difference metric is used to identify gradual scene changes. The identified gradual and abrupt scene changes are validated by applying a plurality of conditions.

A Kalman filtering approach to multispectral image classification and detection of changes in signature abundance

Abstract: Subpixel detection and classification are important in identification and quantification of multicomponent mixtures in remotely sensed data, such as multispectral/hyperspectral images. A recently proposed orthogonal subspace projection (OSP) has shown some success in Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. However, like most techniques, OSP has its own constraints. One inherent limitation is that the number of signatures to be classified cannot be greater than that of spectral bands. Owing to this limitation, OSP may not perform well for multispectral imagery as it does for hyperspectral imagery. This phenomenon is observed by three-band Satellite Pour l'Observation de la Terra (SPOT) data because of an insufficient number of spectral bands compared to the number of materials to be classified. Further, most approaches proposed for multispectral and hyperspectral image analysis, including OSP, operate on a pixel by pixel basis. In this case, a general assumption is made on the fact that the image data are stationary and pixel independent. Unfortunately, this may be true for laboratory data, but not for real data, due to varying atmospheric and scattering effects. In this paper, a Kalman filtering approach is presented that overcomes the aforementioned problems. In addition to the observation process described by a linear mixture model, a Kalman filter utilizes an abundance state equation to model the nonstationary nature in signature abundance. As a result, the signature abundance can be estimated and updated recursively by the Kalman filter and an abrupt change in signature abundance can be detected via the abundance state equation.

Methods of scene change detection and fade detection for indexing of video sequences

Abstract: This invention relates to methods of abrupt scene change detection and fade detection for indexing of MPEG-2 and MPEG-4 compressed video sequences. Abrupt scene change and fade-detection techniques applied to signals in compressed form have reasonable accuracy and the advantage of high simplicity since they are based on entropy decoding and do not require computationally expensive inverse Discrete Cosine Transformation (DCT).

Adaptive information filtering: detecting changes in text streams

Abstract: The task of information filtering is to classify documents from a stream as either relevant or non-relevant according to a particular user interest with the objective to reduce information load. When using an information filter in an environment that is changing with time, methods for adapting the filter should be considered in order to retain classification accuracy. We favor a methodology that attempts to detect changes and adapts the information filter only if inevitable in order to minimize the amount of user feedback for providing new training data. Yet, detecting changes may require costly user feedback as well. This paper describes two methods for detecting changes without user feedback. The first method is based on evaluating an expected error rate, while the second one observes the fraction of classification decisions made with a confidence below a given threshold. Further, a heuristics for automatically determining this threshold is suggested and the performance of this approach is experimentally explored as a function of the threshold parameter. Some empirical results show that both methods work well in a simulated change scenario with real world data.

Detection of concealed ground targets in CARABAS SAR images using change detection

Abstract: The paper describes a new method to detect man-made objects hidden under foliage or camouflage. The method is based on change detection and thus multiple revisits of the same area. It uses SAR image data provided by the low-frequency and ultra-wideband CARABAS SAR system which operate in the 20 - 90 MHz frequency range. Experimental results show a drastic reduction in false-alarm rate compared to methods based on single-pass SAR images. Small- to medium-sized trucks are consistently detected with a false-alarm rate of the order of 0.1 - 1 per km2. This level of false-alarm rate is quite sufficient for most military or civilian applications of interest.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Information processing for remote sensing

Abstract: Pattern recognition SAR image processing and segmentation parameter extraction neural network and fuzzy logic methods change detection knowledge-based method and data fusion image processing algorithms image compression intelligent system discrimination of buried objects.

Image analysis for video surveillance based on spatial regularization of a statistical model-based change detection

Abstract: Advanced video surveillance applications require two successive steps: image analysis and content understanding. The first step analyses and extracts the characteristics of the video sequence. It defines the regions or the objects of interest according to their spatial/temporal properties. This analysis results in a segmentation of the video sequence. This is interpreted by the content understanding step according to the specific scenario and surveillance requirements. This paper addresses the image analysis problem for a video surveillance system. We use a statistical model-based change detection technique that defines the areas of interest in the image. Each area is analyzed separately by integrating spatial and temporal descriptors in a multi-feature clustering algorithm. The selective procedure we propose minimizes the computational load and significantly improves the results provided by the change detection technique. We test this method on both indoor and outdoor surveillance sequences. All the results show a correct segmentation of the scene. Moreover each object defined in the segmentation is described in terms of its spatial and temporal properties. These results can represent a valid input for a later content understanding procedure in several surveillance scenarios.

