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The problem of the change point detection in a sequence of random variables is considered. The task arises in control of technological processes, particularly, in oil and gas production management. Some equipment parameters are to be controlled in order to detect a change of the equipment characteristics and, consequently, a breakdown of its technological regime. As a rule, the data observed are stochastic with the unknown distribution. In the paper a non-parametric method for detection a change in the mean or in the variance of data is developed and some modifications are proposed. All the algorithms do not use the information concerning the distribution function of observations before and after the change point. The algorithms are applied to detect change points of the characteristics of a gas well.

The rapid mainstream adoption of cloud computing and the corresponding spike in the energy usage of big data systems make the efficient management of cloud computing resources a more pressing issue than ever before. To this end, numerous online algorithms such as Receding Horizon Control and Online Balanced Descent have been designed. However it is difficult for cloud service providers to select the best control algorithm dynamically for resource provisioning when confronted with consumer resource demands that are notoriously unpredictable and volatile. Furthermore, it highly possible that it might not be the case for any one algorithm to consistently perform well over the months-long contract period. In this paper, we first exemplify the need to address non-stationarity in cloud computing by showcasing traces from MS Azure. We then develop a novel meta-algorithm that combines change point detection and online optimization. The new algorithm is shown to outperform existing solutions in real-world trace-driven simulations.

The difficulty of very high resolution image change detection lies in the low interclass separability between the changed class and the unchanged class. According to experiments, we found that this separability can be improved by mining the relationship contained in the training samples. Based on this observation, a supervised change detection approach is proposed in this paper based on relationship learning. The proposed approach begins with enriching the training samples based on their neighborhood relationship and label coherence; this relationship is then learned simultaneously with the classifier, and, finally, the latter classification performance benefits from the learned relationship. Experiments demonstrate the effectiveness of the proposed approach.

Highlights • Perception relies on sensitivity to predictable structure in the environment. • We used artificial acoustic scenes to investigate this in the auditory modality. • Listeners track the temporal structure of multiple concurrent acoustic streams. • Sensitivity to predictable structure supports auditory scene analysis, even when scenes are complex. • Benefit of regularity observed even when listeners are unaware of the predictable structure., The notion that sensitivity to the statistical structure of the environment is pivotal to perception has recently garnered considerable attention. Here we investigated this issue in the context of hearing. Building on previous work (Sohoglu and Chait, 2016a; elife), stimuli were artificial ‘soundscapes’ populated by multiple (up to 14) simultaneous streams (‘auditory objects’) comprised of tone-pip sequences, each with a distinct frequency and pattern of amplitude modulation. Sequences were either temporally regular or random. We show that listeners’ ability to detect abrupt appearance or disappearance of a stream is facilitated when scene streams were characterized by a temporally regular fluctuation pattern. The regularity of the changing stream as well as that of the background (non-changing) streams contribute independently to this effect. Remarkably, listeners benefit from regularity even when they are not consciously aware of it. These findings establish that perception of complex acoustic scenes relies on the availability of detailed representations of the regularities automatically extracted from multiple concurrent streams.

Eco-toxicity testing assesses the potential harmfulness of unknown toxic substances as well as other effects of those substances, such as synergistic, additive, and antagonistic effects using various organisms including water flea, fish, benthic amphipod, and microalgae. Microalgae are important components in the food chains of marine ecosystems. Conventionally, the growth rate of an individual microalgae population is analyzed for 72 h to identify the effects of external stresses, e.g., chemicals, pH, temperature, CO2, etc. However, this method requires counting the number of microalgae every 24 h using a microscope, which is not only labor intensive but also requires substantial expertise. Dunaliella tertiolecta which rapidly changes its morphology when exposed to toxic substances, has been a species of interest among biomass and eco-toxicology researchers. However, such small changes are difficult to visualize and interpret with conventional microscopy or physicochemical techniques. Therefore, we propose a new method which utilizes microalgal morphological changes via lens-free shadow imaging technology (LSIT). To this end, a field-portable cell analyzer, NaviCell, which integrates LSIT was developed. Within this platform, the morphological changes of hundreds of microalgae can be automatically analyzed in parallel within just 3–4 min with over 96% precision and accuracy enabling rapid eco-toxicity evaluation.

PubMedID: 26739011 This paper aimed to analyze short-term changes in landscape pattern that primarily results from building development in the east coast of Mersin Province (Turkey). Three sites were selected. Ikonos (2003) and Quickbird (2009) images for these sites were classified, and land cover transformations were quantitatively analyzed using cross-tabulation of classification results. Changes in landscape structure were assessed by comparing the calculated values of area/edge and shape metrics for the earlier and later dates. Area/edge metrics included percentage of land and edge density, while shape metrics included perimeter-area ratio, fractal dimension, and related circumscribing circle (RCC) metrics. Orchards and buildings were dominating land cover classes. Variations in patch edge, size, and shapes were also analyzed and discussed. Degradation of prime agricultural areas due to building development and implications of such development on habitat fragmentation were highlighted. © 2016, Springer International Publishing Switzerland.

After being trained to find a previous missing object within an array of four different objects, rats received occasional probe trials with such test arrays rotated from that of their respective three-object study arrays. Only animals exposed to each object’s non-spatial features consistently paired with both its spatial features (feeder’s relative orientation and direction) in the first experiment or with only feeder’s relative orientation in the second experiment (Fixed Configuration groups) were adversely affected by probe trial test array rotations. This effect, however, was less persistent for this group in the second experiment but re-emerged when objects’ non-spatial features were later rendered uninformative. Animals that had both types of each object’s features randomly paired over trials but not between a trial’s study and test array (Varied Configuration groups) were not adversely affected on probe trials but improved their missing-object recognition in the first experiment. These findings suggest that the Fixed Configuration groups had integrated each object’s non-spatial with both (in Experiment 1) or one (in Experiment 2) of its spatial features to construct a single representation that they could not easily compare to any object in a rotated probe test array. The Varied Configuration groups must maintain separate representations of each object’s features to solve this task. This prevented them from exhibiting such adverse effects on rotated probe trial test arrays but enhanced the rats’ missing-object recognition in the first experiment. We discussed how rats’ flexible use (retrieval) of encoded information from their visuospatial working memory corresponds to that of humans’ visuospatial memory in object change detection and complex object recognition tasks. We also discussed how foraging-specific factors may have influenced each group’s performance in this task. Electronic supplementary material The online version of this article (doi:10.1007/s10071-015-0915-8) contains supplementary material, which is available to authorized users.

