Change Detection Problem Research Articles

Motion Path Study Based on Isometric Solenoids and Discrete Simulation

This paper focuses on the position, speed change and collision detection problems during the traveling process of the bench dragon from both physical and mathematical perspectives. Firstly, based on the equidistance solenoid equation and the mechanical arm model, this paper iteratively recursively starts from the dragon head and sequentially solves the position of each section of the bench in the traveling process. Secondly, this paper combines the velocity analysis of the rigid rod and the nature of isometric solenoid, and also solves the position of each section of the bench in the traveling process sequentially in the form of iterative recursion. Finally, this paper discretizes the disk-in trajectory and carries out collision detection, so as to obtain the time, position, velocity and other information at the termination moment. The study shows that the model can effectively solve the complex optimization problem in the marching process of the dragon dance team.

Efficient state change detection method for modeling stateful time series data

AbstractTime series analysis is an important research area and has wide real applications. Time series modeling has been identified and well known as the key fundamental problem in time series analysis, and it has received lots of research attentions. The hardness of achieving sophisticated and efficient time series models depends on the characteristic of the data met. In practical, time series data are usually generated by the physical process with several working states, which are determined by the system design. In such cases, the data characteristics of each state is quite different from the other states, where the modeling problem becomes much harder and it is more challenging to design modeling methods for stateful time series data. Therefore, this paper focuses on the stateful time series data, and studies the state change detection problem, which is identified as a key fundamental problem for modeling stateful data. To achieve effective and efficient state change detection, the proposed method utilizes two essential ideas. Since the discrepancy (a.k.a. histogram) based method has been proposed and verified to be more effective by previous works, the first idea is to improve its performance by involving more temporal information and utilizing the encoder–decoder techniques to reform the data. In view of the efficiency issue of the discrepancy‐based method, the second idea is to integrate both the Bayesian and discrepancy‐based detection methods, such that a perfect tradeoff between time efficiency and detection performance can be achieved. Experiments on both real and synthetic datasets show that the proposed methods are both effective and efficient, and the state change detection problem can be solved well by them on stateful time series data.

Kernel-Based Multivariate Nonparametric CUSUM Multi-Chart for Detection of Abrupt Changes

In many cases, it is difficult to obtain precise distributional information on multivariate sequences. Therefore, there is a need to propose nonparametric methods for monitoring multivariate sequences. This article discusses the multivariate change detection problem and utilizes the kernel function as the statistic to construct the nonparametric Multivariate Cumulative Sum multi-chart, under the assumption that there is prior information about the abrupt changes. Through theoretical and numerical analysis, we show that the proposed control chart is more effective compared to other existing control charts. The good monitoring effect of this method demonstrates a strong potential for application.

A Siamese Swin-Unet for image change detection

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying).

Clustering-based attack detection for adversarial reinforcement learning

Detecting malicious attacks presents a major challenge in the field of reinforcement learning (RL), as such attacks can force the victim to perform abnormal actions, with potentially severe consequences. To mitigate these risks, current research focuses on the enhancement of RL algorithms with efficient detection mechanisms, especially for real-world applications. Adversarial attacks have the potential to alter the environmental dynamics of a Markov Decision Process (MDP) perceived by an RL agent. Leveraging these changes in dynamics, we propose a novel approach to detect attacks. Our contribution can be summarized in two main aspects. Firstly, we propose a novel formalization of the attack detection problem that entails analyzing modifications made by attacks to the transition and reward dynamics within the environment. This problem can be framed as a context change detection problem, where the goal is to identify the transition from a “free-of-attack” situation to an “under-attack” scenario. To solve this problem, we propose a groundbreaking “model-free” clustering-based countermeasure. This approach consists of two essential steps: first, partitioning the transition space into clusters, and then using this partitioning to identify changes in environmental dynamics caused by adversarial attacks. To assess the efficiency of our detection method, we performed experiments on four established RL domains (grid-world, mountain car, carpole, and acrobot) and subjected them to four advanced attack types. Uniform, Strategically-timed, Q-value, and Multi-objective. Our study proves that our technique has a high potential for perturbation detection, even in scenarios where attackers employ more sophisticated strategies.

