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Current practices in traffic sign monitoring heavily rely on manual inspections, a method that is both time-consuming and prone to human error. This leads to inefficiencies in the management and maintenance of these critical roadside assets. The objective of this work is to overcome these limitations by proposing a method for automated change detection in traffic signs using low-density LiDAR data. The proposed solution integrates noise elimination, point cloud restructuring, and cross-scan KD-tree generation, followed by the application of unsupervised machine learning techniques for change identification. The effectiveness of this method was verified by testing across three different highways with varying point cloud resolutions. For robust testing, an algorithm was also designed to simulate a broad range of different damage scenarios in traffic signs of different types, sizes, and placements. Testing in different scenarios along almost 15 km of the road revealed impressive results with accuracy and F1 score metrics ranging from 92% to 100%. Moreover, the algorithm was also extremely efficient with an average runtime of just 115" per km of fully automated unattended processing. The change detection potential of the proposed algorithm extends beyond traffic signs, as it could be adapted for many highway elements, enhancing the efficiency of transportation asset management and highway maintenance programmes. The findings indicate that this approach not only fills a significant gap in the current traffic sign monitoring and asset management practice but also offers a promising, comprehensive solution towards automated, cost-effective, and precise monitoring and maintenance of traffic signs, thus addressing a major challenge in this area. [ABSTRACT FROM AUTHOR]

Change detection has always played a crucial role in observing the Earth's surface. Although visual Transformers (ViTs) have achieved excellent performance in change detection, they require high computational complexity. In addition, it does not explicitly address the differences caused by factors such as climate, lighting, and shooting angle in remote sensing (RS) image pairs. Therefore, we propose an efficient, lightweight Siamese network that considers multi-dimensional feature interaction. Firstly, we use a lightweight Transformer-based headless backbone network to extract feature information at each stage for bi-temporal images. To better capture the details and structure of images and eliminate the adverse effects of climate, lighting, and shooting angle, we design a multidimensional feature interaction method that uses spatial, channel, and amplitude dimension interaction methods after feature extraction operations at different stages. Furthermore, this approach achieves domain adaptation between bi-temporal domains to a certain extent while preserving the original semantic correspondence. Comprehensive experiments and extensive ablation studies on two common datasets, LEVIR-CD and S2Looking, have shown that our method achieves better performance with fewer parameters. [ABSTRACT FROM AUTHOR]

Change detection (CD) has a significant application in the remote sensing field. Because of the popularity of hyperspectral image (HSI) and the application of deep learning methods, hyperspectral image change detection (HSI-CD) techniques have been greatly developed. Among them, convolutional neural network (CNN) has garnered the greatest interest in HSI-CD due to their superior feature learning capabilities. However, current CNN-based algorithms have trouble capturing spectral similarity and long-range dependency owing to their intrinsic structural restrictions. Recently, transformers have been shown to extract global dependency from nature images in an extremely efficient way. But it has some difficulties in handling high-dimensional data, such as HSI. To address these issues, we propose an improved multi-scale and spectral-wise transformer (MS-SWT). The proposed MS-SWT is capable of capturing spectral similarity and long-range dependence between bands to enhance the efficiency of the HSI-CD task. Furthermore, to maximize the utilization of spatial information, we present a multi-scale feature fusion module (MFFM) to extract and fuse different dimensions of spatial features. More importantly, a locality self-attention (LSA) module is employed to alleviate the problem of smoothing the distribution of attention scores due to the large number of spectral embeddings. Moreover, we design a channel self-supervised loss function that can capture intrinsic information from the spectral channels to further strengthen the robustness of model training when the training samples are scarce. Lastly, comprehensive experiments present the high performance of our MS-SWT on four bitemporal HSI datasets and demonstrate the superiority of MS-SWT over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In recent decades, building change detection (BCD) algorithms have made significant progress by introducing deep learning (DL) for pixel-based change detection (CD). However, they still suffer the problem of low accuracy owing to insufficient feature extraction that is not discriminative enough. Hence, this results in poor semantic segmentation, including irrelevant change information and inconsistent boundaries. To tackle these problems, we present a novel DL-based method known as building change detection (BCD-Net) in an end-to-end manner. Our approach not only deals with the above-mentioned challenges but also significantly increases the level of accuracy. Moreover, our proposed method is inspired by full-scale U-Net3+ that uses an encoder and decoder for semantic segmentation. We modified the U-Net3+ by adding subpixel convolution layers instead of upsampling layers. BCD-Net is applied in three main parts: (1) data preparation, (2) model training/optimization of model parameters, and (3) building change detection based on tuned models. To evaluate the performance of BCD-Net, the benchmark unmanned aerial vehicle (UAV), and satellite imagery were employed. The BCD-Net obtained an accuracy of 98.80% accuracy with the known WHU building dataset and 94.48% on the EGY-BCD dataset. The obtained results demonstrated that the proposed BCD-Net outperformed the rest of the techniques and achieved competitive accuracy, a low rate of miss-detection, and a false alarm rate. [ABSTRACT FROM AUTHOR]