Background/Purpose: The objective of this literature review is to explore different land use and land cover methods using machine learning techniques and also their applications in change detection. Reviewing various methods adopted in this domain opens up a new path for taking up further research by extending the current approaches. Design/Methodology/Approach: The research findings presented in various scholarly articles are collected from secondary resources including scholarly journal publications. These articles are analyzed, and the interpretations are highlighted in this review paper. Findings/Result: This research provides insight into various techniques used to classify remote sensing imagery. The gaps identified during the analysis with different approaches have helped to get a clear picture when formulating research questions in the remote sensing geographic information systems domain. Research limitations/implications: This study has surveyed various applications of remote sensing in GIS. This study is limited to a review of the various machine-learning approaches used for implementing change detection. The various deep learning architectures for image classification could be further explored. Originality/Value: The articles selected for review in this study are from scholarly research journals and are cited by other authors in their publications. The papers selected for review are relevant to the research work and research proposal presented in this paper. Paper Type: Literature review paper.

Surface coal mining causes vegetation disturbance while providing an energy source. Thus, much attention is given to monitoring the vegetation of surface coal mining regions. Multitemporal satellite imagery, such as Landsat time-series imagery, is an operational environment monitoring service widely used to access vegetation traits and ensure vegetation surveillance across large areas. However, most of the previous studies have been conducted with change detection models or threshold-based methods that require multiple parameter settings or sample training. In this paper, we tried to analyze the change traits of vegetation in surface coal mining regions using shape-based clustering based on Normalized Difference Vegetation Index (NDVI) time series without multiple parameter settings and sample training. The shape-based clustering used in this paper applied shape-based distance (SBD) to obtain the distance between time series and used Dynamic Time Warping Barycenter Averaging (DBA) to generate cluster centroids. We applied the method to a stack of 19 NDVI images from 2000 to 2018 for a surface coal mining region located in North China. The results showed that the shape-based clustering used in this paper was appropriate for monitoring vegetation change in the region and achieved 79.0% overall accuracy in detecting disturbance-recovery trajectory types.

The study of human behaviors in cognitive sciences provides clues to understand and describe people’s personal and interpersonal functioning. In particular, the temporal analysis of behavioral dynamics can be a powerful tool to reveal events, correlations and causalities but also to discover abnormal behaviors. However, the annotation of these dynamics can be expensive in terms of temporal and human resources. To tackle this challenge, this paper proposes a methodology to semi-automatically annotate behavioral data. Behavioral dynamics can be expressed as sequences of simple dynamical processes: transitions between such processes are generally known as change-points. This paper describes the necessary steps to detect and classify change-points in behavioral data by using a dataset collected in a real use-case scenario. This dataset includes motor observations from children with typical development and with neuro-developmental disorders. Abnormal movements which are present in such disorders are useful to validate the system in conditions that are challenging even for experienced annotators. Results show that the system: can be effective in the semi-automated annotation task; can be efficient in presence of abnormal behaviors; may achieve good performance when trained with limited manually annotated data.

The high-intensity exploitation of mineral resources in mining cities in China has created new environmental challenges and serious environmental situations in recent years. The land use patterns in mining areas change rapidly for natural and anthropogenic reasons. Land-use/land-cover (LU/LC) change is extremely important in the sustainable development of mining cities. Large-scale opencast coal mining results in the destruction of built-up land, industrial land and mining land as well as arable land resources. Therefore, the analysis of changes in mineral resources and arable land use has attracted increasing attention. With the development of remote sensing (RS) and deep learning (DL) technology, many forms of data for detecting land-use changes are available. The goal of this paper is to promote coordinated land-use development. In this study, an ensemble feature pyramid convolutional neural network model (EFPCNNM) approach, which combines the theory of convolutional neural network (CNN), feature pyramid (FP) and ensemble learning (EL), was used to analyze land-use change in the Longmen Mountain opencast coal mine area in Anqing City, Huaining County, Anhui Province, China. Unlike the CNN, and ensemble convolutional neural network model (ECNNM), which only used the high-level features to predict the ground objects, and the feature information of other layers was not fully considered, EFPCNNM enhanced the small target information in high-level features and improved the detection performance of significant ground object. EFPCNNM was highly portable and could be embedded in many models to further boost performance. Comparative experiments on support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), CNN and ECNNM methods demonstrated that the EFPCNNM could exceed the other models in terms of detection accuracy and inference speed, e.g., the overall accuracy was 93.5036%, kappa coefficient was 0.9423, and time was 2.7569 s. Multitemporal RS images from eight periods starting in 2005 and ending in 2019 were used as the land-use data. With a typical classification system and RS quantitative analysis, this paper also evaluated the temporal and spatial changes in arable land use in the mining area. Of all the land-use types, the area of arable land decreased the most over the study period, and the area of mining land and construction land increased the most. In addition, changes in the seepage area associated with collapse sites in the study area were analyzed. The results of this change detection process could provide decision support for the coordinated development of land use and mineral resources in mining areas.

During vehicle braking, when vehicles move on the road with unknown road roughness elevation and unknown tire/road friction coefficient, fewer sensors shall be used for vehicle braking closed-loop control and braking distance prediction to obtain the dynamic states of the vehicle suspension and tire systems. In this paper, a vehicle dynamic model is established in Carsim software. Modify lump LuGre friction model and road roughness elevation model of four tires are proposed based on matlab. When vehicles brake on the road with time-varying split-μ, a braking control algorithm established in this paper. The road roughness elevation and the braking force of each tire are supplied to the vehicle dynamic model in Carsim. A state estimate algorithm of suspension system is proposed. The scheme for minimum sensor of this estimator is determined. A state estimate algorithm of the tire/road friction using only tire angular velocity information is proposed. When vehicles brake on the road with different levels of roughness, the influence of the number of installation groups of the sensors, the tire vertical stiffness deviations, and the measurement noise on the estimation error of the estimator is analyzed. When the vehicle is driving on the road with unknown adhesive ability, based on the estimator of tire/road friction using only tire angular velocity information, the tire/road friction internal state, the changes of road adhesive ability, and the vehicle velocity are estimated well.

