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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.

This folder contains the datasets used for the paper "Transfer learning with weak labels from radiology reports: application to glioma change detection". A preprint of the manuscript can be found on arXiv. Inside the parent directory, you will find two datasets: one is the in-house dataset from the University Hospital of Lausanne (CHUV), and the second is made ofpreprocessed difference maps from the longitudinal patientsof the BraTS-TCIA-2015 dataset. The labels associated with both datasets can be found on the corresponding github repository (https://github.com/connectomicslab/Glioma_Change_Detection_T2w/tree/master/extra_files). Inside the in-house dataset directory, you will find two subdirectories (sub-datasets) named HAD_diffmaps and WAD_diffmaps. Both sub-datasets contain T2w difference maps of patients with high-grade gliomas. HAD stands for Human-Annotated Dataset: as explained in the paper, the labels for this sub-dataset were created manually from three radiologists by looking at the corresponding radiology reports. Conversely, WAD stands for Weakly-Annotated Dataset: for this sub-dataset, the labels were created automatically with a Natural Language Processing pipeline for radiology reports. Please refer to the paper for more details. For each subject inside any of the two sub-datasets you will find session pairs. This is because every report/difference map links two time points (i.e. two sessions), since we tackle longitudinal change detection. Then, inside each session pair folder, you will find the following files: - sub-XX_ses-001_vs_ses-002_comparative_bet_cropped_t2_n4.nii.gz -> is the comparative/previous (i.e. ses-001) T2w volume already with N4 bias field correction (bfc), and already skull-stripped (bet = brain extraction performed with HD-BET) - sub-XX_ses-001_vs_ses-002_comparative_bet_cropped_zscored_t2_n4.nii.gz -> is the comparative T2w volume with N4 bfc, bet, and z-score normalized - sub-XX_ses-001_vs_ses-002_current_bet_cropped_t2_n4.nii.gz -> is the current (i.e. ses-002) T2w volume with N4 bfc, and bet - sub-XX_ses-001_vs_ses-002_current_bet_cropped_zscored_t2_n4.nii.gz -> is the current Tw2 volume with N4 bfc, bet, and z-score normalized - sub-XX_ses-001_vs_ses-002_difference_t2_volumes.nii.gz -> is the voxel-wise difference maps between the two normalized volumes. It is the one used for classification in the paper - sub-XX_ses-001_vs_ses-002_out_prev_2_curr_0GenericAffine.mat -> is the affine matrix generated by ANTs when registering the previous comparative volume to the current - sub-XX_ses-001_vs_ses-002_reg_quality_metrics_comp2curr.csv -> it contains some metrics (Neighborhood Correlation and Mutual Information) to monitor the registration quality &nbsp

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.

Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study Dynamic Multi-objective Optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called Multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of Dynamic Multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.

The loss of archaeological heritage continues today, because of both natural disasters and human-made actions. Alarmingly, a significant amount of the destruction is perpetrated by looters and illegal excavations. This problem is not a new one. However, it has increased exponentially in recent years, especially in countries which witnessed internal turmoil (i.e., the Arab Spring) but also throughout Europe. Local authorities struggle to provide adequate controls because of a lack of human resources, budget constraints or technological know-how. This paper describes a multimodal documentation and monitoring workflow applied to an archaeological site for which, due to the sensitivity of the topic, no specific details can be publicly disclosed. The techniques used include UAV aerial surveys, image-based modelling, change detection, relief visualization and GIS mapping. Thanks to the analysis of the multitemporal datasets, it was possible to assess the extension and spatial progression of illegal excavation over a two-year period.

Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material segmentation, corrosion condition state segmentation, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel annotation guidelines and benefitted from a novel semi-automated annotation process for both object detection and pixel-level detection models. Using the data obtained from our collected sources, task-appropriate deep learning models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a GAN-Inversion model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not wide-spread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes. Doctor of Philosophy Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material detection, corrosion condition state identification, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel labeling guidelines and benefitted from a novel semi-automated labeling process for the artificial intelligence models. Using the data obtained from our collected sources, task-appropriate artificial intelligence models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a generative model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not widespread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes.

Over the last couple of years both cameras, audio and radio sensors have become cheaper and more common in our everyday lives. Such sensors can be used to create maps of where the sensors are positioned and the appearance of the surroundings. For sound and radio, the process of estimating the sender and receiver positions from time of arrival (TOA) or time-difference of arrival (TDOA) measurements is referred to as automatic calibration. The corresponding process for images is to estimate the camera positions as well as the positions of the objects captured in the images. This is called structure from motion (SfM) or visual simultaneous localisation and mapping (SLAM). In this thesis we present studies on how to create such maps, divided into three parts: to find accurate measurements; robust mapping; and merging of maps.The first part is treated in Paper I and involves finding precise – on a subsample level – TDOA measurements. These types of subsample refinements give a high precision, but are sensitive to noise. We present an explicit expression for the variance of the TDOA estimate and study the impact that noise in the signals has. Exact measurements is an important foundation for creating accurate maps. The second part of this thesis includes Papers II–V and covers the topic of robust self-calibration using one-dimensional signals, such as sound or radio. We estimate both sender and receiver positions using TOA and TDOA measurements. The estimation process is divided in two parts, where the first is specific for TOA or TDOA and involves solving a relaxed version of the problem. The second step is common for different types of problems and involves an upgrade from the relaxed solution to the sought parameters. In this thesis we present numerically stable minimal solvers for both these steps for some different setups with senders and receivers. We also suggest frameworks for how to use these solvers together with RANSAC to achieve systems that are robust to outliers, noise and missing data. Additionally, in the last paper we focus on extending self-calibration results, especially for the sound source path, which often cannot be fully reconstructed immediately. The third part of the thesis, Papers VI–VIII, is concerned with the merging of already estimated maps. We mainly focus on maps created from image data, but the methods are applicable to sparse 3D maps coming from different sensor modalities. Merging of maps can be advantageous if there are several map representations of the same environment, or if there is a need for adding new information to an already existing map. We suggest a compact map representation with a small memory footprint, which we then use to fuse maps efficiently. We suggest one method for fusion of maps that are pre-aligned, and one where we additionally estimate the coordinate system. The merging utilises a compact approximation of the residuals and allows for deformations in the original maps. Furthermore, we present minimal solvers for 3D point matching with statistical deformations – which increases the number of inliers when the original maps contain errors.