Deep learning has revolutionized change detection (CD) in remote sensing tasks. However, high-resolution CD based on deep learning faces challenges in handling semantic complexity in bi-temporal images, especially across varying weather and lighting conditions. While CNN-based approaches optimize network structures to enhance contextual information, recent attention-based methods increase computational demands. This study introduces the Channel-Spatial Attention Network (CSANet) to extract multi-scale and semantic information from images. Evaluated on LEVIR-CD and DSIFN-CD datasets, CSANet outperforms several state-of-the-art methods, demonstrating its potential for advanced change detection in high-resolution remote sensing. [ABSTRACT FROM AUTHOR]

To improve the accuracy of detecting changes in remote sensing time series, an improved algorithm based on the combination of the antileakage least-squares spectral analysis (ALLSSA) algorithm and detecting breakpoints and estimating segments in trends (DBEST) algorithm is proposed and applied. The method uses the ALLSSA algorithm to decompose the time series and identify the trend components in the time series. Then, the trend segmentation mechanism of the DBEST algorithm is used to detect the changes in the trend component. In this paper, the improved algorithm is evaluated using a simulated time series data set, a time series data set with multiple change points, and data set based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing time series. The results demonstrate that the average detection accuracies of the improved algorithm and DBEST algorithm are 98.4% and 85.2%, respectively, for the simulated time series data set. For the time series data set with multiple change points, the average root mean square errors (RMSEs) of the trend data for the improved and DBEST algorithms are 0.0386 and 0.0331, respectively. The mean normalized residual norms (MNRNs) of the improved and DBEST algorithms are 0.0252 and 0.0351, respectively. Finally, the improved algorithm, DBEST algorithm, and breaks for additive season and trend (BFAST) algorithm are applied to MODIS NDVI data, and their performance with remote sensing data is compared. The improved algorithm has higher detection accuracy and a smaller MNRN, indicating that more information is included in the trend and seasonal components. Therefore, the proposed method is useful for analysing trends in remote sensing time series data. [ABSTRACT FROM AUTHOR]

Most existing remote sensing disaster assessment methods rely on convolutional neural networks (CNNs). Although CNNs can extract effective semantic features, determining global spatial relationships remains limited due to the locality of convolutional operations. The recently developed transformer-based method can extract global information from images effectively by encoding image tokens. However, its consumption of computational resources varies markedly with increasing image resolution. In this paper, we propose a novel transformer-based neural network for disaster assessment problems. The network uses a multihead neighbourhood attention (MNA) transformer as the base layer of the encoder to achieve more efficient self-attention computation. In addition, the bitemporal feature fusion module (BFFM) performs differential enhancement and injects the change information to the decoder via skip connections. The multiscale tokenizer generates multiscale image tokens to mitigate the loss of detail during encoding. Experimental results on three datasets show that the proposed method outperforms existing methods. [ABSTRACT FROM AUTHOR]

Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster images reduce the model's performance because of the presence of destroyed buildings. In this paper, we propose a siamese model, called SiamixFormer, which uses pre- and post-disaster images as input. Our model has two encoders and has a hierarchical transformer architecture. The output of each stage in both encoders is given to a temporal transformer for feature fusion in a way that query is generated from pre-disaster images and (key, value) is generated from post-disaster images. To this end, temporal features are also considered in feature fusion. Another advantage of using temporal transformers in feature fusion is that they can better maintain large receptive fields generated by transformer encoders compared with CNNs. Finally, the output of the temporal transformer is given to a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD, and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for change detection and could outperform the state-of-the-art. [ABSTRACT FROM AUTHOR]