Successful geotechnical projects occur when the design is based on a thorough understanding of the geologic and environmental systems and the interaction of these systems over time. The ability to examine and track movement through space and time has been an essential part of the geoprofessional’s toolkit since the onset of the practice. Since the early 2000s, high-resolution three-dimensional (3D) topographic data have begun to transform how we map and understand movement through time across spatially extensive regions at unprecedented levels of accuracy and confidence. This paper examines how high-resolution 3D topographical data, four-dimensional (4D) analysis, and visualization of data in 3D environments can improve our ability to better understand changes in the morphology and material behaviour through time, leading to better decisions and better outcomes. Evolution of advancements made over the past 20 years is presented through case studies where positive impacts were realized through the adoption of 3D remote sensing and 4D analysis, and cases where data could be used in the future to improve outcomes. The paper presents current research being done to further improve processing techniques and exploit new data collection and computational processing capabilities, pushing the capability of time-dependant 4D geotechnical monitoring to new limits.

Change detection in remote sensing images aims to accurately determine any significant land surface changes based on acquired multi-temporal image data, being a pivotal task of remote sensing image processing. Over the past few years, owing to its powerful learning and expression ability, deep learning has been widely applied in the general field of image processing and has demonstrated remarkable potentials in performing change detection in images. However, a majority of the existing deep learning-based change detection mechanisms are modified from single-image semantic segmentation algorithms, without considering the temporal information contained within the images, thereby not always appropriate for real-world change detection. This paper proposes a High-Resolution Triplet Network (HRTNet) framework, including a dynamic inception module, to tackle such shortcomings in change detection. First, a novel triplet input network is introduced, which is capable of learning bi-temporal image features, extracting the temporal information reflecting the difference between images over time. Then, a network is employed to extract high-resolution image features, ensuring the learned features preserving high-resolution characteristics with minimal reduction of information. The paper also proposes a novel dynamic inception module, which helps improve the feature expression ability of HRTNet, enriching the multi-scale information of the features extracted. Finally, the distances between feature pairs are measured to generate a high-precision change map. The effectiveness and robustness of HRTNet are verified on three popular high-resolution remote sensing image datasets. Systematic experimental results show that the proposed approach outperforms state-of-the-art change detection methods.

This paper presents a real-time object-based 3D change detection method that is built around the concept of semantic object maps. The algorithm is able to maintain an object-oriented metric-semantic map of the environment and can detect object-level changes between consecutive patrol routes. The proposed 3D change detection method exploits the capabilities of the novel ZED 2 stereo camera, which integrates stereo vision and artificial intelligence (AI) to enable the development of spatial AI applications. To design the change detection algorithm and set its parameters, an extensive evaluation of the ZED 2 camera was carried out with respect to depth accuracy and consistency, visual tracking and relocalization accuracy and object detection performance. The outcomes of these findings are reported in the paper. Moreover, the utility of the proposed object-based 3D change detection is shown in real-world indoor and outdoor experiments.

Repeat measurements are often used to monitor geomorphological changes on different scales. Such measurements can produce interval digital elevation models (DEMs) which differenced generate distributed maps of elevation changes. One of the most popular tools for creating these models is the Geomorphic Change Detection (GCD) tool. In this paper, we aim to highlight its applicability in studying tufa formation dynamics (TFD) using tufa digital elevation models (DEMs) of submillimeter resolution. In a two-year period, TFD was monitored on seven limestone plates installed in the tufa- forming watercourses near Skradinski Buk waterfall (National park Krka, Croatia). The total error of initial, first-year, and second- year tufa DEMs was calculated from checkpoints. Results have shown that the biggest advantage of the GCD tool is manifested in the situation of low tufa growth when the combination of added uniform error values and confidence interval reduces the percentage of the area with detected elevation changes. Also, no universal pattern of tufa growth in relation to the measurement period was found. This paper has introduced a new possibility for analyzing TFD using tufa DEMs of submillimeter resolution.

Multiplicative Autoregressive Random Field (MAR) based texture models have been identified as one of the most appropriate models for SAR intensity images to capture the stochastic spatial interaction among neighboring pixels. But very few studies have tested their viability particularly in disaster applications. In this paper, we analyse the MAR texture models for their advantageous in land cover change detection compared to the changes resulting from logarithm of SAR image intensity and speckle filtered SAR imagery. The paper shows that lognormal random fields with multiplicative spatial interactions in the form of MAR models can be an effective alternative to suppress speckle noise and model SAR image intensity in time series data analysis. The pre and post disaster observational data of the Tohoku earthquake, in the east coast of Japan, acquired by the Advanced Land Observation Satellite (ALOS)/phased array type L-band synthetic aperture radar (PALSAR) were synthesized using MAR model based texture measures. Two of the main texture descriptors of the MAR model were considered primarily in this study. Those are the neighborhood weighting and the noise variance parameters. A 2nd order neighborhood configuration was used to estimate them. We present a variogram based analysis, structural similarity index measure (SSIM), and the mean ratio detector (MRD) as three different approaches to analyse the changes in land cover using radar texture. The change detection results of the MRD were further tested using area error proportion (AEP), root mean square error (RMSE) and correlation coefficient (CC), keeping normalized ratio, principle component analysis (PCA) and adaptive Lee filtered polarimetric intensity based change as the references.

A novel and effective change detection method based on manifold regularized joint non-negative matrix factorization (MJNMF) framework is proposed in this paper, which detects the changes that occurred in multi-temporal remote sensing images. Most change detection methods, including dictionary learning, principal component analysis (PCA), etc., do not consider the non-negativity among image pixels. However, image itself is a non-negative signal, and the non-negative constraint has better interpretability in practical applications. Nonnegative Matrix Factorization, which incorporates the non-negativity constraint and thus learns object parts, obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly. In this paper, our proposed approach based on MJNMF framework aims to establish a pair of joint basis matrices by unchanged training samples from unchanged area. Then, unchanged pixels can be well reconstructed by the corresponding basis matrix, while changed pixels cannot be reconstructed from the basis matrix corresponding to the knowledge of unchanged samples, or a larger reconstruction error can be generated even if changed pixels are reconfigurable. In order to suppress similar information and highlight different information, the cross-reconstruction error is used to generate the difference image. Finally, the binary image is obtained by the robust fuzzy local information c-means (FLICM) clustering algorithm. In addition, inspired by manifold learning, we incorporate manifold regularization into the proposed method to keep the geometric structure of data and improve the accuracy of change detection. Experimental results obtained on simulated and real remote sensing images confirm the effectiveness of the proposed method.