In this paper, various methods have been used to control and evaluate engineering difficulties in mining accurately. Different unstable scenarios occurring at the surfaces of underground mine walls, have been identified by comparing 3D terrestrial laser scanning surveys and subsequent point cloud 3D analysis. These techniques, combined with a change detection analysis approach and the integration of rock mechanics’ modelling, represent an asset for the assessment and management of the risk in mining. The change detection analysis can be used as control of mining and industrial processes as well as to identify valid model scenarios for establishing possible failure causes. A pillar spalling failure has been identified in an Italian underground marble quarry and this topic represents the basis of the present paper. A Finite-Element Method was used to verify the occurrence of relatively high-stress concentrations in the pillar. The FEM modelling revealed that stresses in the proximity of the pillar may have sufficient magnitude to induce cracks growth and spalling failure.

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

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.

With the development of Earth observation technology, very-high-resolution (VHR) image has become an important data source of change detection. Nowadays, deep learning methods have achieved conspicuous performance in the change detection of VHR images. Nonetheless, most of the existing change detection models based on deep learning require annotated training samples. In this paper, a novel unsupervised model called kernel principal component analysis (KPCA) convolution is proposed for extracting representative features from multi-temporal VHR images. Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multi-class change detection. In the KPCA-MNet, the high-level spatial-spectral feature maps are extracted by a deep siamese network consisting of weight-shared PCA convolution layers. Then, the change information in the feature difference map is mapped into a 2-D polar domain. Finally, the change detection results are generated by threshold segmentation and clustering algorithms. All procedures of KPCA-MNet does not require labeled data. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of the proposed method in two binary change detection data sets and one multi-class change detection data set.

The wide availability of embedded sensor platforms and lowcost cameras—together with the developments in wireless communication—make it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the peculiarities of visual sensor networks with respect to standard vision systems and we identify possible strategies to accomplish image processing and analysis tasks over them. Although the rather strong constraints in computational and transmission power of embedded platforms that may prevent the use of state of the art computer vision and pattern recognition methods, we argue that multinode processing methods may be envisaged to decompose a complex task into a hierarchy of computationally simpler problems to be solved over the nodes of the network. These ideas are illustrated by describing an application of visual sensor network to infomobility. In particular, we consider an experimental setting in which several views of a parking lot are acquired by the sensor nodes in the network. By integrating the various views, the network is capable to provide a description of the scene in terms of the available spaces in the parking lot.

Change detection (CD) is an important remote sensing (RS) data analysis technology. Existing remote sensing change detection (RS-CD) technologies cannot fully consider situations where pixels between bitemporal images do not correspond well on a one-to-one basis due to factors such as seasonal changes and lighting conditions. Existing networks construct two identical feature extraction branches through convolution, which share weights. The two branches work independently and do not merge until the feature mapping is sent to the decoder head. This results in a lack of feature information interaction between the two images. So, directing attention to the change area is of research interest. In complex backgrounds, the loss of edge details is very important. Therefore, this paper proposes a new CD algorithm that extracts multi-scale feature information through the backbone network in the coding stage. According to the task characteristics of CD, two submodules (the Feature Interaction Module and Detail Feature Guidance Module) are designed to make the feature information between the bitemporal RS images fully interact. Thus, the edge details are restored to the greatest extent while fully paying attention to the change areas. Finally, in the decoding stage, the feature information of different levels is fully used for fusion and decoding operations. We build a new CD dataset to further verify and test the model’s performance. The generalization and robustness of the model are further verified by using two open datasets. However, due to the relatively simple construction of the model, it cannot handle the task of multi-classification CD well. Therefore, further research on multi-classification CD algorithms is recommended. Moreover, due to the high production cost of CD datasets and the difficulty in obtaining them in practical tasks, future research will look into semi-supervised or unsupervised related CD algorithms.

Woody vegetation landscape features, such as hedges, tree patches, and riparian vegetation, are important elements of landscape and biotic diversity. For the reason that biodiversity loss is one of the major ecological problems in the EU, it is necessary to establish efficient workflows for the registration and monitoring of woody vegetation landscape features. In the paper, we propose and evaluate a methodology for automated detection of changes in woody vegetation landscape features from a digital orthophoto (DOP). We demonstrate its ability to capture most of the actual changes in the field and thereby provide valuable support for more efficient maintenance of landscape feature layers, which is important for the shaping of future environmental policies. While the most reliable source for vegetation cover mapping is a combination of LiDAR and high-resolution imagery, it can be prohibitively expensive for continuous updates. The DOP from cyclic aerial photography presents an alternative source of up-to-date information for tracking woody vegetation landscape features in-between LiDAR recordings. The proposed methodology uses a segmentation neural network, which is trained with the latest DOP against the last known ground truth as the target. The output is a layer of detected changes, which are validated by the user before being used to update the woody vegetation landscape feature layer. The methodology was tested using the data of a typical traditional Central European cultural landscape, Goričko, in north-eastern Slovenia. The achieved F1 of per-pixel segmentation was 83.5% and 77.1% for two- and five-year differences between the LiDAR-based reference and the DOP, respectively. The validation of the proposed changes at a minimum area threshold of 100 m2 and a minimum area percentage threshold of 20% showed that the model achieved recall close to 90%.

