Showing papers on "Change detection published in 2018"

Fully Convolutional Siamese Networks for Change Detection

Abstract: This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

Abstract: We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

Abstract: Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode.

Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks

Abstract: The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.

Change detection in remote sensing images using conditional adversarial networks

Abstract: . We present a method for change detection in images using Conditional Adversarial Network approach. The original network architecture based on pix2pix is proposed and evaluated for difference map creation. The paper address three types of experiments: change detection in synthetic images without objects relative shift, change detection in synthetic images with small relative shift of objects, and change detection in real season-varying remote sensing images.

Street-view change detection with deconvolutional networks

Abstract: We propose a system for performing structural change detection in street-view videos captured by a vehicle-mounted monocular camera over time. Our approach is motivated by the need for more frequent and efficient updates in the large-scale maps used in autonomous vehicle navigation. Our method chains a multi-sensor fusion SLAM and fast dense 3D reconstruction pipeline, which provide coarsely registered image pairs to a deep Deconvolutional Network (DN) for pixel-wise change detection. We investigate two DN architectures for change detection, the first one is based on the idea of stacking contraction and expansion blocks while the second one is based on the idea of Fully Convolutional Networks. To train and evaluate our networks we introduce a new urban change detection dataset which is an order of magnitude larger than existing datasets and contains challenging changes due to seasonal and lighting variations. Our method outperforms existing literature on this dataset, which we make available to the community, and an existing panoramic change detection dataset, demonstrating its wide applicability.

Change Detection in Heterogenous Remote Sensing Images via Homogeneous Pixel Transformation

Abstract: The change detection in heterogeneous remote sensing images remains an important and open problem for damage assessment. We propose a new change detection method for heterogeneous images (i.e., SAR and optical images) based on homogeneous pixel transformation (HPT). HPT transfers one image from its original feature space (e.g., gray space) to another space (e.g., spectral space) in pixel-level to make the pre-event and post-event images represented in a common space for the convenience of change detection. HPT consists of two operations, i.e., the forward transformation and the backward transformation. In forward transformation, for each pixel of pre-event image in the first feature space, we will estimate its mapping pixel in the second space corresponding to post-event image based on the known unchanged pixels. A multi-value estimation method with noise tolerance is introduced to determine the mapping pixel using $K$ -nearest neighbors technique. Once the mapping pixels of pre-event image are available, the difference values between the mapping image and the post-event image can be directly calculated. After that, we will similarly do the backward transformation to associate the post-event image with the first space, and one more difference value for each pixel will be obtained. Then, the two difference values are combined to improve the robustness of detection with respect to the noise and heterogeneousness (modality difference) of images. Fuzzy-c means clustering algorithm is employed to divide the integrated difference values into two clusters: changed pixels and unchanged pixels. This detection results may contain some noisy regions (i.e., small error detections), and we develop a spatial-neighbor-based noise filter to further reduce the false alarms and missing detections using belief functions theory. The experiments for change detection with real images (e.g., SPOT, ERS, and NDVI) during a flood in U.K. are given to validate the effectiveness of the proposed method.

Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images

Abstract: Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise suppression. First, an affiliated temporal change image, which obtains temporal difference in the pixel level, are built by three different metrics. Second, the simple linear iterative clustering algorithm is used to generate superpixels, which tightly adhere to the change image boundaries for the purpose of acquiring homogeneous change samples. Third, a sCAE network is trained with the superpixel samples as input to learn the change features in semantic. Then, the encoded features by this sCAE model are binary classified to create the change result map. Finally, the proposed method is compared with methods based on principal components analysis and Markov random fields. Experiment results show that our deep learning model can separate nonlinear noise efficiently from change features and obtain better performance in change detection for synthetic aperture radar images than conventional change detection algorithms.

Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

Abstract: Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images

Abstract: Change detection has been a hotspot in remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detection algorithms have been proposed. Among these methods, image transformation methods with feature extraction and mapping could effectively highlight the changed information and thus has better change detection performance. However, changes of multi-temporal images are usually complex, existing methods are not effective enough. In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection. Therefore, in this paper, based on deep network and slow feature analysis (SFA) theory, we proposed a new change detection algorithm for multi-temporal remotes sensing images called Deep Slow Feature Analysis (DSFA). In DSFA model, two symmetric deep networks are utilized for projecting the input data of bi-temporal imagery. Then, the SFA module is deployed to suppress the unchanged components and highlight the changed components of the transformed features. The CVA pre-detection is employed to find unchanged pixels with high confidence as training samples. Finally, the change intensity is calculated with chi-square distance and the changes are determined by threshold algorithms. The experiments are performed on two real-world datasets and a public hyperspectral dataset. The visual comparison and quantitative evaluation have both shown that DSFA could outperform the other state-of-the-art algorithms, including other SFA-based and deep learning methods.

Change detection based on Faster R-CNN for high-resolution remote sensing images

Abstract: Change detection is of great significance in remote sensing. The advent of high-resolution remote sensing images has greatly increased our ability to monitor land use and land cover changes...

