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Change detection (CD), as one of the central problems in Earth observation, has attracted a lot of research interest over recent decades. Due to the rapid development of satellite sensors in recent years, we have witnessed an enrichment of the CD source data with the availability of very-high-resolution (VHR) multispectral imagery, which provides abundant change clues. However, precisely locating real changed areas still remains a challenge. In this article, we propose an end-to-end superpixel-enhanced CD network (ESCNet) for VHR images, which combines differentiable superpixel segmentation and a deep convolutional neural network (DCNN). Two weight-sharing superpixel sampling networks (SSNs) are tailored for the feature extraction and superpixel segmentation of bitemporal image pairs. A UNet-based Siamese neural network is then employed to mine the different information. The superpixels are then leveraged to reduce the latent noise in the pixel-level feature maps while preserving the edges, where a novel superpixelation module is used to serve this purpose. Furthermore, to compensate for the dependence on the number of superpixels, we propose an innovative adaptive superpixel merging (ASM) module, which has a concise form and is fully differentiable. A pixel-level refinement module making use of the multilevel decoded features is also appended to the end of the framework. Experiments on two public datasets confirmed the superiority of ESCNet compared to the traditional and state-of-the-art (SOTA) deep learning-based CD (DLCD) methods.

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.

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.

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.

Remote sensing (RS) image change detection (CD) is a basic application in the military and people's livelihood. The imaging results of multitemporal and multispectral RS images have interference effects such as spectral information difference, imaging light difference, sensor noise, so the accuracy and robustness of traditional CD algorithms are restricted. In this study, we propose an unsupervised fully connected multivariate alteration detection (UFMAD) algorithm. First, UFMAD is used to extract the deep difference feature of normalized multitemporal RS images. The weighted map was obtained by using the chi-square distance of the deep difference feature and weighted with the input data. Then, the UFMAD is iteratively optimized under the three proposed constraints of variance maximization, similarity maximization, and uncorrelation of deep features. Finally, the deep feature was clustered to get the change results. By analyzing the experimental results of six advanced CD algorithms on three sets of data, the highest relative detection accuracy of UFMAD has increased by 7.0% . It is verified that the algorithm proposed in this thesis has obvious performance advantages.

Change detection is an important task in remote sensing (RS) image analysis. It is widely used in natural disaster monitoring and assessment, land resource planning, and other fields. As a pixel-to-pixel prediction task, change detection is sensitive about the utilization of the original position information. Recent change detection methods always focus on the extraction of deep change semantic feature, but ignore the importance of shallow-layer information containing high-resolution and fine-grained features, this often leads to the uncertainty of the pixels at the edge of the changed target and the determination miss of small targets. In this letter, we propose a densely connected siamese network for change detection, namely SNUNet-CD (the combination of Siamese network and NestedUNet). SNUNet-CD alleviates the loss of localization information in the deep layers of neural network through compact information transmission between encoder and decoder, and between decoder and decoder. In addition, Ensemble Channel Attention Module (ECAM) is proposed for deep supervision. Through ECAM, the most representative features of different semantic levels can be refined and used for the final classification. Experimental results show that our method improves greatly on many evaluation criteria and has a better tradeoff between accuracy and calculation amount than other state-of-the-art (SOTA) change detection methods.

Change detection is a key technology in the field of polarimetric synthetic aperture radar (PolSAR) image processing. The current research on the change detection mainly focuses on studying PolSAR images with the same angle or small angle difference, and the angle problem is not considered. However, when the angle difference occurs, especially a large angle difference, some pixels might be falsely detected because the angle difference can affect the polarimetric characteristics. In this letter, we propose a multilevel information fusion-based (MIFB) method, which is suitable for extracting change information from PolSAR images with angle difference. In particular, the proposed method first adopts data resolution correction, then applies an improved feature-based registration algorithm, and finally, incorporates weighted graph theory with the superpixel segmentation algorithm to extract and merge pixel-based and object-based change areas to eliminate false alarms. Experimental results for multitemporal and multiangle PolSAR images reveal that the MIFB method can effectively eliminate false detection caused by angle differences and improve the detection accuracy.

Change detection (CD) is one of the most important topics in the field of remote sensing. In this letter, we propose an effective satellite images CD network named DifUnet++. As the presentation of explicit difference is more conducive to extract change features, we design a differential pyramid of two input images as the input of Unet++. Considering the scale diversity of changed regions in remote sensing images, a multiply side-outs fusion strategy is adopted to predict the detection results of different scales. Furthermore, a learning upsampling method is utilized to refine the details of CD. The proposed architecture is evaluated on two public satellite image CD data sets. The experimental results show that our method performs much better than state-of-the-art methods.