Change detection (CD), as a special remote-sensing (RS) segmentation task, faces challenges, including alignment errors and illumination variation, dense small targets, and large background intraclass variance in very high-resolution (VHR) remote-sensing images. Recent methods have avoided the misjudgment caused by illumination variation and alignment errors by increasing the ability of global modeling, but the latter two problems have still not been fully addressed. In this paper, we propose a new CD model called SFCD, which increases the feature extraction capability for small targets by introducing a shifted-window (Swin) transformer. We designed a foreground-aware fusion module to use attention gates to trim low-level feature responses, enabling increased attention to the changed region compared to the background when recovering the changed region, thus reducing background interference. We evaluated our model on two CD datasets, LEVIR-CD and CDD, and obtained F1 scores of 91.78 and 97.87, respectively. The experimental results and visual interpretation show that our model outperforms several previous CD models. In addition, we adjusted the parameters and structure of the standard model to develop a lightweight version that achieves an accuracy beyond most models with only 1.55 M parameters, further validating the effectiveness of our design.

In recent years, Africa has seen much construction of large-scale hydrological infrastructures in the arid and semi-arid regions of numerous countries. This paper aims to quantify the effects of this form of hydrological infrastructure, especially the Upper Atbara and Setit Dam Complex (UASDC) in Eastern Sudan, on the land use/cover (LUC) and socioeconomic domains. This paper attempts to advance our understanding of this phenomenon by using multiple approaches. A framework using the integration of 3S technologies and a logical approach for quantifying the significance of the results to society has been developed. The method used Landsat5 TM in 2002, Sentinel2A in 2018, and statistical data to create the LUC map. The final map included seven classes; the overall accuracy of changes in LUC patterns was 94.9% in 2002 and 93% in the results reveal that significant changes occurred in terms of LUC, having a considerable effect on socio-economic development. The results were analyzed with the logical approach for overall objectives, where 85% represents S1, 3.3% represents S2, and 11.7% represents S3, respectively. This study provides an insight into further investigations of the dam’s effect on climate and groundwater, and offers a new perspective on land use prediction, simulation, and environmental sustainability.

Flexibility and evolution have been a hot topic in the context of business process management. Business process drift detection is a family of methods to detect changes by analyzing the event log, but existing methods have some disadvantages in dealing with concept drifts. First, most of these methods detect changes depending on an exploration of a potentially large feature space and are time consuming. Second, with the size of lag period becoming small, the accuracies of some methods will drop rapidly. In this paper, we address these problems by proposing a novel drift detection technique called LCDD that significantly differs from existing methods. The core idea is to find out new features and disappeared features from traces after the log fragment meets local completeness. To begin with, we use the relations of direct succession as the lightweight feature, which are compared between complete window and detection window. Then, we find new direct successions and stable disappeared direct successions by just moving detection window. Finally, the forgetting mechanism is used to abandon some direct successions after finding a change point. An extensive empirical evaluation shows that LCDD is faster and more accurate.

Let $\boldsymbol{X}=(X_t)_{t\ge 0}$ be a process behaving as a general increasing Lévy process (subordinator) prior to hitting a given unknown level $m_0$, then behaving as another different subordinator once this threshold is crossed. This paper addresses the detection of this unknown threshold $m_0\in [0,+\infty]$ from an observed trajectory of the process. These kind of model and issue are encountered in many areas such as reliability and quality control in degradation problems. More precisely, we construct, from a sample path and for each $\epsilon >0$, a so-called detection level $L_\epsilon$ by considering a CUSUM inspired procedure. Under mild assumptions, this level is such that, while $m_0$ is infinite (i.e. when no changes occur), its expectation $ \mathbb{E}_{\infty}(L_{\epsilon})$ tends to $+\infty$ as $\epsilon$ tends to $0$, and the expected overshoot $ \mathbb{E}_{m_0}([L_{\epsilon} - m_0]^+)$, while the threshold $m_0$ is finite, is negligible compared to $ \mathbb{E}_{\infty}(L_{\epsilon})$ as $\epsilon$ tends to $0$. Numerical illustrations are provided when the Lévy processes are gamma processes with different shape parameters.

Remote sensing change detection involves detecting pixels that have changed from a bi-temporal image of the same location. Current mainstream change detection models use encoder-decoder structures as well as Siamese networks. However, there are still some challenges with this: (1) Existing change feature fusion approaches do not take into account the symmetry of change features, which leads to information loss; (2) The encoder is independent of the change detection task, and feature extraction is performed separately for dual-time images, which leads to underutilization of the encoder parameters; (3) There are problems of unbalanced positive and negative samples and bad edge region detection. To solve the above problems, a mutual feature-aware network (MFNet) is proposed in this paper. Three modules are proposed for the purpose: (1) A symmetric change feature fusion module (SCFM), which uses double-branch feature selection without losing feature information and focuses explicitly on focal spatial regions based on cosine similarity to introduce strong a priori information; (2) A mutual feature-aware module (MFAM), which introduces change features in advance at the encoder stage and uses a cross-type attention mechanism for long-range dependence modeling; (3) A loss function for edge regions. After detailed experiments, the F1 scores of MFNet on SYSU-CD and LEVIR-CD were 83.11% and 91.52%, respectively, outperforming several advanced algorithms, demonstrating the effectiveness of the proposed method.