With the rapid development of remote sensing technologies, the frequency of observations of the same location is increasing, and many satellites and sensors produced a large amount of time series images. These images make long-term change detection and dynamic characteristic estimation of ground features possible. However, conventional remote sensing image change detection methods mostly rely on manual visual interpretation and supervised or unsupervised computer-aided classification. Traditional methods always face many bottlenecks when processing big and fast-growing datasets, such as low computational efficiency, low level of automation, and different identification standards and accuracies caused by different operators. With the rapid accumulation of remote sensing data, it has become an important but more challenging task to conduct change detection in a more precise, automated and standardized way. The development of geointelligent computing technologies provides a means of solving these problems and improve the accuracy and efficiency of remote sensing image change detection. In this paper, we presented a novel deep learning model called nest network(NestNet) based on a convolutional neural network to improve the accuracy of the automatic change detection task by using remotely sensed time series images. NestNet extracts the respective features of bi-temporal images using an encoding parallel module and subsequently employs absolute different operations to process the features of two images. Compared with change detection method based on U-Shaped network plus plus (UNet++), the parallel module improves the efficiency of NestNet. Finally, a decoding module is used to generate a predicted change image. This paper compares NestNet to traditional methods and state-of-the-art deep learning models on two datasets. The experimental results demonstrate that the accuracy of NestNet is better than that of state-of-the-art methods. It can be concluded that the NestNet model is a potential approach for change detection using high resolution remote sensing images.

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. The main task of this research paper is the collection of most relevant scientific research in the field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in the present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in the end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low-resolution Sentinel-1A/B radar remote sensing data are recommended as free available for monitoring of destruction on a degree microregion level. Change detection and texture-based methods are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found better for the separation of collapsed and intact buildings. Dual polarization (VV, VH) backscattering coefficients and coherence coefficients (before the earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from the 2016 Central Italy Earthquake.

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

Effective shared autonomy requires a clear understanding of driver's behavior, which is governed by multiple psychophysiological and environmental variables. Disentangling this intricate web of interactions requires understanding the driver's state and behaviors in different real-world scenarios, longitudinally. Naturalistic Driving Studies (NDS) have shown to be an effective approach to understanding the driver's state and behavior in real-world scenarios. However, due to the lack of technological and computing capabilities, former NDS only focused on vision-based approaches, ignoring important psychophysiological factors such as cognition and emotion. The main objective of this paper is to introduce HARMONY, a human-centered multimodal naturalistic driving study, where driver's behaviors and states are monitored through (1) in-cabin and outside video streams (2) physiological signals including driver's heart rate and hand acceleration (IMU data), (3) ambient noise, light, and the vehicle's GPS location, and (4) music logs, including song features such as tempo. HARMONY is the first study that collects long-term naturalistic facial, physiological, and environmental data simultaneously. This paper summarizes HARMONY's goals, framework design, data collection and analysis, and the on-going and future research efforts. Through a presented case study, we first demonstrate the importance of longitudinal driver state sensing through using Kernel Density Estimation Methods. Second, we leverage the application of Bayesian Change Point detection methods to demonstrate how we can identify driver behaviors and responses to the environmental conditions by fusing psychophysiological information with features extracted from video streams.

The differences between the imaging environments of sensors lead to great differences in remote sensing images of the same area in different seasons. Relative radiation correction has high practical value as the main method to reduce such differences. However, the differences in vegetation radiation caused by seasonal changes are difficult to correct by traditional radiation correction methods. The corrected results also have difficulty achieving better results at the level of human eye visual perception. Moreover, the traditional measurement of the relative radiation correction result image quality index is not consistent with the human eye visual perception effect. To address the above two problems, this paper performs seasonal relative radiation correction on high-resolution remote sensing images by CycleGAN based on a convolutional neural network, including two transformations: 1) the transformation of remote sensing images from autumn-winter to spring-summer and 2) the transformation of remote sensing images from spring-summer to autumn-winter. The similarity between the relative radiation-corrected image and the reference image is measured by the convolutional neural network model with the ability to discriminate distances. The results show that the visual effect of this method is significantly better than that of other relative radiation correction methods, and the visual perception distance is consistent with the human eye visual perception judgment. The changed area still retains its original feature characteristics. The visual perception distance of the conversion from autumn-winter to spring-summer images is improved by 9% compared with other state-of-the-art methods. The visual perception distance of spring-summer images to autumn-winter images is improved by 3%. We expect that the method in this paper can be used for preprocessing to improve the accuracy of algorithms for remote sensing image classification, image change detection, etc.

Satellite imagery is an important information source for research on remote sensing (RS)-based humanitarian applications. The selection of satellite imagery is one of the most important steps for such research. This paper firstly shows the selection of satellite imagery in past research from 2015 to 2021. It can be found that most research on land cover and land use (LCLU) change caused by conflicts or refugees/internally displaced persons (IDPs) chose medium spatial resolution (MSR) imagery. Most research on dwelling detection of refugee/IDP camps applied high or very high spatial resolution (HSR/VHSR) imagery. There is much research that applied multiple types of satellite imagery for humanitarian applications. Then, the paper presents general characteristics of commonly available optical satellite imagery. Next, with the development of sensors, this paper suggests that data fusion of SPOT-5 and Sentinel-2 may be helpful in LCLU change detection caused by refugees/IDPs or conflicts. Smallsat imagery may be promising for research on humanitarian applications that require a high temporal frequency of imagery.

PurposeVisual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.Design/methodology/approachThe developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.FindingsTaking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.Originality/valueThe originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.

In recent years, the technology of small-scale unmanned aerial vehicles (UAVs) has steadily improved in terms of flight time, automatic control, and image acquisition. This has lead to the development of several applications for low-altitude tasks, such as vehicle tracking, person identification, and object recognition. These applications often require to stitch together several video frames to get a comprehensive view of large areas (mosaicking), or to detect differences between images or mosaics acquired at different times (change detection). However, the datasets used to test mosaicking and change detection algorithms are typically acquired at high-altitudes, thus ignoring the specific challenges of low-altitude scenarios. The purpose of this paper is to fill this gap by providing the UAV mosaicking and change detection dataset. It consists of 50 challenging aerial video sequences acquired at low-altitude in different environments with and without the presence of vehicles, persons, and objects, plus metadata and telemetry. In addition, this paper provides some performance metrics to evaluate both the quality of the obtained mosaics and the correctness of the detected changes. Finally, the results achieved by two baseline algorithms, one for mosaicking and one for detection, are presented. The aim is to provide a shared performance reference that can be used for comparison with future algorithms that will be tested on the dataset.