Agricultural land abandonment and retirement are important and lead to different types of land use and cover change. Generally, abandonment and retirement are caused by different social and environmental factors and result in different ecological and economic benefits and costs. Faced with the complexity of agricultural land change over time, this study aims to develop a new framework to distinguish between agricultural land abandonment and retirement and detect the extent and exact year of abandonment and retirement using Google Earth Engine. We tested our approach for three typical regions in the northern China crop-pasture band, where agricultural land abandonment and retirement are widespread. First, based on the spectral features obtained from Landsat images, annual land-cover maps were obtained with sample migration and random forest from 1998 to 2019 (0.87 overall accuracy). Second, a temporal consistency check method was proposed to further improve the classification performance (0.92 overall accuracy). Third, a trajectory-based change detection approach was developed to identify abandonment and retirement (F1 score for abandonment: 0.74, retirement: 0.83). Our results indicate that the spatiotemporal patterns of abandonment and retirement in the study area greatly differed. Overlapping of the topography and climate data showed that agricultural land with steep slopes (> 10°) was more likely to be retired and that abandonment was more likely to occur in areas with less precipitation. Overall, the methods used herein are robust for agricultural land abandonment and retirement monitoring and may be extended to other land-cover change studies.

Change detection (CD) aims to identify surface changes from bitemporal images. In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD. However, CD results can be easily affected by external factors, including illumination, noise, and scale, which leads to pseudo-changes and noise in the detection map. To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article. A metric module is employed in DSAMNet to learn change maps by means of deep metric learning, in which convolutional block attention modules (CBAM) are integrated to provide more discriminative features. As an auxiliary, a DS module is introduced to enhance the feature extractor's learning ability and generate more useful features. Moreover, another challenge encountered by data-driven DL algorithms is posed by the limitations in change detection datasets (CDDs). Therefore, we create a CD dataset, Sun Yat-Sen University (SYSU)-CD, for bitemporal image CD, which contains a total of 20,000 aerial image pairs of size 256 x 256. Experiments are conducted on both the CDD and the SYSU-CD dataset. Compared to other state-of-the-art methods, our network achieves the highest accuracy on both datasets, with an F1 of 93.69% on the CDD dataset and 78.18% on the SYSU-CD dataset.

Synthetic aperture radar (SAR) image change detection (CD) is an important application in the field of remote sensing. Due to the lack of labeled data especially in the pixelwise task, it is urgent to develop unsupervised techniques to effectively detect changes. In this article, we propose a novel unsupervised representation learning framework for CD in SAR images, called multiscale self-attention (SA) deep clustering based on octave convolution. The main motivation is that a convolutional neural network (CNN) has the ability to extract significant feature hidden in input images, but it relies heavily on annotated data. Clustering is typically free from supervision; however, SAR images always suffer from speckle noise, which is unfriendly for clustering. Thus, we integrate unsupervised clustering with CNN to learn clustering-friendly feature representations. In the unified framework, CNN feature learning and clustering can be optimized end-to-end without supervision. To better suppress speckle noise and boost the joint optimization for distinguishing changes and unchanges, we use the K-means++ algorithm that is robust to noise as the clustering algorithm. In the meanwhile, we introduce the octave convolution and SA mechanism into the network to fully mine important spatial structure information for enhancing noise resistance of the network. Moreover, multiscale fusion modules are proposed to fuse multiscale input into a complementary feature representation that contains more context and semantic information around each pixel so that it refines the difference feature extraction while reducing speckle noise. Experiments on challenging SAR data sets demonstrate the effectiveness and potential of the proposed model compared with the current state-of-the-art algorithms.

Currently, most of the supervised change detection approaches require a training data set that contains samples from both the changed and the unchanged data. However, under certain condition, such as natural disaster and military attack, the changed data samples are very few or even not available but the unchanged data are abundant. In this letter, we develop a generative adversarial networks (GANs)-based one-class classification (OCC) technique for time series remote-sensing image change detection. The proposed method is only trained with the unchanged data instead of both the changed and unchanged data. To achieve this purpose, first, spatial-spectral features are extracted from the time series remote-sensing images. Second, a GAN model is trained to detect the changes only with the extracted features of unchanged data. Remarkably, to offset the outlier errors caused by the incomplete supervision information provided by unchanged data alone, changed data, instead of unchanged data, are generated to improve power of discriminator. Finally, testing data are classified by the trained discriminator of GAN to produce a binary change map. Experimental results obtained on two optical time series remote-sensing data sets confirmed the effectiveness of our proposed method.

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

For cumulative change detection using time-series polarimetric synthetic aperture radar (SAR) (PolSAR) data, pairwise comparisons using bi-temporal data change detection are often performed over each successive pair of images in the entire time series. In this way, due to the impact of the quality of the difference image (DI) of bitemporal data and the classification accuracy of the DI into the change and nonchange classes, there is a great risk of many false alarms or omissions caused by gradually occurred changes. Therefore, this letter proposes a symmetric similarity matrix based on a likelihood ratio test (LRT), which groups the similarity between any two-time point data in time series. Then, Shannon entropy is applied to the similarity matrix to calculate the DI of the entire time-series data. Finally, an image segmentation method based on Markov random field (MRF) is used to classify the calculated DI into the change and nonchange classes. The experiments using the multitemporal fully polarimetric Radarsat-2 data acquired at three different time points prove the effectiveness of the proposed method compared with some existing methods.