In an era of rapid technological improvements, state-of-the-art methodologies and tools dedicated to protecting and promoting our cultural heritage should be developed and extensively employed in the contemporary built environment and lifestyle. At the same time, sustainability principles underline the importance of the continuous use of historic or vernacular buildings as part of the building stock of our society. Adopting a holistic, integrated, multi-disciplinary strategy can link technological innovation with the conservation and restoration of heritage buildings. This paper presents the ongoing research and results of the application of Machine Learning methods for the remote monitoring of the built environment of the historic cluster in Cypriot cities. This study is part of an integrated, multi-scale, and multi-disciplinary study of heritage buildings, with the end goal of creating an online HBIM platform for urban monitoring.

In traditional image processing, the Fourier transform is often used to transform an image from the spatial domain to the frequency domain, and frequency filters are designed from the perspective of the frequency domain to sharpen or blur the image. In the field of remote sensing change detection, deep learning is beginning to become a mainstream tool. However, deep learning can still refer to traditional methodological ideas. In this paper, we designed a new convolutional neural network (MFGFNet) in which multiple global filters (GFs) are used to capture more information in the frequency domain, thus sharpening the image boundaries and better preserving the edge information of the change region. In addition, in MFGFNet, we use CNNs to extract multi-scale images to enhance the effects and to better focus on information about changes in different sizes (multi-scale combination module). The multiple pairs of enhancements are fused by the difference method and then convolved and concatenated several times to obtain a better difference fusion effect (feature fusion module). In our experiments, the IOUs of our network for the LEVIR-CD, SYSU, and CDD datasets are 0.8322, 0.6780, and 0.9101, respectively, outperforming the state-of-the-art model and providing a new perspective on change detection.

High-resolution remote sensing image change detection technology compares and analyzes bi-temporal or multitemporal high-resolution remote sensing images to determine the change areas. It plays an important role in land cover/use monitoring, natural disaster monitoring, illegal building investigation, military target strike effect analysis, and land and resource investigation. The change detection of high-resolution remote sensing images has developed rapidly from data accumulation to algorithm models because of the rapid development of technologies such as deep learning and earth observation in recent years. However, the current deep learning-based change detection methods are strongly dependent on large sample data, and the training model has insufficient cross-domain generalization ability. As a result, a prior semantic information-guided change detection framework (PSI-CD), which alleviates the change detection model’s dependence on datasets by making full use of prior semantic information, is proposed in this paper. The proposed method mainly includes two parts: one is a prior semantic information generation network that uses the semantic segmentation dataset to extract robust and reliable prior semantic information; the other is the prior semantic information guided change detection network that makes full use of prior semantic information to reduce the sample size of the change detection. To verify the effectiveness of the proposed method, we produced pixel-level semantic labels for the bi-temporal images of the public change detection dataset (LEVIR-CD). Then, we performed extensive experiments on the WHU and LEVIR-CD datasets, including comparisons with existing methods, experiments with different amounts of data, and ablation study, to show the effectiveness of the proposed method. Compared with other existing methods, our method has the highest IoU for all training samples and different amounts of training samples on WHU and LEVIR-CD, reaching a maximum of 83.25% and 83.80%, respectively.

Like natural catastrophes or armed conflicts, resource extraction projects herald the alteration or destruction of natural and cultural landscapes alike. Dam construction is a major threat to cultural heritage in Western Asian archaeology. One event may result in obliterating hundreds of sites, most of which never reappear or do so only sporadically following cyclical water fluctuation. Destruction of sites remains ongoing, necessitating constant assessment of damage and the establishment of strategies of documentation and maintenance. This paper proposes a new paradigm for future safeguarding and, more widely, a new tool for managing contiguous terrestrial and lacustrine cultural zones. It outlines a new set of cost-efficient tools for observing these archaeological localities’ “emergence patterns” and quickly assessing damage timescales and site areas. As a case study, Iraq’s largest hydroelectric basin, the Mosul Dam reservoir on the upper Tigris, is discussed, as it offers several insights into tackling endemic issues of site recording, monitoring, and threat assessment in a constantly shifting environment. The research has profited from an unexpected drought in 2018 yielding archaeological sites and villages thought forever lost and is informed by new archaeological projects recently undertaken on the eastern bank of the lake.

Visual change detection in video is one of the essential tasks in computer vision applications. Recently, a number of supervised deep learning methods have achieved top performance over the benchmark datasets for change detection. However, inconsistent training-testing data division schemes adopted by these methods have led to documentation of incomparable results. We address this crucial issue through our own propositions for benchmark comparative analysis. The existing works have evaluated the model in scene dependent evaluation setup which makes it difficult to assess the generalization capability of the model in completely unseen videos. It also leads to inflated results. Therefore, in this paper, we present a completely scene independent evaluation strategy for a comprehensive analysis of the model design for change detection. We propose well-defined scene independent and scene dependent experimental frameworks for training and evaluation over the benchmark CDnet 2014, LASIESTA and SBMI2015 datasets. A cross-data evaluation is performed with PTIS dataset to further measure the robustness of the models. We designed a fast and lightweight online end-to-end convolutional network called ChangeDet (speed-58.8 fps and model size-1.59 MB) in order to achieve robust performance in completely unseen videos. The ChangeDet estimates the background through a sequence of maximum multi-spatial receptive feature (MMSR) blocks using past temporal history. The contrasting features are produced through the assimilation of temporal median and contemporary features from the current frame. Further, these features are processed through an encoder-decoder to detect pixel-wise changes. The proposed ChangeDet outperforms the existing state-of-the-art methods in all four benchmark datasets.