COMIC: An unsupervised change detection method for heterogeneous remote sensing images based on copula mixtures and Cycle-Consistent Adversarial Networks

In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction. However, the difference map derived from subtraction is susceptible to image translation errors, in which case the changed area and the unchanged area are less distinguishable. To overcome the above shortcoming, we propose a new unsupervised copula mixture and CycleGAN-based CD method (COMIC), which combines the advantages of copula mixtures on statistical modeling and the advantages of CycleGANs on data mining. In COMIC, the pre-event image is first translated from its original modality to the post-event image modality. After that, by constructing a copula mixture, the joint distribution of the features from the heterogeneous images can be learnt according to quantitive analysis of the dependence structure based on the translated image and the original pre-event image, which are of the same modality and contain totally the same objects. Then, we model the CD problem as a binary hypothesis testing problem and derive its test statistics based on the constructed copula mixture. Finally, the difference map can be obtained from the test statistics and the binary change map (BCM) is generated by K-means clustering. We perform experiments on real RS datasets, which demonstrate the superiority of COMIC over the state-of-the-art methods.

Anomalous Change Detection in Drilling Process Using Variational Autoencoder with Temperature Near Drill Edge

The different flexibility and diversity requirements for respective manufacturing units have made modern cutting tool management much more crucial and complicated, as a greater variety of tools and more frequent tool changes are required to enhance production efficiency and avoid unplanned manufacturing downtime. Developing in-process anomalous change detection methods has been identified as an essential challenge. Machine learning techniques have been widely applied in tool condition monitoring and anomalous change detection. As anomaly data is rare in manufacturing processes, supervised machine learning approaches (such as regression and classification) are not applied to the anomalous change detection problem. Rather, self-supervised machine learning (a representative type of unsupervised machine learning) is applied. This study describes a variational autoencoder (VAE) neural network and proposes a VAE-based method for tool condition monitoring and change detection in a drilling process using the temperature near a drill edge. The proposed VAE evaluates the drill tool condition based on the reconstruction error between the input temperature and its estimate per a drill unit process through the trained network. Computational simulations demonstrate that the proposed VAE network model can avoid overfitting to the anomaly data and that its expressive power is greater than that of the conventional autoencoder model.

SMBCNet: A Transformer-Based Approach for Change Detection in Remote Sensing Images through Semantic Segmentation

Remote sensing change detection (RSCD) is crucial for our understanding of the dynamic pattern of the Earth’s surface and human influence. Recently, transformer-based methodologies have advanced from their powerful global modeling capabilities in RSCD tasks. Nevertheless, they remain under excessive parameterization, which continues to be severely constrained by time and computation resources. Here, we present a transformer-based RSCD model called the Segmentation Multi-Branch Change Detection Network (SMBCNet). Our proposed approach combines a hierarchically structured transformer encoder with a cross-scale enhancement module (CEM) to extract global information with lower complexity. To account for the diverse nature of changes, we introduce a plug-and-play multi-branch change fusion module (MCFM) that integrates temporal features. Within this module, we transform the change detection task into a semantic segmentation problem. Moreover, we identify the Temporal Feature Aggregation Module (TFAM) to facilitate integrating features from diverse spatial scales. These results demonstrate that semantic segmentation is an effective solution to change detection (CD) problems in remote sensing images.

SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection

Change detection (CD) aims to find the difference between two images at different times and output a change map to represent whether the region has changed or not. To achieve a better result in generating the change map, many State-of-The-Art (SoTA) methods design a deep learning model that has a powerful discriminative ability. However, these methods still get lower performance because they ignore spatial information and scaling changes between objects, giving rise to blurry boundaries. In addition to these, they also neglect the interactive information of two different images. To alleviate these problems, we propose our network, the Scale and Relation-Aware Siamese Network (SARAS-Net) to deal with this issue. In this paper, three modules are proposed that include relation-aware, scale-aware, and cross-transformer to tackle the problem of scene change detection more effectively. To verify our model, we tested three public datasets, including LEVIR-CD, WHU-CD, and DSFIN, and obtained SoTA accuracy. Our code is available at https://github.com/f64051041/SARAS-Net.

Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images

Change detection in very-high-spatial-resolution (VHR) remote sensing images is a very challenging area with applicability in many problems ranging from damage assessment to land management and environmental monitoring. In this study, we investigated the change detection problem associated with analysing the vegetation corresponding to crops and natural ecosystems over VHR multispectral and hyperspectral images obtained by sensors onboard drones or satellites. The challenge of applying change detection methods to these images is the similar spectral signatures of the vegetation elements in the image. To solve this issue, a consensus multi-scale binary change detection technique based on the extraction of object-based features was developed. With the objective of capturing changes at different granularity levels taking advantage of the high spatial resolution of the VHR images and, as the segmentation operation is not well defined, we propose to use several detectors based on different segmentation algorithms, each applied at different scales. As the changes in vegetation also present high variability depending on capture conditions such as illumination, the use of the CVA-SAM applied at the segment level instead of at the pixel level is also proposed. The results revealed the effectiveness of the proposed approach for identifying changes over land cover vegetation images with different types of changes and different spatial and spectral resolutions.

Multivariate Lévy-type drift change detection and mortality modeling

In this paper we give a solution to the quickest drift change detection problem for a multivariate Lévy process consisting of both continuous (Gaussian) and jump components in the Bayesian approach. We do it for a general 0-modified continuous prior distribution of the change point as well as for a random post-change drift parameter. Classically, our criterion of optimality is based on a probability of false alarm and an expected delay of the detection, which is then reformulated in terms of a posterior probability of the change point. We find a generator of the posterior probability, which in case of general prior distribution is inhomogeneous in time. The main solving technique uses the optimal stopping theory and is based on solving a certain free-boundary problem. We also construct a Generelized Shiryaev-Roberts statistic, which can be used for applications. The paper is supplemented by two examples, one of which is further used to analyze Polish life tables (after proper calibration) and detect the drift change in the correlated force of mortality of men and women jointly.

CAT: a coarse-to-fine attention tree for semantic change detection

Semantic change detection (SCD) and land cover mapping (LCM) are always treated as a dual task in the field of remote sensing. However, due to diverse real-world scenarios, many SCD categories are not easy to be clearly recognized, such as “water-vegetation” and “water-tree”, which can be regarded as fine-grained differences. In addition, even a single LCM category is usually difficult to define. For instance, some “vegetation” categories with litter vegetation coverage are easily confused with the general “ground” category. SCD/LCM becomes challenging under both challenges of its fine-grained nature and label ambiguity. In this paper, we tackle the SCD and LCM tasks simultaneously by proposing a coarse-to-fine attention tree (CAT) model. Specifically, it consists of an encoder, a decoder and a coarse-to-fine attention tree module. The encoder-decoder structure extracts the high-level features from input multi-temporal images first and then reconstructs them to return SCD and LCM predictions. Our coarse-to-fine attention tree, on the one hand, utilizes the tree structure to better model a hierarchy of categories by predicting the coarse-grained labels first and then predicting the fine-grained labels later. On the other hand, it applies the attention mechanism to capture discriminative pixel regions. Furthermore, to address label ambiguity in SCD/LCM, we also equip a label distribution learning loss upon our model. Experiments on the large-scale SECOND dataset justify that the proposed CAT model outperforms state-of-the-art models. Moreover, various ablation studies have demonstrated the effectiveness of tailored designs in the CAT model for solving semantic change detection problems.