Accuracy in remote sensing image change detection is an important area of study. A new approach for improving the change detection accuracy of SAR remote sensing images is investigated in this paper. The research in this paper is presented in three parts. First, we proposed a new image enhancement algorithm. We combined the image enhancement algorithm based on the combination of the wavelet domain and spatial domain and the power-law. The contrast of the original image was enhanced by histogram equalization; then, the high- and low-frequency coefficients of the image were processed by wavelet fusion, and the image was sharpened by a nonsharpening mask so that the image could be sharpened from the image. Compared with other correlation enhancement contrast algorithms, we assert that the algorithm retains the high-frequency details of the image while improving its sharpness. Second, we propose a new change detection algorithm. A power rate change detection algorithm is used to improve image brightness, a logarithmic difference map is used to obtain a difference map, and an FLCM clustering algorithm is used to improve the clustering effect by suppressing noise from domain information. To improve detection accuracy, a saliency map is extracted from the difference map, the former of which is then used. The detection template obtained from the graph greatly reduces the range of change detection and effectively reduces the noise of the SAR image. The proposed power rate change detection algorithm is superior to similar algorithms. Third, our proposed enhancement algorithm is applied to our proposed change detection algorithm, which further improves the accuracy of image change detection.

Quickest change detection (QCD) is a fundamental problem in many applications. Given a sequence of measurements that exhibits two different distributions around a certain flipping point, the goal is to detect the change in distribution around the flipping point as quickly as possible. The QCD problem appears in many practical applications, e.g., quality control, power system line outage detection, spectrum reuse, and resource allocation and scheduling. In this paper, we focus on spectrum sensing as our application since it is a critical process for proper functionality of cognitive radio networks. Relying on the cumulative sum (CUSUM), we derive the probability of detection and the probability of false alarm of CUSUM based spectrum sensing. We show the correctness of our derivations using numerical simulations., Comment: This paper is accepted for publication in IEEE Communication Letters Jan 2020

Urban areas are subject to multiple and very different changes, in a two- and three-dimensional sense, mostly as a consequence of human activities, such as urbanization, but also because of catastrophic and sudden events, such as earthquakes, landslides, or floods. This paper aims at designing a procedure able to cope with both types of changes by combining interferometric coherence and backscatter amplitude, and provide a semantically meaningful analysis of the changes detected in both city inner cores and suburban areas. Specifically, this paper focuses on detecting multi-dimensional changes in urban areas using a stack of repeat-pass SAR data sets from Sentinel-1A/B satellites. The proposed procedure jointly exploits amplitude and coherence time series to perform this task. SAR amplitude is used to extract changes about the urban extents, i.e. in 2D, while interferometric coherence is sensitive to the presence of buildings and to their size, i. e. to 3D changes. The proposed algorithm is tested using a time-series of two years of Sentinel-1 data, from May 2016 to October 2018, and in two different Chinese cities, Changsha and Hangzhou, with the aim to understand both the temporal evolution of the urban extents, and the changes within what is constantly classified as “urban” throughout the considered time period.

Purpose Background modelling has played an imperative role in the moving object detection as the progress of foreground extraction during video analysis and surveillance in many real-time applications. It is usually done by background subtraction. This method is uprightly based on a mathematical model with a fixed feature as a static background, where the background image is fixed with the foreground object running over it. Usually, this image is taken as the background model and is compared against every new frame of the input video sequence. In this paper, the authors presented a renewed background modelling method for foreground segmentation. The principal objective of the work is to perform the foreground object detection only in the premeditated region of interest (ROI). The ROI is calculated using the proposed algorithm reducing and raising by half (RRH). In this algorithm, the coordinate of a circle with the frame width as the diameter is considered for traversal to find the pixel difference. The change in the pixel intensity is considered to be the foreground object and the position of it is determined based on the pixel location. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; The proposed system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizes the pixel as the foreground and mines the precise foreground object. The broad experimental results and the evaluation parameters of the proposed approach with the state of art methods were compared against the most recent background subtraction approaches. Moreover, the efficiency of the authors’ method is analyzed in different situations to prove that this method is available for real-time videos as well as videos available in the 2014 challenge change detection data set. Design/methodology/approach In this paper, the authors presented a fresh background modelling method for foreground segmentation. The main objective of the work is to perform the foreground object detection only on the premeditated ROI. The region for foreground extraction is calculated using proposed RRH algorithm. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; most challenging case is that, the slow moving object is updated quickly to detect the foreground region. The anticipated system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizing the pixel as the foreground and mining the precise foreground object. Findings Plum Analytics provide a new conduit for documenting and contextualizing the public impact and reach of research within digitally networked environments. While limitations are notable, the metrics promoted through the platform can be used to build a more comprehensive view of research impact. Originality/value The algorithm used in the work was proposed by the authors and are used for experimental evaluations.

$Anomaly$ $detection$ problems (also called $change$-$point$ $detection$ problems) have been studied in data mining, statistics and computer science over the last several decades in applications such as medical condition monitoring and weather change detection. In recent days, however, anomaly detection problems have become increasing more relevant in the context of $network$ $science$ since useful insights for many complex systems in biology, finance and social science are often obtained by representing them via networks. Notions of local and non-local curvatures of higher-dimensional geometric shapes and topological spaces play a $fundamental$ role in physics and mathematics in characterizing anomalous behaviours of these higher dimensional entities. However, using curvature measures to detect anomalies in networks is not yet very common. To this end, a main goal in this paper to formulate and analyze curvature analysis methods to provide the foundations of systematic approaches to find $critical$ $components$ and $detect$ $anomalies$ in networks. For this purpose, we use two measures of network curvatures which depend on non-trivial global properties, such as distributions of geodesics and higher-order correlations among nodes, of the given network. Based on these measures, we precisely formulate several computational problems related to anomaly detection in static or dynamic networks, and provide non-trivial computational complexity results for these problems. This paper must $not$ be viewed as delivering the final word on appropriateness and suitability of specific curvature measures. Instead, it is our hope that this paper will stimulate and motivate further theoretical or empirical research concerning the exciting interplay between notions of curvatures from network and non-network domains, a $much$ desired goal in our opinion., Comment: Final revised version; to appear in Algorithmica