Seasonal changes usually exist and cause false alarms in the bi-temporal change detection from high-resolution remote sensing images. It is difficult to remove these false alarms only using bi-temporal images for traditional change detection methods. A change detection method is proposed to remove seasonal false alarms in bi-temporal change detection by introducing time series information of medium-resolution remote sensing images. First, the mid-resolution time series results are mapped to the ground objects obtained by multiscale segmentation of high-resolution remote sensing images. Second, set the thresholds for the proportion of each category of pixels in the object to obtain high-resolution time series results. Finally, the high-resolution change detection results are optimized by the improved high-resolution time series results. Experimental results show that this method can optimize the results of high-resolution change detection, and the accuracy of this method was improved by at least 0.23 than that of traditional change detection by reducing seasonal errors. The proposed method was an effective change detection approach for high-resolution images to reduce detection errors due to seasonal differences. [ABSTRACT FROM AUTHOR]

The technology of change detection from a pair of registered satellite remote sensing images is of great importance for the study of landform changes and thus has attracted much attention. Recently, convolutional neural networks have been favoured in many applications due to their excellent feature extraction performance. However, for high-resolution satellite remote sensing images, previous methods are difficult to obtain finer details of change detection. There are many complex scenery and objects in satellite remote sensing images, which leads to the decline of the performance of the network model. In order to better solve the above problems, we propose a graph convolution-based multi-scale network with a channel attention mechanism. Specifically, we first project the initial features at different levels as vertex features, and there is a mutual relationship between the vertices. After that, we learn the correlations between these vertices through a graph reasoning. We then reproject the newly obtained vertex features to the original pixel space to obtain fine feature maps of the same size and fuse fine features of different scales through the attention fusion module. Finally, we realize the change detection of the input bi-temporal image through the feature difference module of the decoder. We separately conduct extensive experiments on three different high-resolution datasets to demonstrate that our method is effective. [ABSTRACT FROM AUTHOR]

After a natural disaster, it is critical to perform a damage assessment of affected buildings rapidly and accurately to meet emergency response requirements. With the development of deep learning (DL) technology, the change detection (CD) method based on fully convolutional networks has been used for disaster analysis and assessment. However, the main limitations of most present methods are their large number of parameters, high computational complexity, and limited deployment equipment resources, which affect the real-time performance of disaster analysis. Therefore, to make a trade-off in the relationship between the accuracy and efficiency of damaged building CD, this research proposes an efficient method based on a lightweight residual block (LRB) in bitemporal high-resolution remote sensing (RS) images (LRBNet). LRBNet is an encoder – decoder structure with the Siamese network and UNet++ as the backbone. It uses our LRB in the whole network to replace the original convolution unit, which can extract features to reduce computational complexity. The LRB consists of the lightweight compression module (LCM) and efficient channel attention (ECA) to learn the essential damage information in the feature map and improve the feature expression ability efficiently. Moreover, the multilevel damage feature aggregation attention module is the last process of the network and performs fine-grained aggregation of the multilevel disaster change features and emphasizes global semantic and spatial information to maximize the representative damage change characteristics. Furthermore, we conducted a series of experiments on the global-scale disaster xBD dataset (including 19 different disaster events). Our method not only accurately evaluated the level of disaster but also had favourable detection efficiency, achieving a good trade-off between recognition accuracy and computational resources. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image change detection is a key technique for such essential applications as flood disaster assessment and forest fire detection. In SAR image change detection, clustering algorithm is the most applied methods, but clustering algorithm only considers the grey features, therefore it is more susceptible to speckle noise. Deep learning model is difficult to be trained by supervised learning because of lack of labels. To alleviate the above-mentioned challenges of SAR image change detection, a parallel dual-branch SAR image change detection network based on clustering and segmentation (Clustering-Segmentation Network) is proposed in this paper. In the clustering branch, the clustering-based change detection results were obtained by fuzzy c-means (FCM) clustering. In the segmentation branch, the Graph-Based Image Segmentation algorithm was used for pre-segmentation. These results were used as labels of the neural network for training for the segmentation-based change detection. After the fusion of these dual-branch, a double sparse dictionary (DSD) discrimination algorithm is proposed to extract the neighbourhood features for the final discrimination, and obtain the final results. By fusing the dual-branch results, the influence of speckle noise on change detection can be suppressed while maintaining a high accuracy. We show that the Clustering-Segmentation Network exhibited better results compared with existing algorithms on several datasets. The accuracy and kappa coefficients are improved by 0.83% and 3.45% respectively, thereby proving the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Detecting the field maturity moment for maize (Zea mays L.) crop represents a relevant point to estimate its optimal harvest time. Knowing the optimal harvest time (defined by grain moisture content) at the end of the crop season is a major concern for maize farmers, as it could lead to substantial economic losses if not harvested on time. For this crop, optimal harvest time usually occurs 3–4 weeks after field maturity, depending on weather conditions. Therefore, this study focused on the interferometric coherence time-series analysis at the end of the maize crop season, to indirectly estimate the field maturity. For such purpose, a coherence object-based change detection method using Sentinel-1 SAR images was developed aiming to estimate the potential field maturity time. These estimations were assessed using an independent data set of field maturity dates obtained through field inspection and crop growth modelling. The technique was tested over 52 fields in the northwest region of Kansas, United States, with a detection rate of 80%, and a field maturity estimation error of 10 days (assessed with the root mean square error). The proposed method constitutes a promising approach to estimating the maize field maturity in near-real time, determining the field harvest readiness, and developing a decision support tool to assist farmers in prioritizing the allocation of fields at harvest time. [ABSTRACT FROM AUTHOR]