Change detection is a research hotspot in the remote sensing field. In this letter, an unsupervised change detection method was proposed by optimizing two critical steps, i.e., the generation and analysis of difference image. First, the difference vectors of features are calculated using the simple differencing method. Some changed and unchanged pixels are generated by the majority voting on the results produced by clustering the difference vectors and then are used for the weight calculation of difference vectors. The weights are calculated by means of F-Score and considered in the weighted change vector analysis to produce a discriminative difference image. Finally, the change map is obtained by the improved Markov random field which takes the difference in the neighborhood pixel values into account. Experimental results on three data sets demonstrated that the proposed method outperformed six unsupervised change detection methods in terms of overall accuracy.

Change detection, as an essential part of synthetic aperture radar (SAR) automatic target recognition (ATR) systems, remains a challenging problem. The lack of labeled data seriously impedes the development of deep learning-based methods in SAR applications. In this letter, we propose a patch-based semisupervised method to detect changed pixels from limited training data in the field of SAR. The complete approach includes the unsupervised pretraining stage and the iterative discrimination stage. The states of pixels are determined by comparing the corresponding image patches. First, to reduce the impact of insufficient data, we train a trapezium U-Net-like structure to extract representative and generalized features by unsupervised training on all pixels. Feature maps contain detailed and semantic information due to repetitive feature fusions. A designed feature activation module is utilized to recalibrate fused features. Finally, the discrimination is finished by a simple classification convolutional neural network (CNN) following a two-step training strategy. Ablation studies indicate the function provided by the proposed modifications. Experiments on real SAR images demonstrate that the proposed method can improve the detection accuracy by more than 1.2% compared with other state-of-the-art deep learning-based methods.

Landslide inventory mapping (LIM) plays an important role in landslide susceptibility analysis. Many LIM approaches based on change detection techniques have been proposed, but with various drawbacks. For example, existing approaches have limited capability to capture the objects of varying shapes/sizes present in an area impacted by landslide. Many existing approaches are supervised and require parameter tuning. Moreover, some methods are prone to salt-and-pepper noise. To overcome these limitations, in this letter, an algorithm based on automatic adaptive region extension using very-high-resolution remote sensing images is developed. First, a simple yet effective k-means clustering method is used to generate training samples for landslide and nonlandslide classes, which refer to changed and unchanged areas, respectively. Second, an automatic adaptive region extension algorithm is developed and applied to each pixel of the postevent image, and the label of an extended region around a pixel is determined by the nearest distance between the central pixel and the changed or unchanged samples. Finally, the labels of a pixel are recorded because a pixel in different adaptive regions may be reassigned dissimilar labels, and the final label of the pixel is consistent with its maximum assigned label. To verify the performance of the proposed approach, we conducted experiments on two different landslide sites with VHR remote sensing images in Lantau Island, Hong Kong, China. Experimental results clearly demonstrate that the proposed approach has several advantages in improving the performance of LIM with VHR remote sensing images.

Change detection (CD) aims to identify the differences between multitemporal images acquired over the same geographical area at different times. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention of researchers. Multitemporal images tend to have different resolutions as they are usually captured at different times with different sensor properties. It is difficult to directly obtain one pixelwise change map for two images with different resolutions, so current methods usually resize multitemporal images to a unified size. However, resizing operations change the original information of pixels, which limits the final CD performance. This article aims to detect changes from multitemporal images in the originally different resolutions without resizing operations. To achieve this, a cross-resolution difference learning method is proposed. Specifically, two cross-resolution pixelwise difference maps are generated for the two different resolution images and fused to produce the final change map. First, the two input images are segmented into individual homogeneous regions separately due to different resolutions. Second, each pixelwise difference map is produced according to two measure distances, the mutual information distance and the deep feature distance, between image regions in which the pixel lies. Third, the final binary change map is generated by fusing and binarizing the two cross-resolution difference maps. Extensive experiments on four datasets demonstrate the effectiveness of the proposed method for detecting changes from different resolution images.

This article presents a probabilistic model based on a bipartite convolutional architecture for unsupervised change detection. We aim to develop a robust change detection method that can adapt to different types of data and scenarios for multitemporal coregistered remote sensing images of the same spatial resolution. On the premise of coregistration, unsupervised change detection usually suffers from the distinct appearances (different intensities or data structures) of the same object in multitemporal images, such as images obtained in different climatic conditions (season, illumination, and so on), and by different and even heterogeneous sensors. Since change detection in heterogeneous images can also adapt to other scenarios, many methods have been proposed recently focusing on such data, but most of them are limited by the need for labeled data or by specific assumptions. With the excellent and flexible feature learning capability of neural networks, we model the change detection into a Gibbs probabilistic model based on a bipartite neural network. The model is driven by an energy function defined as the squared feature distance, which is the core of change detection. Via optimizing the model, the difference degree of each pixel is automatically obtained for further identification. The probabilistic model learns to capture the distribution in an unsupervised way. Therefore, the proposed method can adapt to various scenarios without being trained by labeled data. Experiments on different types of data and scenarios demonstrate the superiority of the proposed method.