Texture in synthetic aperture radar (SAR) images is a combination of the intrinsic texture of scene backscattering and the texture due to noncoherent high-frequency multiplicative noise (HMN) interactions that reflect erroneous information and lead to observation misinterpretation. The focus of this paper is the fractal analysis of KOMPSAT-5 SAR images of noncoherent sea-ice textures while being decomposed by discrete wavelet transform (DWT) processing. As a novel approach, fractal analysis relies on SAR sea-ice spatial backscattering data generation and time-frequency domain (TFD) formulations from the perspective of uncorrelated HMN. To the best of our knowledge, this is the first time that the extraction of the resolution profile and raw data for the reference KOMPSAT-5 SAR sea-ice image have been derived, evaluated and compared both qualitatively and quantitatively at each scale of DWT decomposition on the basis of the presence of HMN. This paper also presents a novel detailed modeling of the multiresolution probability distribution function of the HMN and its power spectral density function modeling at each scale of the decomposition. Other quality assessment techniques, such as two K-means clustering algorithms and several visualized verification methods, such as gradient vector field, advection mapping and tensor field mapping, have been implemented in this regard to investigate embedded HMN suppression and its adverse effects on the presence of pixel anomalies. As a result, as the decomposition continues, the HMN at each scale of decomposition is constantly altering from high-frequency uncorrelated anomalies to low-frequency joint spatial information within the observed 2-D data. In other words, excessive multiscale HMN suppression will result in spatial information loss that makes the DWT scale selection quite important for texture classification. The results also show that the importance of HMN suppression in SAR sea-ice images in the form of pixel anomaly decomposition for the purpose of further texture investigation should be in accordance with the spectral behavior of the HMN. The results are helpful for SAR remote sensing image restoration and data preservation when dealing with high-resolution SAR images, such as in time series analysis, sea-ice texture change detection, and polar structural mapping. The proposed approach is implemented on real KOMPSAT-5 SAR satellite sea-ice images, while fractal spatial resolution profile simulations are carried out based on the inversed equalized hybrid domain image formation algorithm.

PurposeThe purpose of this paper is to analyze the characteristics of spatio-temporal dynamics and the evolution of land use change is essential for understanding and assessing the status and transition of ecosystems. Such analysis, when applied to Horqin sandy land, can also provide basic information for appropriate decision-making.Design/methodology/approachBy integrating long time series Landsat imageries and geographic information system (GIS) technology, this paper explored the spatio-temporal dynamics and evolution-induced land use change of the largest sandy land in China from 1983 to 2016. Accurate and consistent land use information and land use change information was first extracted by using the maximum likelihood classifier and the post-classification change detection method, respectively. The spatio-temporal dynamics and evolution were then analyzed using three kinds of index models: the dynamic degree model to analyze the change of regional land resources, the dynamic change transfer matrix and flow direction rate to analyze the change direction, and the barycenter transfer model to analyze the spatial pattern of land use change.FindingsThe results indicated that land use in Horqin sandy land during the study period changed dramatically. Vegetation and sandy land showed fluctuating changes, cropland and construction land steadily increased, water body decreased continuously, and the spatial distribution patterns of land use were generally unbalanced. Vegetation, sandy land and cropland were transferred frequently. The amount of vegetation loss was the largest. Water body loss was 473.6 km2, which accounted for 41.7 per cent of the total water body. The loss amount of construction land was only 1.0 km2. Considerable differences were noted in the rate of gravity center migration among the land use types in different periods, and the overall rate of construction land migration was the smallest. Moreover, the gravity center migration rates of the water body and sandy land were relatively high and were related to the fragile ecological environment of Horqin sandy land.Originality/valueThe results not only confirmed the applicability and effectiveness of the combined method of remote sensing and GIS technology but also revealed notable spatio-temporal dynamics and evolution-induced land use change throughout the different time periods (1983-1990, 1990-2000, 2000-2010, 2010-2014, 2014-2016 and 1983-2016).

This paper presents a survey on dynamic object detection in video sequences. Many methods have been proposed during several years that intent to solve the problems of dynamic background, jittering, illumination changes, camouflage, among other. The survey is intended to provide a recent analysis of these methods to highlight the main characteristics of each approach. The survey is based on published paper from 2013 up to date. 77 methods were found, and they are described and analyzed in this survey. In addition, the survey proposes a complete classification of the methods based on the type of technique used to achieve the detection of dynamic objects. It also evaluates the limitations of each approach and the tendencies regarding the most used techniques, color space, datasets, hardware, programming language and processing time. One of the most popular approaches correspond to advanced statistical models, and artificial neural networks with 28% and 22% respectively. The analysis reports 11% of use of GPUs, most of them found in the neural network approaches. Regarding datasets, there are authors that report the use of three datasets the most popular is Change Detection. The most used software to implement the methods is MATLAB and the RGB color space, the most employed.

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model’s execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach.

Cyber-resilience is an increasing concern in developing autonomous navigation solutions for marine vessels. This paper scrutinizes cyber-resilience properties of marine navigation through a prism with three edges: multiple sensor information fusion, diagnosis of not-normal behaviours, and change detection. It proposes a two-stage estimator for diagnosis and mitigation of sensor signals used for coastal navigation. Developing a Likelihood Field approach, a first stage extracts shoreline features from radar and matches them to the electronic navigation chart. A second stage associates buoy and beacon features from the radar with chart information. Using real data logged at sea tests combined with simulated spoofing, the paper verifies the ability to timely diagnose and isolate an attempt to compromise position measurements. A new approach is suggested for high level processing of received data to evaluate their consistency, that is agnostic to the underlying technology of the individual sensory input. A combined parametric Gaussian modelling and Kernel Density Estimation is suggested and compared with a generalized likelihood ratio change detector that uses sliding windows. The paper shows how deviations from nominal behaviour and isolation of the components is possible when under attack or when defects in sensors occur., Comment: 18 pages, 21 figures

