Change Detection System Research Articles

Classification and detection of changes in land use and land cover using satellite images and GIS- near Suez Canal area

Egypt has developed significantly in the vicinity of the Suez Canal and its surrounding towns over the last ten years. Utilizing satellite pictures to identify land use and cover types from thematic maps remains difficult. Select the optimal strategy for successful satellite image classification across various applications. Enhancing classification accuracy requires the most effective algorithm. The effectiveness of a change detection system has not been tested in the community of Abu SUWEIR, located near the Suez Canal region in Ismailia City, Egypt. This project analyzes seven classification algorithms with the use of remotely sensed Planet Scope multispectral pictures to evaluate and monitor changes in land cover in ABU SUWEIR Village from 2016 to 2022. The post-January 25, 2011 revolution period in Egypt saw a transition from anarchy to the commencement of progress and improvement. This analysis demonstrates that the ANN classifier achieved an overall accuracy of 77.33% in 2016 and 72.00% in 2022, with corresponding KAPPA coefficients of 0.66 and 0.58 for each year. Agriculture was the most common land use type according to the ANN categorization, making up over 60.48% of the study area in 2016 and 53.89% in 2022. Bare terrain is the second most common landcover type, accounting for 30.31% in 2022 and 25.91% in 2016. On the contrary, urbanized regions, accounting for 13.61% in 2016 and 15.80% in 2022, are the least categorized.

AstrobeeCD: Change detection in microgravity with free-flying robots

This work presents AstrobeeCD, a system for 3D scene change detection toward near-real-time environmental awareness of space outposts using the Astrobee free-flying robot in microgravity. Assistive free-flyer robots autonomously caring for future crewed space habitats must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. A set of image and depth data from one time step is used to reconstruct a 3D model of the environment. The 3D model is used as the basis for comparison for free-flyer environment surveys at future time steps, where an image-based change detection algorithm identifies inconsistencies against the 3D model. Change detection is demonstrated using real image and pose data collected by an Astrobee robot in a test environment on Earth at NASA Ames Research Center and from microgravity aboard the International Space Station. Change detection computation time and performance are quantitatively evaluated on the test data captured on Earth, and it identifies scene changes more quickly than a point cloud clustering-based algorithm applied to data from the same surveys.

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

Research on a near real-time regional change detection system of UAV remote sensing images based on embedded technology

Research on a near real-time regional change detection system of UAV remote sensing images based on embedded technology

Remote sensor measurement system for sub-centimeter structural change detection

This work presents an autonomous remote sensor measurement system to monitor structural change detection. We utilized a portable network analyzer as a core component of a ground-based synthetic aperture radar to achieve structural monitoring. First, we realized a 2-meter rail apparatus, where antennas and radio frequency components of the radar can move, to obtain a synthetic radar aperture. Then, we develop signal processing algorithms to obtain radar images and to perform change detection. Although the range resolution of our system is 15 cm due to the frequency bandwidth of the radar, we have successfully developed an interferometric change detection technique to achieve up to 440μm change detection of the monitored structure.

A quartz crystal microbalance (QCM)-based easy setup device for real-time mass change detection under high-power RF plasma.

Sensing technologies serve a crucial role in monitoring and testing surface properties in biosensors, thin films, and many other industries. Plasma treatments are routinely used in most of these technologies to modify the surfaces of materials. However, due to the high radio frequency (RF) noise in plasma processes, real-time surface tracking is still rather difficult. In this study, we aim to construct an easy-to-set up mass change detection system capable of operating under RF plasma conditions. For this purpose, we have presented a novel technique that utilizes the quartz crystal microbalance sensor to detect mass changes in different plasma environments. The constructed device was then tested under 13.56MHz, 100W plasma atmosphere. The results showed that the resonance frequency of a crystal was successfully measured with 1.0Hz resolution under the impact of plasma-induced high power of RF noise. Moreover, as a preliminary study, we used ethylenediamine (EDA) to track changes in resonance frequency under plasma conditions and observed noise-free signals in frequency-voltage curves. Furthermore, the system's sensitivity was found to be 3.8 ng/Hz, with a test molecule (EDA) deposition of about 380 ng in the RF plasma atmosphere. Overall, this study focused on creating a relatively new approach for detecting the real-time mass change in a strong RF environment, which we believe could be an improved and easy-to-set up technique for plasma-based processes such as surface coating, etching, and activation.