Wipe scene change detection in video sequences

Abstract: This paper presents a novel algorithm for wipe scene change detection in video sequences. In the proposed scheme, each image in the sequence is mapped to a reduced image. Then we use statistical features and structural properties of the images to identify wipe transition region. Finally, Hough transform is used to analyse the wiping pattern and the direction of wiping. Results show that the algorithm is capable of detecting all wipe regions accurately even when the video sequence contains other special effects.

Content-based scene change detection and classification technique using background tracking

Abstract: Scene is considered a good unit for indexing and retrieving data from large video databases. In this paper, we present a new content-based approach for detecting and classifying scene changes in video sequences. Our technique can detect and classify not only abrupt changes (i.e., hard cuts) but also gradual changes such as fades and dissolves. We compute background difference between frames, and use background tracking to handle various camera motions. Although our method processes significantly less data, it results in more semantically rich pieces (i.e., scenes). Our experiments on various types of videos indicate that the proposed technique is much less sensitive to the predefined threshold values, and is very effective in reducing the number of false hits. Our approach is particularly suitable for very large video databases because it is both space and time efficient.

Post shot boundary detection technique: flashlight scene determination

Abstract: Shot boundary detection, or scene change detection, is a technique used in the initial phase of video indexing. One of the problems in detection is the discrimination of abrupt scene change from flashlight scenes. Current method of detecting flashlight is based upon object edges, in which performance can be affected by the contents of the scene. To overcome this, we present a novel method that utilises the edge direction, reducing erroneous matching with increasing dilation radius. This will improve the accuracy of similarity testing and reduce the amount of erroneously matched edges by at least four times. Our experiment in discriminating flashlight effects from abrupt scene change frame pairs shows that our technique produces a perfect detection, which cannot be achieved by feature-based detection. Such contribution is important as it improves the indexing of real life video used in the upcoming video database standard MPEG-7.

Statistical Detection of Enterprise NetworkProblems

Abstract: The detection of network fault scenarios was achieved using an appropriate subset of Management Information Base (MIB) variables. Anomalous changes in the behavior of the MIB variables was detected using a sequential Generalized Likelihood Ratio (GLR) test. This information was then temporally correlated using a duration filter to provide node level alarms which correlated with observed network faults and performance problems. The algorithm was implemented on data obtained from two different network nodes. The algorithm was optimized using five of the nine fault data sets, and it proved general enough to detect three of the remaining four faults. Consistent results were obtained from the second node as well. Detection of most faults occurred in advance (at least 5 minutes) of the fault suggesting the possibility of prediction and recovery in the future.

Unsupervised change detection techniques using multispectral satellite images

Abstract: The use of commercial satellite images for the verification of nuclear treaties has been suggested previously in the context of a new protocol extending the rights of the International Atomic Energy Agency (IAEA) to verify the absence of undeclared nuclear activities within its member states. The present paper describes an application of principal component and canonical correlation analysis for change detection of Landsat TM images of the plutonium production complex near Kyshtym, Russia.

Least-squares estimation of multiple abrupt changes contaminated by multiplicative noise using MCMC

Abstract: This paper addresses the problem of change point detection in signals corrupted by multiplicative noise. Multiplicative noise has been observed in many signal processing applications. These applications include image processing (speckle) or communication systems (fading channels). This paper focuses on edge detection in synthetic aperture radar (SAR) images contaminated by multiplicative speckle noise. However, the proposed approach could also be used for the segmentation of any multiplicative noise corrupted signals or images. When the signal/noise statistics are known, the change point detection problem can be formulated in a Bayesian framework. However, this approach may be intractable in SAR image processing because of the non-Gaussian multiplicative colored noise. The change point can then be estimated using the simple least-squares (LS) algorithm. The main contributions of this paper are to study the Bayesian and LS detectors for edge detection in speckled SAR images.