In this paper, the problem of detecting accidents using speed sensors distributed spatially on a freeway is considered. Due to the significant impact of road accidents on health and development, early and accurate detection of accidents is crucial. To address this issue, a novel Bayesian quickest change detection formulation is introduced, which considers both average detection delay and false alarm rate. The optimum strategy is derived via dynamic programming and shown to compare a recursively computed statistic with a function of false alarm to identify accidents as they happen. Considering that post-accident conditions are typically not known, two methods are proposed that recursively estimate multiple unknown parameters during the accident detection process. Further, four aggregation methods are proposed to improve performance exploiting spatial correlations between sensors. Extensive evaluation results demonstrate improvement up to 65.2% and 87.2% in average detection delay and false alarm rate, respectively, against prior work.

Change detection is the amount of changes that can guide to more concrete understandings into essential method concerning land cover, land usage and ecological variations. This paper deals with an intelligent methodology to optimize the solution out of the solution space that improves the efficiency of the change detection process. To support the theme, an integrated semi-supervised method is designed with a focus on image fusion, semi supervised clustering and binary swarm based optimization. A new approach of handling fusion using sparse coding is referred to expand the amount of information. Using the extracted information, change detection process is carried out by a constrained clustering technique to provide a solution reflecting the level of changes occurred in the region of investigation. To improve the accuracy by proposing a global optimum solution, still the result is refined through binary swarm based optimization process and hence the results are accelerated towards the increased level of accuracy followed by which the change map is reconstructed to show case changes prominently. To determine the accurateness of the proposed methodology quantitative and qualitative analysis has been done with different datasets. The proposed method has been evaluated with existing techniques such as k-means, AKM, FCM, ECKM and ASCC to show the efficiency and proved to be the preeminent change detection methodology compared to the state-of-art methods.

The superpixel generation is a key step for object-based classification and change detection. For the time-series PolSAR superpixel generation, the traditional polarimetric similarity measure based on the joint covariance matrix has limitations in discriminating different time-series similarity sequences with different fluctuations. Besides, in the traditional time-series PolSAR superpixel generation methods, it is difficult to determine the tradeoff factor between polarimetric and spatial similarity. In this paper, an adaptive time-series PolSAR superpixel generation method based on the simple local iterative clustering (SLIC) is proposed, named as TSPol-ASLIC. There are three main improvements. Firstly, a novel time-series polarimetric similarity measure based on the root mean square (RMS) is proposed. Multi-temporal polarimetric statistical information are combined to describe the polarimetric proximity between pixels, referring to the RMS of the multi-temporal proximities. Secondly, an edge detection method based on the stacked two-dimensional Gaussian-shaped (s2-D GS) window is proposed to initialize the central seeds for superpixel generation. Thirdly, an improved SLIC clustering similarity combined with the time-series polarimetric, time-series power and spatial similarities is proposed. Meanwhile, a homogeneity factor is applied to adaptively balance the relative weights of various similarities. We use 8 Radarsat-2 quad-polarization SAR images and 14 Sentinel-1 dual-polarization SAR images to evaluate the effectiveness. The results show our similarity measure and superpixel generation results are superior to those of the traditional methods. For example, as for the Radarsat-2 data, the improvement of the boundary recall by the proposed similarity measure and homogeneity factor is about 4% and 10%, respectively

With the wide application of deep learning (DL), change detection (CD) for remote sensing images (RSIs) has realized the leap from the traditional to the intelligent methods. However, many existing methods still need further improvement in practical applications, especially in increasing the effectiveness of feature extraction and reducing model computational cost. In this paper, we propose a novel deep multiscale Siamese network with parallel convolutional structure (PCS) and self-attention (SA) (MSPSNet), which has excellent capabilities of feature extraction and feature integration under an acceptable consumption. It mainly contains three subnetworks: deep multiscale feature extraction, feature integration by the PCS, and feature refinement based on the SA. In the first subnetwork, a deep multiscale Siamese network based on convolutional block is designed to depict image features at different scales for different temporal images. In the subsequent subnetworks, a PCS model is proposed to integrate multiscale features of different temporal images, and then, a SA model is constructed to further enhance the representation of image information. Experiments are conducted on two public RSIs datasets, indicating that the proposed framework performs well in detecting changes.