Review of Land Use Change Detection—A Method Combining Machine Learning and Bibliometric Analysis

Land use change detection (LUCD) is a critical technology with applications in various fields, including forest disturbance, cropland changes, and urban expansion. However, the current review articles on LUCD tend to be limited in scope, rendering a comprehensive review challenging due to the vast number of publications. This paper systematically reviewed 3512 articles retrieved from the Web of Science Core database between 1985 and 2022, utilizing a combination of bibliometric analysis and machine learning methods with LUCD as the main focus. The results indicated an exponential increase in the number of LUCD studies, indicating continued growth in this research field. Commonly used methods include classification-based, threshold-based, model-based, and deep learning-based change detection, with research themes encompassing forest logging and vegetation succession, urban landscape dynamics, and biodiversity conservation and management. To build an intelligent change detection system, researchers need to develop a flexible framework that integrates data preprocessing, feature extraction, land use type interpretation, and accuracy evaluation, given the continuous evolution and application of remote sensing data, deep learning, big data, and artificial intelligence.

Monitoring Sparse and Attributed Network Streams with MultiLevel and Dynamic Structures

In this study, we create a new monitoring system for change detection in sparse attributed network streams with multilevel or nested dynamic structures. To achieve this, we hypothesize that the contingency of establishing an edge between two network nodes at time t depends on the properties of the network edges, network nodes, groups, or categories. Then, we estimate the model parameters using the expressed logit model. The model parameters are developed using the state-space model to achieve a dynamic state in the system. The extended Kalman filter (EKF) updates state-space parameters and predicts upcoming networks. Predicted residuals are tracked using statistical process control charts to identify changes in the underlying mechanism of edge generation. This research makes a methodological contribution by combining zero-inflated generalized linear mixed models (ZI-GLMMs) with the state-space model to monitor changes in the sequences of sparse, attributed, and weighted multilevel networks by applying control charts. The proposed model is compared to previous models to evaluate performance by implementing three scenarios. The results show that the model is faster at detecting the first change. Finally, using real e-MID data, we measured the model’s performance in detecting real data changes. The findings suggest that the proposed model could predict a crisis in advance of significant European Central Bank statements and events.

Power grid surveillance: Topology change detection system using power line communications

This paper proposes an efficient channel impulse response (CIR)-based technique to detect topology changes in the power grid. The features of the proposed approach include the following aspects: (i) it is a software-only solution, not requiring any intervention on the current smart grid architecture; (ii) topology changes can be detected via a simple distributed algorithm that requires only local communications; and, (iii) both memory and computational footprints of the proposed solution are minimal. The above-mentioned features make this contribution notably appealing for the resource-constrained smart grid domain. Furthermore, the paper provides a detailed discussion of the introduced technique, accompanied by an implementation reflecting a realistic use case, and presents an extensive simulation campaign to show the quality and viability of the proposed approach. A unique feature of our solution is that it performs well even when the communication channel is affected by a high noise level. For instance, with noise levels below 100 dBμV (the typical PLC noise power range), the proposed solution showed a ≈100% detection rate.To the best of our knowledge, our solution is the first one proposing a fully CIR-based, distributed, deterministic algorithm for intrusion detection and localization. We believe that the performance and advantages of the proposed technique pave the way for further smart grid applications and solutions.

Lane change maneuver detection considering real-time vehicle dynamic features via V2X communication

More than 90% of traffic accidents are caused by driver behavior, with lane change behavior being a major contributor. Recently, driving assistance systems are being introduced on vehicles to reduce traffic accidents, and a reliable vehicle lane change collision detection system is a key component of these systems. Besides, the foundation of the vehicle lane change detection system is the effective vehicle lane change detection model. In this paper, based on the support vector machine, we propose a model for detecting driver lane change maneuvers and take into account the real-time vehicle dynamic features transmitted via Vehicle to X (V2X) Communication. The accuracy is ideal for lane keep and lane change situations, and it is also robust for zigzag driving situations, according to tests conducted using the NGSIM real traffic dataset. The detection accuracy for left and right lane change maneuvers is 97.5% and 99.09%, respectively, while the false alarm rate is 8.56%. Additionally, the average advance detection time is 1.7 s, which is suitable for actual driving application scenarios.

Construction and use of real-time seismic velocity change detection system to monitor the temporal and spatial changes of crustal stress and fault activity in Taiwan

Taiwan is a hotbed of seismic and orogeny activity and this is the basis of the research of Dr Hsin-Hua Huang, Earth Structures Laboratory, Institute of Earth Sciences, Academia Sinica, Taiwan. He has illuminated detailed crustal and slab structures that have furthered understanding of Taiwan orogeny, incorporated ambient noise data to resolve high-resolution imaging of the volcanic and fault structure in northern Taiwan and conducted research that led to the discovery of multi-layering magma beneath the Yellowstone supervolcano in the US. The ultimate goal of his diverse studies is to achieve an improved imaging of the structure of the Earth and establish better understanding of the orogenic tectonic process and assess and mitigate natural hazards, such as volcanoes and earthquakes. In their latest project, Huang and the team are using the seismic wave interferometry method, combined with Taiwan's existing real-time signal transmission Taiwan Broadband Seismic Network (BATS) and the Central Weather Bureau Seismic Network (CWB24), to develop an automated real-time seismicity change detection system. The idea is that by measuring changes in the transmission velocity of seismic waves in the crust, physical parameters such as the degree of rock compression or changes in porosity can be linked and discussed. The researchers have performed an extensive review of knowledge about the current stage of fault zone failure and healing processes,the advantages of seismic noise interferometry and the need for Taiwan fault zone monitoring and related seismic interferometry research. In order to understand the temporal changes in crustal structure and the behaviour of velocity changes across faults, a long-term and stable real-time seismic velocity change monitoring system is necessary.