In recent years, with the development of high-resolution remote sensing (RS) images and deep learning technology, high-quality source data and state-of-the-art methods have become increasingly available, and great progress has been made in change detection (CD) in RS fields. However, existing methods still suffer from weak network feature representation and poor CD performance. To address these problems, we propose a novel CD network, called dual-pathway CD network (DP-CD-Net), which can help enhance feature representation and achieve a more accurate difference map. The proposed method contains a dual-pathway feature difference network (FDN), an adaptive fusion module (AFM), and an auxiliary supervision strategy. Dual-pathway FDNs can effectively enhance feature representation by supplementing the detailed information from the encoding layers. Then, we use the AFM method to fuse the difference maps. To solve the problem of training difficulty, we use the auxiliary supervision strategy to improve the performance of DP-CD-Net. We conduct extensive experiments to validate the performance of the proposed method on the LEVIR-CD dataset. The results demonstrate that the proposed method performs better than existing methods.

In this letter, we propose a new unsupervised change detection method based on low-rank prior for multispectral images. It is assumed that the changed and unchanged pixels are from different subspaces due to different appearance and statistical properties. So, low-rank representation (LRR) is employed to find informative pixels from the superpixels of the difference image (DI). Besides, taking the sparsity of changed pixels in the observed scenes into consideration, the selection rule is designed to distinguish these pixels. Then, principal component analysis (PCA) is used for the training of changed and unchanged dictionaries from these pixels. Finally, the change map is estimated by comparing the reconstruction error of each pixel in DI on changed and unchanged dictionaries. By LRR, more representative pixels are found for subsequent dictionary learning, which can efficiently improve the performance of the proposed method. Experiments on multitemporal images from the Landsat satellite demonstrate the effectiveness of the proposed method.

Today, remote sensing (RS) data are already regarded as ``big data.'' Developments in computer science have made it possible to explore the potential treasure within remote sensing big data, but only limited remote sensing research has made use of big data technology due to gaps in techniques between big data and remote sensing. In this research, we analyzed the full processing flow of remote sensing big data from the perspective of both computer science and remote sensing science and proposed a modular framework. Computation ready data (CRD), a dynamic data type for computation based on analysis ready data (ARD), is proposed to connect the two main modules of the framework, the data module and computation module. Compared with existing research, the proposed framework classifies and abstracts the key technical and research points of the processing of remote sensing big data as replaceable modules and bridges them through an open organization. Subsequently, we built a prototype platform with open-source technologies and carried out three experiments to validate the feasibility and advantages of the framework, namely normalized difference vegetation index (NDVI) production, water body change detection, and land use classification. Results indicate that this framework can greatly reduce experimental costs for remote sensing researchers. While the proposed framework has proven flexible and practical, further research is needed for the technical implementation of certain modules to achieve the original intention of the framework.

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.

With the ability to locate subtle trace objects in the large-scale region, coherent change detection (CCD) has been vital research for a synthetic aperture radar (SAR) system. Finding the difference between repeat-pass repeat-geometry SAR image pair and extracting impressive trace pixels from difference image, the SAR CCD methods consist of a difference generation module and a difference analysis module. The previous CCD methods mainly pay attention to devising a sophisticated working system or an appropriate statistic model to generalize a well difference image. In this article, we introduce the deep learning method into the CCD algorithm and propose a novel trace detection paradigm, which works by hierarchically fusing the unsupervised coherent statistics model and supervised deep learning model. To be specific, the complex reflectance change detection estimator is introduced to generate a difference image and reduce the false alarm in the low clutter-to-noise region. Since the low correlation in a difference image caused by the natural factors severely affects the detection performance, the multiple statistics based on intensity summation and intensity difference are, respectively, proposed to extract water region and vegetation region and suppress the corresponding false alarm. Then the construction of the coarse-to-fine image makes use of land cover information and trace features while the compressed Unet improves the utilization efficiency of trace samples. Meanwhile, the inductive transfer learning based on unsupervised pretraining and few labeled trace samples helps to train a well detection model. Experiments on measured SAR data demonstrate the effectiveness of proposed methods.

Landslide inventory mapping (LIM) on the basis of change detection techniques has potential significance for landslide disaster analysis. In this letter, a novel LIM approach based on the adaptive histogram-mean distance (AHMD) is proposed, which adaptively considers spatial contextual information of different landslide regions to improve the detection performance. First, to adapt the shape, size, and distribution of various landslides, an adaptive region around a pixel is extracted by a novel adaptive region extension algorithm without parameter setting. Second, the pixels within the adaptive region are taken to construct the spectral frequency histograms, and then, the adaptive histogram mean (AHM) is developed as the feature of a histogram. Third, the AHMD is defined based on the bin-to-bin (B2B) distance to measure change magnitude between the pairwise AHMs. Finally, LIM can be obtained by a supervised threshold method called double-window flexible pace search (DFPS). Experimental results tested on two real datasets with a very high spatial resolution (VHR) demonstrate the outperformance of the proposed AHMD approach with seven comparative methods.