Multi-SUAV Collaboration and Low-Altitude Remote Sensing Technology-Based Image Registration and Change Detection Network of Garbage Scattered Areas in Nature Reserves

Change detection is an important task in remote sensing image processing and analysis. However, due to position errors and wind interference, bi-temporal low-altitude remote sensing images collected by SUAVs often suffer from different viewing angles. The existing methods need to use an independent registration network for registration before change detection, which greatly reduces the integrity and speed of the task. In this work, we propose an end-to-end network architecture RegCD-Net to address change detection problems in the bi-temporal SUAVs’ low-altitude remote sensing images. We utilize global and local correlations to generate an optical flow pyramid and realize image registration through layer-by-layer optical flow fields. Then we use a nested connection to combine the rich semantic information in deep layers of the network and the precise location information in the shallow layers and perform deep supervision through the combined attention module to finally achieve change detection in bi-temporal images. We apply this network to the task of change detection in the garbage-scattered areas of nature reserves and establish a related dataset. Experimental results show that our RegCD-Net outperforms several state-of-the-art CD methods with more precise change edge representation, relatively few parameters, fast speed, and better integration without additional registration networks.

Online change-point detection: a weighted sum approach with constraint and application to dynamic network observation

This article considers the change detection problem for one-dimensional observation sequences and dynamic network observation sequences. Since for network data, especially for large-scale network data, it is unrealistic to obtain the underlying distribution or underlying probabilistic structure. In this view, we consider a weighted sum approach with constraint for change detection. The purpose of the constraint is to highlight the changes, thus the method proposed has better performance for small shift. Meanwhile, different metrics for network data are suitable for different types of changes, the detection ability of L 1-norm is better for dense change, and the detection ability of max-norm is better for sparse change. A parallel multi-chart is proposed as a guidance for improving the performance of change detection for different types of changes. Furthermore, the theoretical results are illustrated numerically on one-dimensional observation sequences and dynamic network observation sequences.

Non-Parametric Quickest Mean-Change Detection

The problem of quickest detection of a change in the mean of a sequence of independent observations is studied. The pre-change observations are assumed to be stationary, while the post-change observations are allowed to be non-stationary. The case where the pre-change distribution is known is studied first, and then the extension where only the mean and variance of the pre-change distribution are known. No knowledge of the post-change distributions is assumed other than that the means of the observations are above some pre-specified threshold larger than the pre-change mean. For the case where the pre-change distribution is known, a test is derived that asymptotically minimizes the worst-case detection delay over all possible post-change distributions, as the false alarm rate goes to zero. Towards deriving this asymptotically optimal test, some new results are provided for the general problem of asymptotic minimax robust quickest change detection in non-stationary settings. Then, the limiting form of the optimal test is studied as the gap between the pre- and post-change means goes to zero, called the Mean-Change Test (MCT). It is shown that the MCT can be designed with only knowledge of the mean and variance of the pre-change distribution. The performance of the MCT is also characterized when the mean gap is moderate, under the additional assumption that the distributions of the observations have bounded support. The analysis is validated through numerical results for detecting a change in the mean of a beta distribution. The use of the MCT in monitoring pandemics is also demonstrated.

A Deep Learning Model for Change Detection on Satellite Images

Change detection is a classical problem in satellite imaging. The change detection problem aims at the analysis of changes between two images. In this work, we test a deep learning model proposed in 2019 by Caye Daudt et al. on data from the Sentinel-2 satellite with images between 10 m and 60 m of spatial resolution. The model uses the early fusion technique combined with a U-net architecture and can be used on color or multispectral images. The tests are performed on the Onera Satellite Change Detection (OSCD) dataset, which was already used for testing deep learning methods for the change detection problem. Here we propose some experiments to evaluate the performance and limits of the algorithm by Caye Daudt et al. **This is an MLBriefs article, the source code has not been reviewed!**<br> **The original source code is [[available here|https://github.com/rcdaudt/fully_convolutional_change_detection]] (last checked 2022/11/26).**

A Hybrid Metaheuristic and Deep Learning Approach for Change Detection in Remote Sensing Data

This study aimed to adapt Convolutional Neural Networks (CNN) to solve the problem of change detection using remote sensing imagery. Specifically, the goal was to investigate the impact of each CNN layer to detect changes between two satellite images acquired on two different dates. As low-level CNN layers detect fine details (small changes) and higher-level layers detect coarse details (large changes), the idea was to assign a weight to each layer and use a genetic algorithm based on a training dataset to generalize the detection process on the test dataset. The results showed the effectiveness of the proposed approach based on two real-life datasets.