Change detection (CD) in high-resolution remote sensing images (RSIs) can be regarded as a binary visual recognition problem. Metric learning (ML) is a reliable method to determine pixel class attributes based on discriminative distance function between learnable image features. However, most related works learn discriminative distance functions in single-scale feature pairs, which suffer from slow convergence and poor local optima, partially due to that the loss function employs only large-scale feature samples while not interacting with the other scale features. Furthermore, more effective features are a prerequisite for improving the performance of ML-based CD. Hence, we propose a novel hierarchical metric learning network (HMLNet) with dual attention for CD in RSIs, where the key point is that hierarchical metric learning is performed in an ensemble manner to improve detection accuracy and accelerate model convergence. Specifically, based on the features extracted from the encoder-decoder backbone, we construct a feature pyramid to handle the complex details of objects at various scales in RSIs, and then perform metric learning between the paired pyramid features at the same scale. In addition, the dual-attention module is proposed to enhance the internal consistency of changed objects and effectively obtain more detailed information by acting on multi-scale pyramid features. Extensive experiments on the two public RSIs CD datasets, and the results demonstrate that the proposed HMLNet can accurately locate changed objects, which consistently outperforms the state-of-the-art CD competitors. [ABSTRACT FROM AUTHOR]

Change detection in remote sensing images has an important impact in various application fields. In recent years, great progress has been made in the change detection methods of multiple types of ground objects, but there are still limited recognition capabilities of the extracted features, resulting in unclear boundaries, and the accuracy rate needs to be improved. To address these issues, we use a high-resolution network (HRNet) to generate high-resolution representations and add new data augmentation methods to improve its accuracy. Secondly, we introduce the model of Transformer structure —— CSWin and HRNet to fuse to improve the performance and effect of the model. In order to enhance the model's ability to perceive ground objects at different scales, a feature fusion network suitable for multi-class semantic segmentation is designed, named A-FPN. This feature fusion network is introduced between the CSWin backbone network and the semantic segmentation network. The experimental results show that the fusion method greatly improves the accuracy to 89.31% on the SECOND dataset, significantly reduces false detections, and recognizes the edges of objects more clearly. And achieved good results in the three evaluation indicators of precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