Relative radiometric normalization (RRN) is widely used for radiometric calibration of bitemporal multispectral images prior to any temporal analysis such as change detection. However, standard RRN methods are not robust against anomalous (or changed) pixels, which warp the calibration and decrease the spectral similarity of processed images. This letter proposes a novel random sample consensus-based RRN method, which only uses small pixel subsets to implement the linear mapping relationship for RRN, and does not require the calibration of its parameters. The experimental results show that the proposed method performs favorably against the widely used RRN methods in all metrics considered in this letter.

Deep learning-based unsupervised change detection (CD) methods compare a prechange and a postchange image in deep feature space and require precise knowledge of the event date for selecting proper pre-/post-change images. However, in many applications changes may occur gradually over a span of time making pre-/post-dates difficult to establish or prior knowledge of event date is unknown. On the other hand, deep learning-based time-series analysis methods are generally supervised. Considering such scenarios, we propose a novel unsupervised deep learning-based method to detect changes in an image time-series. The method does not make any assumption on the date of the occurrence of the change event. It treats CD as an anomaly detection problem by exploiting multilayer long short term memory (LSTM) network to learn a representation of the time series. The proposed method ingests a shuffled time series and uses an encoder-decoder LSTM model to rearrange the input sequence in correct order. While the model fails to rearrange the changed pixels, unchanged data can be rearranged in the correct order. This enables the identification of the changed pixels. To show the effectiveness of the proposed method, we tested it on two multitemporal Sentinel-1 data sets over Brumadinho, Brazil, and Bhavanisagar, India.

Anomaly detection (AD) is a field of intense research in remote sensing (RS) image processing. Identifying low probability events in RS images is a challenging problem given the high dimensionality of the data, especially when no (or little) information about the anomaly is available a priori. While a plenty of methods are available, the vast majority of them do not scale well to large datasets and require the choice of some (very often critical) hyperparameters. Therefore, unsupervised and computationally efficient detection methods become strictly necessary, especially now with the data deluge problem. In this article, we propose an unsupervised method for detecting anomalies and changes in RS images by means of a multivariate Gaussianization methodology that allows to estimate multivariate densities accurately, a long-standing problem in statistics, and machine learning. The methodology transforms arbitrarily complex multivariate data into a multivariate Gaussian distribution. Since the transformation is differentiable, by applying the change of variables formula, one can estimate the probability at any point of the original domain. The assumption is straightforward: pixels with low estimated probability are considered anomalies. Our method can describe any multivariate distribution, makes an efficient use of memory and computational resources, and is parameter-free. We show the efficiency of the method in experiments involving both AD and change detection (CD) in different RS image sets. For AD, we propose two approaches. The first is using directly the Gaussianization transform and the second is using a hybrid model that combines Gaussianization and the Reed-Xiaoli (RX) method typically used in AD. For CD, we take advantage of the Gaussianization transform and attribute the change to pixels with low probability compared to the first image, instead of those with high difference value typically employed in RS. Results show that our approach outperforms other linear and nonlinear methods in terms of detection power in both anomaly and CD scenarios, showing robustness and scalability to dimensionality and sample sizes.

Change detection for multitemporal hyperspectral images (HSIs) has always been a research hotspot of remote sensing. However, most current detection methods only use spectral information or spatial information separately, and there are many false detection areas in the detection results. Besides, the feature extraction method based on neural networks needs a huge amount of training samples, but collecting labeled training samples for change detection tasks is difficult. Therefore, this letter proposes a hyperspectral change detection method based on a simplified 3-D convolutional autoencoder (S3DCAECD). First, the framework is based on deep unsupervised autoencoder (AE), which can extract deep spectral-spatial features from bitemporal images without the need for prior information. Second, by adding a 3-D convolution kernel and eliminating the pooling layer, the structure of 3-D convolutional AE is simplified, which can reduce spectral redundancy and improve data processing speed. Finally, a softmax classifier with a 2-D convolutional layer added is used to obtain the detection result, and only a few label samples are needed to train the classifier. Three HSIs' experimental results indicate that the accuracy of the S3DCAECD is more than 95% on three experimental datasets and it has better detection results than several commonly used methods.