A geophysical method named ground-penetrating radar (GPR) can be applied to geotechnical investigation to determine the underground conditions. In this study, a GPR is used to detect the ballast condition of the railway track. The ballast bed plays an important role in rail track keeping the gauge between sleepers and thereby position of the rails. As the train moves over the track, ballast helps to hold the track in place. Ballast fouling is formed by coal dust and the breakdown of ballast or from soil interference under the rail track. The rail track can be damaged substantially due to the fouling material such as coal, metal piece and large stone. This paper presents the non-destructive testing method, namely ground-penetrating radar (GPR) for identification of different types of materials. To detect the underground substructures, 800 MHz ground-coupled antenna was used. Constructed a model rail track with different fouling material are buried under the subsurface structures. This work is carried out in Civil Engineering laboratory, IISc, Bangalore. This research work presents the detection of underground structures like different metals, big-sized ballast and clean ballast. Find maximum change-point algorithm is proposed to detect the abrupt changes in the signal; by this, it will also detect the size of a target by calculating the start and end trace of the buried object. It is important for the diagnosis of ground substructures. This paper also describes the application of wavelet decomposition to detect the condition of the ballast viz clean or fouled. The wavelet decomposition finds the shape of the object by calculating the higher-order statistical parameters like skewness test and kurtosis test.

Gentrification and urban polarization has become one of the biggest societal issues of modern urbanization. While the apartment residential areas for affluent people are proliferating, poor people who cannot afford those areas are involuntarily forced to move out to other areas. In this paper, we proposed an active learning based residential area segmentation algorithm (AL-RASA) that can distinguish tenement area from the satellite images, which can show an imbalance of urban wealth. This paper used the Mask-RCNN as the backbone segmentation module, due to the high cost of labelling satellite image and the requirement of professional sociological knowledge, this paper used a method combining active learning to label the data efficiently and train the model. Finally, with the change detection model, this algorithm can produce an image of the change of tenement areas during last two decades. To better study residential area segmentation problem, this paper provided a high-resolution satellite images dataset of Seoul (South Korea).

Change detection from synthetic aperture radar images becomes a key technique to detect change area related to some phenomenon as flood and deformation of the earth surface. This paper proposes a transfer learning and Residual Network with 18 layers (ResNet-18) architecture-based method for change detection from two synthetic aperture radar images. Before the application of the proposed technique, batch denoising using convolutional neural network is applied to the two input synthetic aperture radar image for speckle noise reduction. To validate the performance of the proposed method, three known synthetic aperture radar datasets (Ottawa; Mexican and for Taiwan Shimen datasets) are exploited in this paper. The use of these datasets is important because the ground truth is known, and this can be considered as the use of numerical simulation. The detected change image obtained by the proposed method is compared using two image metrics. The first metric is image quality index that measures the similarity ratio between the obtained image and the image of the ground truth, the second metrics is edge preservation index, it measures the performance of the method to preserve edges. Finally, the method is applied to determine the changed area using two Sentinel 1 B synthetic aperture radar images of Eddahbi dam situated in Morocco.

This paper explores machine learning to address a problem of Partially Observable Multi-sensor Sequential Change Detection (POMSCD), where only a subset of sensors can be observed to monitor a target system for change-point detection at each online learning round. In contrast to traditional Multisensor Sequential Change Detection tasks where all the sensors are observable, POMSCD is much more challenging because the learner not only needs to detect on-the-fly whether a change occurs based on partially observed multi-sensor data streams, but also needs to cleverly choose a subset of informative sensors to be observed in the next learning round, in order to maximize the overall sequential change detection performance. In this paper, we present the first online learning study to tackle POMSCD in a systemic and rigorous way. Our approach has twofold novelties: (i) we attempt to detect changepoints from partial observations effectively by exploiting potential correlations between sensors, and (ii) we formulate the sensor subset selection task as a Multi-Armed Bandit (MAB) problem and develop an effective adaptive sampling strategy using MAB algorithms. We offer theoretical analysis for the proposed online learning solution, and further validate its empirical performance via an extensive set of numerical studies together with a case study on real-world data sets.

Hyperspectral (HS) images provide a dense sampling of target spectral signatures. Thus, they can be used in a multitemporal framework to detect and discriminate between different kinds of fine spectral change effectively. However, due to the complexity of the problem and the limited amount of multitemporal images and reference data, only a few works in the literature addressed change detection (CD) in HS images. In this paper, we present a novel method for unsupervised multiple CD in multitemporal HS images based on a discrete representation of the change information. Differently from the state-of-the-art methods, which address the high dimensionality of the data using band reduction or selection techniques, in this paper, we focus our attention on the representation and exploitation of the change information present in each band. After a band-by-band pixel-based subtraction of the multitemporal images, we define the hyperspectral change vectors (HCVs). The change information in the HCVs is then simplified. To this end, the radiometric information of each band is separately analyzed to generate a quantized discrete representation of the HCVs. This discrete representation is explored by considering the hierarchical nature of the changes in HS images. A tree representation is defined and used to discriminate between different kinds of change. The proposed method has been tested on a simulated data set and two real multitemporal data sets acquired by the Hyperion sensor over agricultural areas. Experimental results confirm that the discrete representation of the change information is effective when used for unsupervised CD in multitemporal HS data.

Purpose Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change. Design/methodology/approach In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope. Findings By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds. Practical implications The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope. Originality/value In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

On September 15, 2018, ICESat-2 (Ice, Cloud, and land Elevation satellite) was successfully launched to measure ice sheet and glacier elevation change, sea ice freeboard, and vegetation. This paper describes the computation of surface elevation change rates obtained with SERAC (Surface Elevation Reconstruction And Change detection) from ICESat-2 observations. After summarizing some relevant aspects of ICESat-2 and its sole instrument ATLAS (Advanced Topographic Laser Altimetry System) the paper focuses on how we calculate time series of elevation change rates from ICESat-2’s data product ATL03. Since real ICESat-2 data suitable for generating time series of several time epochs are not yet available, we used simulated data for this study. We will start generating time series from real ICESat-2 data after the conclusion of the ongoing calibration and validation phase and we expect to present real-world examples at the WG III/9 meeting in June, 2019 in Enschede, The Netherlands.