PlaneSDF-Based Change Detection for Long-Term Dense Mapping

The ability to process environment maps across multiple sessions is critical for robots operating over extended periods of time. Specifically, it is desirable for autonomous agents to detect changes amongst maps of different sessions so as to gain a conflict-free understanding of the current environment. In this paper, we look into the problem of change detection based on a novel map representation, dubbed Plane Signed Distance Fields (PlaneSDF), where dense maps are represented as a collection of planes and their associated geometric components in SDF volumes. Given point clouds of the source and target scenes, we propose a three-step PlaneSDF-based change detection approach: (1) PlaneSDF volumes are instantiated within each scene and registered across scenes using plane poses; 2D height maps and object maps are extracted per volume via height projection and connected component analysis. (2) Height maps are compared and intersected with the object map to produce a 2D change location mask for changed object candidates in the source scene. (3) 3D geometric validation is performed using SDF-derived features per object candidate for change mask refinement. We evaluate our approach on both synthetic and real-world datasets and demonstrate its effectiveness via the task of changed object detection. Supplementary video: https://youtu.be/oh-MQPWTwZI

A novel multiple change detection approach based on tri-temporal logic-verified change vector analysis in posterior probability space

• TLCVAPS gets rid of the dependence on change class information that contained in training samples, reducing the cost of sample acquisition in multiple change detection problems. • TLCVAPS avoids the error accumulation caused by comparing classification results at different dates for change trajectory detection. • TLCVAPS is the first attempt to introduce temporal logic into multiple change detection tasks, which is a pilot research for the utilization of time information. Detailed land cover change trajectory offers a better opportunity for understanding the dynamic of land surface process. However, change information contained in training samples, which are usually difficult to obtain, needs to be provided in advance to achieve such goal within a complex scenario. A novel multiple change detection approach, namely Tri-temporal Logic-verified Change Vector Analysis (TLCVA) in posterior Probability Space (TLCVAPS), was proposed in this paper. It removes the dependence on change class information contained in training samples, while reducing the detection errors by taking change logic into account for the first time. The proposed approach consists of three steps, including: (1) change vector produced in posterior probability space, (2) binary change detection via TLCVA, and (3) change trajectory identification through combining change vector angle comparison and logic verification in change pattern. We applied the proposed approach on three tri-temporal datasets obtained in Nanjing, Xianning, and Zhenjiang, China, respectively. The results confirmed the superiority of TLCVAPS in comparison with state-of-the-art multiple change detection methods with the same prior knowledge.

Object-Oriented Unsupervised Change Detection Based on Neighborhood Correlation Images and k-Means Clustering for the Multispectral and High Spatial Resolution Images

An unsupervised change-detection problem is formulated as a binary classification problem corresponding to the change and no change areas. This paper proposes a novel unsupervised object-oriented change detection method based on neighborhood correlation images (NCIs) and k-means clustering for high-resolution remote sensing images. We tested our proposed method in two study areas of Beijing with RapidEye images and compared it with three other popular change detection methods based on different images: change vector analysis (CVA), principal component analysis (PCA), and multivariate alteration detection (MAD). The results indicate that our method has the highest overall accuracy (90.80% in Shunyi District, Beijing and 90.40% in Daxing District, Beijing) and Kappa coefficient (0.7922 in Shunyi District, Beijing and 0.7796 in Daxing District, Beijing). In addition, the McNemar test indicates that our method is robust and stable across different study areas. We concluded that the object-oriented NCIs method outperforms traditional difference images (CVA, PCA, and MAD) in unsupervised change detection. The experimental results demonstrate the effectiveness of the proposed approach in solving the problem of unsupervised change detection for high-resolution images.