Change detection of high-resolution remote sensing images is an important task in earth observation and was extensively investigated. Recently, deep learning has shown to be very successful in plenty of remote sensing tasks. The current deep learning-based change detection method is mainly based on conventional long short-term memory (Conv-LSTM), which does not have spatial characteristics. Since change detection is a process with both spatiality and temporality, it is necessary to propose an end-to-end spatiotemporal network. To achieve this, Conv-LSTM, an extension of the Conv-LSTM structure, is introduced. Since it shares similar spatial characteristics with the convolutional layer, L-UNet, which substitutes partial convolution layers of UNet-to-Conv-LSTM and Atrous L-UNet (AL-UNet), which further using Atrous structure to multiscale spatial information is proposed. Experiments on two data sets are conducted and the proposed methods show the advantages both in quantity and quality when compared with some other methods.

It is quite an important and challenging problem for change detection (CD) from heterogeneous remote sensing images. The images obtained from different sensors (i.e., synthetic aperture radar (SAR) & optical camera) characterize the distinct properties of objects. Thus, it is impossible to detect changes by direct comparison of heterogeneous images. In this article, a new unsupervised change detection (USCD) method is proposed based on image translation. The cycle-consistent adversarial networks (CycleGANs) are employed to learn the subimage to subimage mapping relation using the given pair (i.e., before and after the event) of heterogeneous images from which the changes will be detected. Then, we can translate one image (e.g., SAR) from its original feature space (e.g., SAR) to another space (e.g., optical). By doing this, the pair of images can be represented in a common feature space (e.g., optical). The pixels with close pattern values in the before-event image may have quite different values in the after-event image if the change happens on some ones. Thus, we can generate the difference map between the translated before-event image and the original after-event image. Then, the difference map is divided into changed and unchanged parts. However, these detection results are not very reliable. We will select some significantly changed and unchanged pixel pairs from the two parts with the clustering technique (i.e., K-means). These selected pixel pairs are used to learn a binary classifier, and the other pixel pairs will be classified by this classifier to obtain the final CD results. Experimental results on different real datasets demonstrate the effectiveness of the proposed USCD method compared with several other related methods.

Compared with the synthetic aperture radar (SAR) image processing theory based on local neighborhood, the nonlocal theory is not limited to a local neighborhood of an image and has great potential in change detection of SAR images. In this study, an approach using ratio-based nonlocal information (RNLI) is proposed for change detection in multitemporal SAR images. First, the RNLI is extracted from a spatial-temporal nonlocal neighborhood where the similarity of two pixels in the nonlocal neighborhood is well characterized by the proposed ratio-based Gaussian kernel function. The parameters of RNLI: noise level and matching window size are adaptively determined to avoid the uncertainty of the change detection result caused by user experience. Second, the difference image is generated by using the RNLI and the ratio operator. Finally, the change map is obtained by segmenting the difference image with a threshold. Experiments conducted on two real datasets and two simulated datasets showed that the proposed method performed better than the other advanced change detection methods, which can better retain the edge information of the changed area while reducing the overall error of the change detection results.

Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the ``same object with different spectra'' problem is one of the biggest challenges in change detection. To overcome the challenge, a novel unsupervised spatial-spectral feature learning (FL) framework based on style transformation (ST) (called STFL-CD) is proposed for MSI change detection in this article. For dual-temporal MSIs, the proposed STFl-CD algorithm consists of two phases: ST and spatial-spectral FL. Since the image styles are inconsistent under different imaging environments, the first innovation is to transform the image styles through unmixing and reconstruction. Through ST, the challenge of the ``same object with different spectra'' problem will be reduced fundamentally. By introducing the attention mechanism, the other innovation is to extract the joint spectral-spatial change features based on a 3-D convolutional neural network with spatial and channel attention. In addition, for multitemporal MSIs, a multitemporal version STFL-CD (MT-STFL-CD) framework is designed based on a recurrent neural network to learn the correlation features between multitemporal remote sensing images. Both of the visual and quantitative results on the real MSI datasets indicate that the proposed unsupervised STFL-CD frameworks have significant advantages on multitemporal MSI change detection. In particular, the performance of the proposed unsupervised STFL-CD algorithm is even comparable to that of the state-of-the-art supervised or semisupervised methods.

With the increasing popularity of deep learning, most recent developments of change detection (CD) approaches have taken advantage of deep learning techniques to improve the detection performance. However, it is usually necessary to elaborately design the network architecture and train the model with a large amount of labeled data, which are difficult to obtain in practice. To overcome these limitations, this letter proposed an unsupervised CD framework for high-resolution remote sensing images integrating transfer learning-based bilinear convolutional neural networks (BCNNs) and object-based change analysis. A difference image is first generated, which is used for the subsequent preclassification and superpixel segmentation. Then, two sets of superpixel samples with reliable labels derived from the bitemporal remote sensing images are input into two pretrained CNNs to extract representative features, respectively. On this basis, the matrix outer product is utilized to generate the combined bilinear features, which are input into the softmax classifier to discriminate the change and no-change information and thus obtaining the final change map by feeding all sample data into the well-trained model. The experimental results on three real data sets demonstrate the effectiveness and superiority of the proposed method over several existing CD approaches.