Multi-temporal high spatial resolution earth observation makes it possible to detect complex urban land surface changes, which is a significant and challenging task in remote sensing communities. Previous works mainly focus on binary change detection (BCD) based on modern technologies, e.g., deep fully convolutional network (FCN), whereas the deep network architecture for semantic change detection (SCD) is insufficiently explored in current literature. In this paper, we propose a deep multi-task encoder-transformer-decoder architecture (ChangeMask) designed by exploring two important inductive biases: sematic-change causal relationship and temporal symmetry. ChangeMask decouples the SCD into a temporal-wise semantic segmentation and a BCD, and then integrates these two tasks into a general encoder-transformer-decoder framework. In the encoder part, we design a semantic-aware encoder to model the semantic-change causal relationship. This encoder is only used to learn semantic representation and then learn change representation from semantic representation via a later transformer module. In this way, change representation can constrain semantic representation during training, which introduces a regularization to reduce the risk of overfitting. To learn a robust change representation from semantic representation, we propose a temporal-symmetric transformer (TST) to guarantee temporal symmetry for change representation and keep it discriminative. Based on the above semantic representation and change representation, we adopt simple multi-task decoders to output semantic change map. Benefiting from the differentiable building blocks, ChangeMask can be trained by a multi-task loss function, which significantly simplifies the whole pipeline of applying ChangeMask. The comprehensive experimental results on two large-scale SCD datasets confirm the effectiveness and superiority of ChangeMask in SCD. Besides, to demonstrate the potential value in real-world applications, e.g., automatic urban analysis and decision-making, we deploy the ChangeMask to map a large geographic area covering 30 km2 with 300 million pixels. Code will be made available.

Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study change detection in binary streams. To this end, we use a likelihood ratio between two models as a measure for indicating change. The first model is a single bernoulli variable while the second model divides the stored data in two segments, and models each segment with its own bernoulli variable. Finding the optimal split can be done in \( \mathcal {O} \mathopen {}\left( n\right) \) time, where n is the number of entries since the last change point. This is too expensive for large n. To combat this we propose an approximation scheme that yields \((1 - \epsilon )\) approximation in \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log ^2 n\right) \) time. The speed-up consists of several steps: First we reduce the number of possible candidates by adopting a known result from segmentation problems. We then show that for fixed bernoulli parameters we can find the optimal change point in logarithmic time. Finally, we show how to construct a candidate list of size \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log n\right) \) for model parameters. We demonstrate empirically the approximation quality and the running time of our algorithm, showing that we can gain a significant speed-up with a minimal average loss in optimality.

Deep learning methods are widely used in the domain of change detection in remote sensing images. While datasets of that kind are abundant, annotated images, specific for the task at hand, are still scarce. Neural networks trained with Self supervised learning aim to harness large volumes of unlabeled satellite high resolution images to help in finding better solutions for the change detection problem. In this paper we experiment with this approach by presenting 4 different change detection methodologies. We propose a fusion method that under specific parameters can provide better results. We evaluate our results using two openly available datasets with Sentinel-2 satellite images, S2MTCP and OSCD, and we investigate the impact of using 2 different Sentinel 2 band combinations on our final predictions. Finally we conclude by summarizing the benefits of this approach as well as we propose future areas of interest that could be of value in enhancing the change detection task’s outcomes.

Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform’s constraints regarding processing time were analyzed by running the detection algorithms on Unibap’s iX10-100 space cloud platform.

This paper presents Triangle-To-Cloud (T2C), a new approach for point cloud change detection. The method is compared to the established method Multiscale Model to Model Cloud Comparison (M3C2) on the accuracy to detect changes, from a point cloud to another. The comparison is performed on Light Detection and Ranging (LiDAR) mappings from an Ouster OS0-128 LiDAR sensor. Both T2C and M3C2 are tested with different parameters in all of the experiments conducted for evaluation. This work demonstrates in the experiments that T2C can outperform M3C2 in its ability to detect changes.

Historical maps depict past states of the Earth's surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence., ISPRS Journal of Photogrammetry and Remote Sensing, 197, ISSN:0924-2716

Climate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. An important factor in climate change is the change in the green cover. Non-availability of standard datasets and limited labeled data points makes it difficult to attain high accuracies in change detection. In this paper, we have proposed a modified ensemble of extreme learning machines (mAEELM) for change detection in green cover to increase the accuracy of the change detection process. It uses Extreme Learning Machine (ELM) as the base classifier. Different ELMs are trained with different configuration so that they have different learning capabilities. These ELMs are combined to create an ensemble and it is then adapted based on the accuracy of the individual ELMs. The ensemble is then pruned to eliminate the ELMs which are not contributing towards the overall result of the ensemble, to make it more efficient. The proposed algorithm has been applied for detecting change on two areas of Gandhuan, Punjab and Chaparkaura Kham, Uttar Pradesh, India. The algorithm shows an average accuracy of 97.8% on both the datasets.

The coastline of the Nile Delta is a dynamic zone in Egypt that is received considerable attention. This region contains many major cities and it also contains a number of economically important harbors such as Damietta, Port Said and Alexandria. This research focuses attention on applying Geographical Information System (GIS) tool to assess prospective changes along the coast of the Nile Delta, Egypt during the past thirty-four years. In this paper, changes to the coastline were monitored using twelve scenes of Landsat images from 1984 to 2018. After the extraction of shorelines from Landsat images, Digital Shoreline Analysis System (DSAS) was used to calculate shoreline displacement rate. The results showed that about 44.2% of the study area was under erosion and 45% was under accretion while it was stable at 10.8% between 1984 and 2018. The results illustrated that Rosetta and Damietta promontories are displayed higher rates of erosion than other coastal regions of the Nile Delta. National measures must be implemented to protect the entire coast in threatened areas.

In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in ChangeDetection.net are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://github.com/walty19950301/CDA-contour-optimizer.