Deep Learning Approach for Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data

Changing lane is a complex driving maneuver and could have a significant effect on traffic safety. Therefore, developing accurate and timely lane change detection systems could assist drivers to perform and complete this complicated driving task safely. This study proposes reliable lane change detection models based on deep learning (DL) and including features from vehicle kinematics, machine vision, roadway geometries, and driver demographics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. A cutting-edge technique named “DeepInsight” was applied to transform numeric lane change data into image data. The generated image data sets were trained, validated, and tested using a novel DL architecture called ResNet-18, considering six categories of features. To balance the lane change database, two data balancing methods—the synthetic minority oversampling technique (SMOTE) and random majority under sampling (RMUS)—were considered and tested with various sampling ratios. In addition, wrapper-based Boruta and eXtreme gradient boosting (XGBoost) algorithms were used to extract relevant features in each category. A recall of 82% and overall accuracy of 77.9% were found using a ratio of 1:1 for the model based on vehicle kinematic features suggesting that the developed model could be used in the absence of other data. However, the best detection performance was observed using the same ratio for a reduced model based on XGBoost, which produced a recall and overall accuracy of 98.8% and 95%, respectively, considering all the features. The proposed detection system could be effectively leveraged to monitor lane change behavior and provide appropriate control strategies in a connected vehicle (CV) environment.

Event-based imaging polarimeter

The design and initial experimental results from an event-based, rotating-polarizer, imaging polarimeter are presented. The speed of division-of-time imaging systems is traditionally constrained by the frame rate of the focal plane sensor and the need to sample several polarization angles during rotation. The asynchronous event reporting of event-based cameras (EBCs) frees these constraints but introduces challenges. The Stokes vectors used to describe polarization are based on irradiance, but EBCs are inherently change-detection systems that struggle to estimate irradiance. Two methods of estimating the polarization state without first recovering irradiance images are presented and compared. The DAVIS 346 sensor enables simultaneous recording of events with conventional frame-based images, enabling direct comparison of event-based polarization estimates to traditional techniques.

Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests

The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government agencies and local communities to monitor natural and anthropogenic disturbances in a much timelier fashion than the thematic maps that are released every year. NRT deforestation alerts enable the establishment of more up-to-date forest inventories and rapid responses to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution. The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly. Here, we present a Sentinel-2-based NRT change detection system, assess its performance over two study sites, Manantlán in Mexico and Cartagena del Chairá in Colombia, and evaluate the forest changes that occurred in 2018. An independent validation with very high-resolution PlanetScope (~3 m) and RapidEye (~5 m) data suggests that the proposed NRT change detection system can accurately detect forest cover loss (> 87%), other vegetation loss (> 76%) and other vegetation gain (> 71%). Furthermore, the proposed NRT change detection system is designed to be attuned using in situ data. Therefore, it is scalable to larger regions, entire countries and even continents.

Auditory change-related cortical response is associated with hypervigilance to pain in healthy volunteers.

BackgroundPatients with chronic pain exhibit hypervigilance (heightened responsiveness to stimuli) to innocuous auditory stimuli as well as noxious stimuli. “Generalized hypervigilance” suggests that individuals who show heightened responsiveness to one sensory system also show hypervigilance to other modalities. However, research exploring the existence of generalized hypervigilance in healthy subjects is limited.MethodsWe investigated whether hypervigilance to pain is associated with auditory stimuli in healthy subjects using the pain vigilance and awareness questionnaire (PVAQ) and auditory change‐related cortical responses (ACRs). ACRs are thought to reflect a change detection system, based on preceding sensory memory. We recorded ACRs under conditions that varied in terms of the accumulation of sensory memory as follows: short‐ACR, with short preceding continuous stimuli and long‐ACR, with long preceding continuous stimuli. In addition, the attention to pain (PVAQ‐AP) and attention to changes in pain (PVAQ‐ACP) subscales were evaluated.ResultsAmplitudes of long‐ACR showed significant positive correlations with PVAQ‐ACP, whereas those of short‐ACR did not show any significant correlations.ConclusionsGeneralized hypervigilance may be observed even in healthy subjects. ACR may be a useful index to evaluate the hypervigilance state in the human brain.

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps.