With the acceleration of urbanization, it is essential to carry out change detection (CD) and obtain surface change information in urban areas. In the early stages, the spectral information of remote sensing images was used as a change index to capture the spectral and texture changes of ground objects in a two-dimensional plane. However, due to the dense buildings in urban areas, shadows and occlusions can be easily formed, and spectral information is sensitive to the imaging environment, such as the illumination, atmospheric conditions, and imaging angles, so that the detection results based on remote sensing images can often be incomplete. Most changes include not only 2-D plane changes but also 3-D elevation changes. Compared with spectral information, the elevation is more stable and more resistant to interference. Therefore, the fusion of remote sensing image and digital surface model (DSM) data has the potential to be used to detect the changes in urban areas. In this article, we propose a complementary evidence fusion 3-D CD framework based on the Dempster-Shafer theory (CDST). In this framework, DSM and normalized difference vegetation index (NDVI) data are combined using a complementary evidence combination rule. The DSM data can effectively overcome the impact of shadows, and the NDVI data can capture the relevant changes of height-insensitive ground objects, such as vegetation and water. When mapping the basic probability assignment (BPA) of the difference image (DI), prior knowledge is used to ensure that the BPA is not affected by the data distribution. Since DSM and remote sensing image data are heterogeneous data, there is a high degree of conflict when representing the change information characteristics of specific areas. For example, the change between grassland and road is small in elevation but significant in the spectral details, and the traditional Dempster's combination rule no longer applies. The proposed CDST framework uses a complementary evidence combination rule, which can effectively alleviate the conflicts between the evidence sources and improve the integrity of the detected changes. The experimental results obtained on real datasets confirm that the proposed method does indeed perform well.

Deep learning-based synthetic aperture radar (SAR) image change detection has recently achieved remarkable success due to its great potential for extracting abstract features. However, the existing methods still have room for improvement in dealing with the speckle of SAR images. In this letter, a deep spatial-temporal gray-level co-occurrence aware convolutional neural network (STGCNet) is proposed, which can effectively mine the spatial-temporal information of the bitemporal images and obtain the speckle-robust results by introducing the 3-D gray-level co-occurrence matrix (3-D-GLCM) as auxiliary feature. Specifically, representative features are extracted from original image pairs and their corresponding 3-D-GLCM through two-stream network, followed by an adaptive fusion module to balance the contribution of each branch. Then, the final binary change detection results are obtained by a fully connected layer. The training process relies on reliable labels generated by unsupervised models rather than manually annotated data, and therefore, the proposed STGCNet is practical in reality. Experiments on synthesized and real SAR data sets demonstrate the robustness and competitiveness of the proposed method compared with the state-of-the-art algorithms.

Multifractals have proven to be a valuable tool in image analysis applications and remote sensing (RS). They can be used for image segmentation, texture analysis, and classification. This study explores their applicability to change detection. We present a new approach to image texture description based on the calculation of a spatialized Holder exponent on multispectral satellite data. Holder exponents are evaluated in the context of an unsupervised change detection algorithm, and the classification of change in various land cover classes. Obtained results show that the sensitivity of proposed methods varies from 69% up to 93% with a considerably low level of false alarms.

With the increasing availability of multitemporal hyperspectral imagery, hyperspectral change detection under heterogeneous backgrounds is a challenging task. Due to the complexity of background features, traditional change detection algorithms in the spectral domain cannot effectively detect changed features. A novel method using multiple morphological profiles (MMPs) is proposed for hyperspectral change detection to make full use of spatial information. In the designed framework, first, the max-tree/min-tree strategy is applied to extract different attributes of multitemporal hyperspectral images (HSIs), i.e., area attribute and height attribute. Second, a spectral angle weighted-based local absolute distance (SALA) method is designed to reconstruct the discriminative spectral domain. Then, the absolute distance (AD) is adopted to extract changes in constructed feature domain. Finally, a change map is obtained by guided filtering. Experiments conducted on four real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.

Change detection (CD) is a hot issue in the field of remote sensing. Hyperspectral images (HSI) contain rich spectral information, and have gradually become an important data source in CD. Spectral-spatial combination is a commonly used strategy for suppressing the influence of noise on the spectrum. However, it is difficult to find a feature space that allows both spectral and spatial features to be optimally expressed. Therefore, this letter proposes a bidirectional reconstruction coding network and enhanced residual network for HSI CD (i.e., BRCN-ERN) based on the strategy of completely extracting spectral and spatial features separately and then fusing them together. In the spectral module, we use the spectrum of unchanged pixels at two time points to construct a bidirectional reconstruction network, and use the reconstruction error as a new source of spectral features. In the spatial module, we use advanced band selection algorithms to filter the bands with good spatial information, and design an enhanced two-dimensional residual network to extract the spatial features of the change tensor. Finally, the obtained spectral and spatial feature vectors are fused and inputted into the fully connected classification network to obtain the final CD map. Real HSI experiments show that our proposed BRCN-ERN has a better CD effect and is more effective than most existing algorithms.