In evaluating the change detection algorithms, the algorithm evaluated must show a superior performance than the state-of-the-art algorithms. The evaluation process steps comprise executing a new algorithm to segment a set of videos from a dataset and compare the results regarding a ground truth. In this paper, we propose using additional information in evaluating change detection algorithms: the level of difficulty in classifying a pixel. First, for each video frame used in the evaluation, we created a difficulty map structure, which stores values representing the level of difficulty required by an algorithm to classify each pixel of that frame. Second, we developed a metric to estimate each dataset video’s difficulty based on our difficulty maps. Third, we applied the metric to selecting the more representative videos from the dataset based on their difficulty level. Finally, to demonstrate the method’s contribution, we evaluated it using all videos from the CDNet 2014 dataset. The results showed that a subset of videos selected by our method has the same potential as the original CDNet 2014 dataset. Hence, a new change detection algorithm can be evaluated more quickly using our subset of videos selected.

Unlike the existing change detection approaches based on the multispectral (MS) image and synthetic aperture radar (SAR) image datasets, a novel unsupervised hyperspectral change detection (UHCD) framework is proposed in this paper. The UHCD framework is designed for hyperspectral images with high dimensions and low availability. This framework consists of two modules: the spectral mapping with adversarial learning and the discriminant analysis with spatial attribute optimization. In comparison with other advanced change detection methods, the proposed framework possesses three distinctive properties: (1) The unsupervised spectral mapping is leveraged to exploit underlying spectral features without the requirement of pseudo-training datasets in the change detection task; (2) We introduce spectral constraint loss into reconstruction space and adversarial loss into latent space to enhance the quality of the features extracted by the spectral mapping network; (3) Spatial attribute optimization uses the spatial correlation to further improve the performance of the proposed UHCD method. The experimental results on two real datasets show that the proposed UHCD achieves competitive performance.

Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated.

Image processing of a target using various methods and types of image processing. detecting an accurate target is hard many times because the targets are hidden in foliage, forest and dense area where humans can’t reach the purpose of the research is to improve our defense power and also our various forces like the army. we study foliage target detection with image processing the processing of the image and detect changes in image processing flow compared two images then resizing them checking the grey scale converting the image coveted again into the binary image to check the intensity and sharpness of the image then we plotted the histogram of the image and then the last step labeling is happening Type of image processing1. Visualization- visualization is the process that detects objects that are unseen in the image. 2. realization- In the appreciation of steps we find or get the objects in the image with accuracy.3. enhance and Reconstruction-make an accurate image from the first image. happening in the sharpening and restoration 4. Pattern recognition Measure the various patterns around the objects in the image. this is the process of pattern recognition 5. Retrieval- in retrieval Browse and search images from a huge record of digital images that do not differ from the original image. The two phases fundamentally make up image processing Analyzing and modifying the image once it has been imported using image-capturing software output, the outcome of which might be a preprocess image or a data collection on the image accuracy. Keywords: Radar, Image processing, target detection, change detection. Title: A Review on Image Processing Algorithm for Foliage Target Detection and Classification Author: Nakshtra Bhavsar, Akanksha Tayade, Hrutesh Ghodmare, N.M. Wagdharikar, Sampurna De, A.A Bazil Raj International Journal of Engineering Research and Reviews ISSN 2348-697X (Online) Vol. 10, Issue 4, October 2022 - December 2022 Page No: 52-61 Research Publish Journals Website: www.researchpublish.com Published Date: 27-December-2022 DOI: https://doi.org/10.5281/zenodo.7486512 Paper Download Link (Source) https://www.researchpublish.com/papers/a-review-on-image-processing-algorithm-for-foliage-target-detection-and-classification, International Journal of Engineering Research and Reviews, ISSN 2348-697X (Online), Research Publish Journals, Website: www.researchpublish.com

Change detection is an important application of remote sensing image interpretation, which identifies changed areas of interest from a pair of bi-temporal remote sensing images. Various deep-learning-based approaches have demonstrated promising results and most of these models used an encoder–decoder shape such as U-Net for segmentation of changed areas. In order to obtain more refined features, this paper introduces a change detection model with cascaded U-Net. The proposed network architecture contains four cascaded U-Nets with ConvNeXT blocks. With a patch embedding layer, the cascaded structure can improve detection results with acceptable computational overhead. To facilitate the training of the cascaded N-Nets, we proposed a novel attention mechanism called the Training whEel Attention Module (TEAM). During the training phase, TEAM aggregates outputs from different stages of cascaded structures and shifts attention from outputs from shallow stages to outputs from deeper stages. The experimental results show that our cascaded U-Net architecture with TEAM achieves state-of-the-art performance in two change detection datasets without extra training data.

The recovery phase following an earthquake event is essential for urban areas with a significant number of damaged buildings. A lot of changes can take place in such a landscape within the buildings’ footprints, such as total or partial collapses, debris removal and reconstruction. Remote sensing data and methodologies can considerably contribute to site monitoring. The main objective of this paper is the change detection of the building stock in the settlement of Vrissa on Lesvos Island during the recovery phase after the catastrophic earthquake of 12 June 2017, through the analysis and processing of UAV (unmanned aerial vehicle) images and the application of Artificial Neural Networks (ANNs). More specifically, change detection of the settlement’s building stock by applying an ANN on Gray-Level Co-occurrence Matrix (GLCM) texture features of orthophotomaps acquired by UAVs was performed. For the training of the ANN, a number of GLCM texture features were defined as the independent variable, while the existence or not of structural changes in the buildings were defined as the dependent variable, assigning, respectively, the values 1 or 0 (binary classification). The ANN was trained based on the Levenberg–Marquardt algorithm, and its ability to detect changes was evaluated on the basis of the buildings’ condition, as derived from the binary classification. In conclusion, the GLCM texture feature changes in conjunction with the ANN can provide satisfactory results in predicting the structural changes of buildings with an accuracy of almost 92%.