The use of phasor measurement units (PMU) has enabled monitoring the power system dynamics online. There are many PMUs installed, which will give a comprehensive view in real or near real time of the entire system. In general, there are many operating conditions of power systems, which requires different tools to analyze the synchrophasor data provided by PMUs. Changes in power system monitoring must be detected as quickly as possible, so that the system operators can choose a different tool for data analysis. Therefore, in this paper, changes in the working conditions of the power system are detected using a subspace state-space based change-point detection method. The detection algorithm proposed in this paper is sequential detection, which aims at both detection accuracy and detection speed. We have tested our proposed algorithm to detect many various kinds of changes in power system operations and verified that the algorithm is able to detect changes successfully, including very small ones in the system such as the low-amplitude forced oscillations buried in the system's ambient conditions in a real-time manner. The algorithm is tested in a set of real-world data to show its practical usage in large systems with imperfect data.

Change detection and diagnosis are important research directions and activities in the field of system engineering and conditional maintenance of equipments and industrial processes. The paper promotes a new method for change detection and optimal segmentation of vibrating data obtained during operation of rolling element bearings (REB). After a description of the bearing faults and dynamic simulation of REB, the paper makes a review of the change detection and segmentation approaches, that could be used in REB fault detection and diagnosis. A new approach for change detection and optimal segmentation of vibrating signals, aiming to determine the change points in signals generated by the faults, produced during REB operating, is presented; the efficiency of the segmentation method is proven using Monte Carlo simulations for different signal models, including models with changes in the mean, in FIR, and AR model parameters, frequently used in processing vibrating signals. In the final part, the paper analyses some experimental results obtained using this approach and data from the Case Western Reserve University Bearing Data Center.

Change/motion detection is a challenging problem in video analysis and surveillance system. Recently, the state-of-the-art methods using the sample-based background model have demonstrated astonishing results with this problem. However, they are ineffective in the dynamic scenes that contain complex motion patterns. In this paper, we introduce a novel data-driven approach that combines the sample-based background model with a feature extractor obtained by training a triplet network. We construct the network by three identical convolutional neural networks, each of which is called a motion feature network. Our network can automatically learn motion patterns from small image patches and transform input images of any size into feature embeddings for high-level representations. The sample-based background model of each pixel is then employed by using the color information and the extracted feature embeddings. We also propose an approach to generate triplet examples from CDNet 2014 for training our network model from scratch. The offline trained network can be used on the fly without re-training on any video sequence before each execution. Therefore, it is feasible for real-time surveillance systems. In this paper, we show that our method outperforms the other state-of-the-art methods on CDNet 2014 and other benchmarks (BMC and Wallflower).

A gamma probability density function (pdf) is shown to be an alternative to model the distribution of the magnitudes of high-resolution, i.e., wavelength-resolution, synthetic aperture radar (SAR) images. As investigated in this paper, it is more appropriate and more realistic statistical in comparison with, e.g., Rayleigh. A bivariate gamma pdf is considered for developing a statistical hypothesis test for wavelength-resolution incoherent SAR change detection. The practical issues in implementation of statistical hypothesis test, such as assumptions on target magnitudes, estimations for scale and shape parameters, and implementation of modified Bessel function, are addressed. This paper also proposes a simple processing scheme for incoherent change detection to validate the proposed statistical hypothesis test. The proposal was experimented with 24 CARABAS data sets. With an average detection probability of 96%, the false alarm rate is only 0.47 per square kilometer.

In general, changes in the multitemporal synthetic aperture radar (SAR) images are detected by classifying the SAR ratio images into the changed and unchanged classes. However, multitemporal SAR images have either increase or decrease in the backscattering values. Therefore, the changed areas can be further classified into positive and negative changed classes. This paper presents an unsupervised change detection approach for detecting the positive and negative changes based on a multi-objective evolutionary algorithm. In this paper, the widely adopted mean-ratio and log-ratio operators are extended to generate SAR ratio images for distinguishing the positive and negative changes. In order to reduce the corruption of speckle noise present in the multitemporal SAR images, a fuzzy cluster validity index is established to exploit local spatial and gray level information. Then the objective functions are simultaneously optimized by a multi-objective evolutionary algorithm. The experimental results on two simulated data sets and three real SAR data sets confirm the effectiveness of the proposed method.

Data streams are collected from the real time operation of complex machines, plants and other technological systems. The collected information could be further used for different purposes, such as performance evaluation, anomaly detection, change detection or fault diagnosis of the operating systems. The analysis of the data streams is done by a calculation tool for estimating the similarity level between pairs of given chunks of data from the data stream, considered as data clouds. One of these data cloud represents a prerecorded (previously known) abnormal operation of the system, while the other data cloud represents a current (still unknown) behavior of the system. Then the similarity analysis will show how close the two data clouds are. In this paper we propose a novel method for similarity analysis that uses two types of models called Data Cloud Model (DCM) and Window Cloud Model (WCM). The DCM is obtained from the data cloud that represents a previously known operation of the system, while the WCM is obtained from the newly collected data cloud from the data stream, called window data cloud. Both data clouds have an equal length (number of data). The algorithms for creating the DCM and WCM are explained in the paper. The DCM consists of Active Grid cells that represent approximately the data density within the known data cloud. The WCM estimates the data density at the same active grid cells, but based on the data from the new window data cloud. Both densities are represented as two Histograms that are compared to each other in order to calculate the similarity level as a value between 0.0 and 1.0. Another problem discussed in the paper is finding a plausible method for detection of significant changes in the data streams. Here the moving window technique is used for collecting series of subsequent data clouds. Then two procedures of moving windows are run in parallel, each of them with different window lengths, in order to calculate the center-of-gravity of the respective data in a real time. The difference between the results of the two moving windows is used as an estimate of the change in the process operation. Both technologies developed in this paper are explain in details in the paper and illustrated on the example of real data stream from a petrochemical plant.

By exploiting the continuity structure of target scene, the problem of interrupted synthetic aperture radar (SAR) imaging for change detection is studied in this paper. Timeline constraints imposed on multi-function modern radars lead to gapped SAR data collections, which in turn results in corrupted image that degrades reliable coherent change detection (CCD). In this paper we extrapolate the missing data using the sparse Bayesian framework. In particular, the inherent clustered structures of the sparse target scene are characterized by structure-aware Bayesian priors. The variational Bayesian inference (VBI) is then utilized to estimate an approximated posterior of the sparse coefficients. Finally the CCD images are obtained by applying the coherence estimator to the resultant complex images. Based on the structural information in the imaging process, the devised method offers the advantages of preserving the weak scatterers and suppressing the artificial points with fewer measurements. Experimental results are presented to demonstrate the effectiveness and superiority of the proposed algorithm.