With abundant temporal, spectral, and spatial information, multispectral images are proficient for acquiring a superior comprehension of the Earth's condition and its changes, which enables the achievement of multiple change detection (CD) tasks. However, high temporal, spatial, and spectral information of data brings obstacles to perform multiple change analysis due to the lack of effective feature extraction operation. In addition, the traditional multiple CD methods rely too much on manual participation. Here, a generative representation learning network (GRN) and a cyclic clustering technique are combined into a unified model, which is driven to learn spatial-temporal-spectral features for unsupervised multiple CD. GRN aims to efficiently extract and merge robust difference information with a recurrent learning mechanism for self-adaptive classification refinement, in which different types of changes can be identified and highlighted. Furthermore, a cyclic training strategy is designed to refine the clustering-friendly features, in which similar change types are gradually merged into the same classes. Meanwhile, the number of change types will be optimized through a self-adaptive way and eventually converge to its stable state, which is close to the real distribution. Experimental results on real multispectral datasets demonstrate the effectiveness and superiority of the proposed model on multiple CD.

Due to the difficulty and time-consuming of labeling ground truth map in practical situations, unsupervised multiple change detection (MCD) for multispectral images (MSIs) have attracted much attention in recent years. One possible strategy to obtain multiple changes is to assign labels to the binary change result. However, some methods are difficult to obtain the accurate binary result because of the complexity of backgrounds; moreover, assigning labels is also a challenge owing to the limitation of the number of spectral channels in MSIs. Therefore, we propose a novel unsupervised MCD framework based on auto-updating multitemporal matrix factorization (AMMF) and spatiospectral channel augmentation (SSCA). In AMMF, the accurate binary change result can be detected based on joint matrix factorization, during which the distribution and subspace information of each temporal image are regularized to encode the spatiotemporal correlation. In SSCA, some novel augmentation strategies are introduced to increase the number of channels in MSIs to form the normalized high-dimensional maps for each temporal image based on nonlinear operations and convolutional sparse analysis, respectively. MCD can be achieved by integrating the binary change result and directional information that can be calculated by high-dimensional maps of different temporal images. Experiments are conducted on two real MSIs, indicating that the proposed framework performs well in detecting multiple changes.

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.

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 widespread use of Internet-of-Things (IoT) technologies, smartphones, and social media services generates huge amounts of data streaming at high velocity. Automatic interpretation of these rapidly arriving data streams is required for the timely detection of interesting events that usually emerge in the form of clusters. This article proposes a new relative of the visual assessment of the cluster tendency (VAT) model, which produces a record of structural evolution in the data stream by building a cluster heat map of the entire processing history in the stream. The existing VAT-based algorithms for streaming data, called inc-VAT/inc-iVAT and dec-VAT/dec-iVAT, are not suitable for high-velocity and high-volume streaming data because of high memory requirements and slower processing speed as the accumulated data increases. The scalable iVAT (siVAT) algorithm can handle big batch data, but for streaming data, it needs to be (re)applied everytime a new datapoint arrives, which is not feasible due to the associated computation complexities. To address this problem, we propose an incremental siVAT algorithm, called inc-siVAT, which deals with the streaming data in chunks. It first extracts a small size smart sample using an intelligent sampling scheme, called maximin random sampling (MMRS), then incrementally updates the smart sample points on the fly, using our novel incremental MMRS (inc-MMRS) algorithm, to reflect changes in the data stream after each chunk is processed, and finally, produces an incrementally built iVAT image of the updated smart sample, using the inc-VAT/inc-iVAT and dec-VAT/dec-iVAT algorithms. These images can be used to visualize the evolving cluster structure and for anomaly detection in streaming data. Our method is illustrated with one synthetic and four real datasets, two of which evolve significantly over time. Our numerical experiments demonstrate the algorithm's ability to successfully identify anomalies and visualize changing cluster structure in streaming data.

Detecting changed regions in paired satellite images plays a key role in many remote sensing applications. The evolution of recent techniques could provide satellite images with a very high spatial resolution (VHR) but made it challenging to apply image coregistration, and many change detection methods are dependent on its accuracy. Two images of the same scene taken at different times or from different angles would introduce unregistered objects and the existence of both unregistered areas and actual changed areas would lower the performance of many change detection algorithms in unsupervised conditions. To alleviate the effect of unregistered objects in the paired images, we propose a novel change detection framework utilizing a special neural network architecture--Generative Adversarial Network (GAN) to generate many better coregistered images. In this article, we show that the GAN model can be trained upon a pair of images by using the proposed expanding strategy to create a training set and optimizing designed objective functions. The optimized GAN model would produce better coregistered images where changes can be easily spotted and then the change map can be presented through a comparison strategy using these generated images explicitly. Compared to other deep learning-based methods, our method is less sensitive to the problem of unregistered images and makes most of the deep learning structure. Experimental results on synthetic images and real data with many different scenes could demonstrate the effectiveness of the proposed approach.