Wetlands are a distinctive terrestrial ecosystem that benefits living things, including people, in various ways. Sustainable wetland ecosystem resources are needed to protect the global environment. Wetlands in China have undergone positive and negative changes in response to several factors, but studies documenting their long-term dynamicity have been few, particularly in Guangling County. This study examines the change of wetlands area based on remotely sensed data while exploring trends associated with climate variations and economic growth in Guangling County, China. Analysis of remotely sensed imagery, mainly in hilly and nonhomogeneous environments is problematic, largely as a result of interference and their high spectral non-homogeneity. We conducted experiments using five classical machine learning algorithms based on the Google Earth Engine (GEE) and obtained the greatest robustness and accuracy using a Support Vector Machine (SVM)—Radial Basis Function (RBF) kernel approach, with overall accuracy and kappa statistics ranging from 86% to 98.1% and from 0.789 to 0.960, respectively. Based on the SVM-RBF model’s outperformance of four other algorithms, we identified spatial distributions of wetland in the study area and associated change trends. We found that 45.71 km2 of wetland area was lost over the past 3.7 decades (January 1984–December 2020), or 81.82% of wetland area coverage. In this paper, we explore how factors such as county economic growth (GDP), humidity, and temperature variations are tightly linked with wetland change.

The variability of surface roughness may lead to relatively large dynamic of backscatter coefficient observed by the synthetic aperture radar (SAR), which complicates the soil moisture (SM) retrieval process based on active remote sensing. The effective roughness parameters are commonly used for parameterizing the soil scattering models, the values of which are often assumed to be constant during different study periods for the same site. This paper investigates the reasonableness of this hypothesis from the perspective of backscatter coefficient simulation and SM retrieval using high resolution SAR data. Three years of Sentinel-1A data from 2016 to 2018 were collected over a sparsely vegetated field within the REMEDHUS SM monitoring network. The advanced integral equation model (AIEM) and Dobson dielectric mixing model were combined for optimizing the effective roughness parameters, as well as simulating the backscatter coefficient and retrieving the SM. The effective roughness parameters were optimized at different temporal periods, such as 2016, 2017, 2018, 2016 + 2017, 2017 + 2018, and 2016 + 2017 + 2018, to analyze their temporal dynamics. It was found that: (1) the effective roughness parameters optimized at different temporal periods are very close to each other; (2) the simulated backscatter from AIEM is consistent with Sentinel-1A observation with root mean square errors (RMSEs) between 1.133 and 1.163 dB and correlation coefficient ® value equals to 0.616; (3) the seasonal dynamics ofin situ SM is well-captured by the retrieved SM with R values floating at 0.685 and RMSEs ranging from 0.049 to 0.052 m3/m3; and (4) inverse of the AIEM with the implementation of effective roughness parameters achieves better performance for SM retrieval than the change detection method. These findings demonstrate that the assumption on the constant effective roughness parameters during the study period of at least three years is reasonable.

The paper presents some basic tools for vibration signals with application in predictive maintenance of rotating machines. After an overview of the maintenance approach, the condition monitoring in predictive maintenance is discussed. Also, signal processing in vibration monitoring, making use of some basic tools as change detection, independent component analysis, time-frequency analysis, and energy distribution in time-frequency plane are presented. These techniques can be combined in a general approach, offering new possibilities for more robust detection of changes in vibration signals and assuring proactive actions in predictive maintenance.

Since a large amount of data can be obtained in the oil production process nowadays and the operation environment is increasingly complicated, it is necessary to research unsupervised and robust fault detection methods for improving safety. In this paper, an online Bayesian-based technique with a novel decision fusion algorithm is proposed for unsupervised fault detection in the pumping unit. First, a new strategy to detect the working condition of the pumping unit by dynamometer card as well as five process measured variables is proposed. To deal with high-dimension data and outliers in dynamometer card, a robust Douglas-Peucker algorithm is developed for obtaining compressed data. A chord ratio index evaluating deviation degree of observations is defined, which can be used for removing outliers during approximation. Two norms are introduced for choosing the threshold in the proposed Douglas-Peucker algorithm. Moreover, a Bayesian-based online change point detection model is attempted for detecting univariate faults in the pumping unit. A decision fusion method derived from Bayesian probability formula is proposed for fusing univariate fault detection results. At last, the power of the proposed method is evaluated by numerical simulations and a real oil production process.

This paper proposes a change detection algorithm based on Fully Convolutional Siamese Networks for optical aerial images, which is trained by using an improved Contrastive Loss-Focal Contrastive Loss (FCL). The proposed framework equipped with contrastive loss can extract features directly from image pairs and measure changes by using a distance metric. In other words, this method encourages reducing intra-class variance and enlarging inter-class difference, so that the binarized change map can be obtained by a simple threshold. In change detection task, a critical problem is how to overcome example imbalance (i.e. unchanged examples are much more than changed examples). To address this challenge, a novel focal contrastive loss is proposed to further improve the performance of the model. FCL can reduce the impact of example imbalance and make the model focus learning on hard examples. Extensive experiments demonstrate that the proposed approach is more abstract as well as robust. Compared with other baseline methods, the presented method achieves better results on SAZDA, TISZADOB, CDD, and WHU-CD data set. It achieves state-of-the-art performance in terms of the F1 measure.