Tunnel structural health inspections are predominantly done through periodic visual observations, requiring humans to be physically present on-site, possibly exposing them to hazardous environments. These surveys are subjective (relying on the surveyor experience), time-consuming, and may demand operation shutdown. These issues can be mitigated through accurate automatic monitoring and inspection systems. In this work, we propose a remotely operated machine vision change detection application to improve the structural health monitoring of tunnels. The vision-based sensing system acquires the data from a rig of cameras hosted on a robotic platform that is driven parallel to the tunnel walls. These data are then pre-processed using image processing and deep learning techniques to reduce nuisance changes caused by light variations. Image fusion techniques are then applied to identify the changes occurring in the tunnel structure. Different pixel-based change detection approaches are used to generate temporal change maps. Decision-level fusion methods are then used to combine these change maps to obtain a more reliable detection of the changes that occur between surveys. A quantitative analysis of the results achieved shows that the proposed change detection system achieved a recall value of 81%, a precision value of 93% and an F1-score of 86.7%.

Real-Time Vehicle Trajectory Estimation Based on Lane Change Detection using Smartphone Sensors

As technology is moving rapidly toward automation and connectivity, it is of paramount importance to predict vehicle trajectories ahead of time. This not only enhances safety but also ensures mobility in a connected and automated environment. Previous studies have shown that, given the previous trajectory, the future trajectory can be estimated. But this method suffers from considerable drawbacks in the case of intersections as it cannot predict turning movements. It also requires advanced sensors that are not readily available in most vehicles. A smartphone device can also be used in such scenarios, bringing partial automation to vehicles without these sensors. This paper presents an integrated method of estimating vehicle trajectories for both general roadway segments and intersections by using a smartphone. A lane change detection system is taken as an indicator of intersection turning movement estimation and corresponding vehicle trajectories are estimated accordingly. The system can achieve high penetration rates and can be used to replicate onboard units. Sensor readings are taken periodically which are first filtered with a low-pass filter to zero out any high-frequency noise and then fed into a machine learning model to detect lane changes. The model can successfully capture lane changes with smartphone data with high accuracy (95%). Finally, vehicle trajectory is estimated using Chebyshev’s polynomial. This type of estimation system can find applications in collision prediction at intersections between a turning vehicle and a pedestrian on a crosswalk.

A foveated vision framework for visual change detection using motion and textural features

Change detection is an important process in many video-based applications such as anomaly event detection and video surveillance. This paper proposes a foveated vision framework that simulates the human visual system for change detection. It contains two phases—first identifying regions with visual changes due to significant motion, and then, the extraction of detailed information of the change. In phase I, change proposals (CPs) and background are segregated by analyzing the intensity and motion features. In phase II, visual changes are estimated from the CPs by analyzing the photometric and textural features. Each phase of analysis has a unique pre-generated archetype. A probabilistic refinement scheme is used to rectify the labeling of background and change. In each phase of analysis, the result is used to update the archetype immediately. Some well-known and recently proposed background modeling/subtraction algorithms are selected for our comparative study. Experimentations are performed on various video datasets. In some videos, our method can achieve higher accuracy than some recently proposed methods by 30%. In the large-scale experimentation using all the testing videos, our method can achieve higher average accuracy than the second best method by more than 3%.

A data-driven lane-changing behavior detection system based on sequence learning

ABSTRACT Lane-changing detection is one of the most challenging tasks in advanced driver assistance system (ADAS). However, modeling driver's lane-changing process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, a novel sequential model, data-driven lane change detection (DLCD) system is proposed using deep learning techniques. Firstly, DLCD system explores to modeling driving context in spatial domain instead of traditional temporal domain. Secondly, DLCD has an ability of extracting innovative features, i.e. vehicle dynamics feature, lane boundary based distance feature and visual scene-centric feature from multi-modal input data efficiently. Finally, an improved focal loss-based deep long short-term memory (FL-LSTM) network is introduced to learn co-occurrence features and capture the dependencies within lane change events simultaneously. The experimental results on a real-world driving data set show that the DLCD system can learn the latent features of lane change behaviors and significantly outperform other advanced models.

Research on the technology of terrain classification and change detection based on deep learning

With the rapid development of urbanization in China, the change of land property is very rapid. The detection of land change is helpful to the government’s supervision and the development and protection of land resources. The traditional methods of land feature classification and change detection of remote sensing image generally adopt the methods of manual annotation and recognition. Due to the performance defects, they can not meet the requirements of remote sensing image business application. In this paper, the technology of satellite remote sensing image feature classification and change detection based on deep learning is proposed, and several demonstration cases of automatic classification and change detection system of buildings, roads, water bodies, forest land and other features based on high-resolution remote sensing image are formed. Relevant research results show that the technology can meet the requirements of remote sensing image classification and change detection, and the accuracy of the review is more than 90%.The method adopted can effectively solve the application needs of related fields.