The polarimetric synthetic aperture radar (PolSAR) system is one of the most successful tools for solving remote-sensing issues. However, the images produced by this system – which uses coherent illumination – are corrupted by the multidimensional speckle noise that gives PolSAR data a multiplicative character. Therefore, tailored processing of PolSAR images is required, e.g. improved hypothesis testing and change detectors. In this work, we propose a novel bivariate distribution – called G I 0 McKay bivariate ( G I 0 MB) - to describe a joint distribution arising from two components of the total scattering power image (SPAN). We derive closed-form expressions for the Kullback–Leibler and Rényi divergences for the G I 0 MB law. We provide new two-sample divergence-based hypothesis tests and evaluate their performance using Monte Carlo experiments. Finally, we apply the new tests to real PolSAR images to evaluate the changes caused by urbanization processes in the Los Angeles and California regions. Results show that our proposals to detect changes in PolSAR images outperform the one based on the likelihood ratio. [ABSTRACT FROM AUTHOR]

Generalized likelihood ratio test (GLRT) is an efficient method to generate difference image (DI) for change detection (CD) using synthetic aperture radar (SAR) images. GLRT is usually applied with a fixed-size moving window to the neighbourhood regions in multitemporal SAR images. The fixed window may be however not optimal for all the pixels under test. To solve disadvantages of the GLRT method, an adaptive circular window is proposed in this work. While the adaptive square window and the best fixed square window achieve an average Kappa coefficient of 83.55% and 82.12%, respectively, the adaptive circular window improves the average Kappa coefficient by 83.65% in six datasets. As another difficulty, speckle noise reduces quality of DI in SAR change detection. Three steps are considered to minimize effects of the speckle noise: (1) a two-stage morphological filter is suggested to reduce the speckle noise; (2) to generate DI, the adaptive circular window for generalized likelihood ratio test (ACWGLRT) is proposed that reduces influence of the speckle noise while preserves the edge details of multitemporal images; and (3) spatial fuzzy c-means (SFCM) is used to reduce effects of the residual speckle-noise during DI classification. The experimental results show superior performance of the proposed change detection method with respect to several competitors. The proposed method has the best Kappa coefficient and percentage correct classification (PCC) in four datasets of Ottawa, San Francisco, Farmland C, and Inland water. [ABSTRACT FROM AUTHOR]

With the gradual adjustment of urban expansion and discontinuation of non-essential functions in Beijing, many decades-old buildings have been demolished. Thus, construction and demolition waste (CDW) has become the focus of urban and dust pollution management. However, CDW piles are volatile and present irregular boundaries. Therefore, it is essential to map CDW regions in a timely and accurate manner to achieve urban development while protecting the environment. To address this issue, we proposed a method of CDW identification based on change detection and deep learning. First, ZY-3 multispectral images from 2016 and 2019 and their difference images were used for initial sample preparation. We expanded the samples using the post-classification comparison method of change detection, resulting in a 25.4% increase in the valid sample set. The expanded samples were then used as an input to the DeepLabV3+ for training. Thereafter, combined with the change information from the digital elevation model, specific forms, such as demolition remains, landfill CDW, and large-scale dump, were extracted using spatial analysis methods. The overall accuracy of CDW recognition was 91.67%, with a Kappa coefficient of 0.8642. In addition, we calculated the accuracy indices using only the initial samples, obtaining a mean Intersection-over-Union value that was 0.086 lower than that obtained using the expanded sample set. Similar results were obtained in PSPNet and UNet. This suggests that change detection is useful in improving the accuracy of the deep learning models. This study is the first to identify three existing forms of CDW and can effectively address the misclassification between CDW and bare land to identify CDW efficiently. [ABSTRACT FROM AUTHOR]

A new change-detection method for remote sensing images based on a conditional random field (CRF) model is proposed in this paper. The method artfully uses memberships of Fuzzy C-means as unary potentials in the fully connected CRF (FCCRF) model without training parameters, and pairwise potentials of the CRF model are defined by a linear combination of Gaussian kernels, with which a highly efficient approximate inference algorithm can be used. The proposed FCCRF model is expressed on the complete set of pixels in both the observed multitemporal images, which can incorporate long range contextual information of remote-sensing images and enable greatly refined change-detection results. Experimental results demonstrate that the proposed approach leads to more accurate pixel-level change-detection performance and is more robust against noise than traditional algorithms. [ABSTRACT FROM AUTHOR]