Urban Change Detection Based on Dempster–Shafer Theory for Multitemporal Very High-Resolution Imagery

Abstract: Fusing multiple change detection results has great potentials in dealing with the spectral variability in multitemporal very high-resolution (VHR) remote sensing images. However, it is difficult to solve the problem of uncertainty, which mainly includes the inaccuracy of each candidate change map and the conflicts between different results. Dempster–Shafer theory (D–S) is an effective method to model uncertainties and combine multiple evidences. Therefore, in this paper, we proposed an urban change detection method for VHR images by fusing multiple change detection methods with D–S evidence theory. Change vector analysis (CVA), iteratively reweighted multivariate alteration detection (IRMAD), and iterative slow feature analysis (ISFA) were utilized to obtain the candidate change maps. The final change detection result is generated by fusing the three evidences with D–S evidence theory and a segmentation object map. The experiment indicates that the proposed method can obtain the best performance in detection rate, false alarm rate, and comprehensive indicators.

Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation

Abstract: Landslide inventory mapping (LIM) plays an important role in hazard assessment and hazard relief. Even though much research has taken place in past decades, there is space for improvements in accuracy and the usability of mapping systems. In this paper, a new landslide inventory mapping framework is proposed based on the integration of the majority voting method and the multiscale segmentation of a postevent images, making use of spatial feature of landslide. Compared with some similar state-of-the-art methods, the proposed framework has three advantages: 1) the generation of LIM is almost automatic; 2) the framework can achieve more accurate results because it takes into account the landslide spatial information in an irregular manner through multisegmentation of the postevent image and object-based majority voting (MV); and 3) it needs less parameter tuning. The proposed framework was applied to four landslide sites on Lantau Island, Hong Kong. Compared with existing methods, including region level set evolution (RLSE), edge level set evolution (ELSE) and change detection Markov random field (CDMRF) methods, quantitative evaluation shows the proposed framework is competitive in terms of Completeness. The framework outperformed RLSE, ELSE, and CDMRF for the four experiments by more than 9% in Correctness and by 8% in Quality. To the authors’ knowledge, this is the first-time that landslide spatial information has been utilized through the integration of multiscale segmentation of postevent image with the MV approach to obtain LIM using high spatial resolution remote sensing images. The approach is also of wide generality and applicable to other kinds of land cover change detection using remote sensing images.

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Abstract: To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

ChangeNet: A Deep Learning Architecture for Visual Change Detection

Abstract: The increasing urban population in cities necessitates the need for the development of smart cities that can offer better services to its citizens. Drone technology plays a crucial role in the smart city environment and is already involved in a number of functions in smart cities such as traffic control and construction monitoring. A major challenge in fast growing cities is the encroachment of public spaces. A robotic solution using visual change detection can be used for such purposes. For the detection of encroachment, a drone can monitor outdoor urban areas over a period of time to infer the visual changes. Visual change detection is a higher level inference task that aims at accurately identifying variations between a reference image (historical) and a new test image depicting the current scenario. In case of images, the challenges are complex considering the variations caused by environmental conditions that are actually unchanged events. Human mind interprets the change by comparing the current status with historical data at intelligence level rather than using only visual information. In this paper, we present a deep architecture called ChangeNet for detecting changes between pairs of images and express the same semantically (label the change). A parallel deep convolutional neural network (CNN) architecture for localizing and identifying the changes between image pair has been proposed in this paper. The architecture is evaluated with VL-CMU-CD street view change detection, TSUNAMI and Google Street View (GSV) datasets that resemble drone captured images. The performance of the model for different lighting and seasonal conditions are experimented quantitatively and qualitatively. The result shows that ChangeNet outperforms the state of the art by achieving 98.3% pixel accuracy, 77.35% object based Intersection over Union (IoU) and 88.9% area under Receiver Operating Characteristics (RoC) curve.

An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support

Abstract: Land-use/land-cover information is the basis of global-change research and regional governmental management. Automatic approaches are always required to update land maps for large-scale areas, and change detection techniques are the most important component of land-updating methods. Previous research has confirmed that simple change detection based on Landsat images from two different years with two different phenophases yields unsatisfactory results and may induce many misclassifications and pseudo-change identifications because of the phenological differences between remote sensing images. With the support of the Google Earth Engine (GEE), we propose a land-use/land-cover type discrimination method based on a classification and regression tree (CART), apply change-vector analysis in posterior probability space (CVAPS) and the best histogram maximum entropy method for change detection, and further improve the accuracy of the land-updating results in combination with NDVI timing analysis, which indicates the annual growth of ground vegetation. In the case study, we select western China as the research area and obtain a 2014 land map based on the ESA GlobCover 2009 dataset. The results confirm that the accuracy of the land-renewal results based on the CART-CVAPS-NDVI method reach 78.6–88.2%, which is 4–10% higher than that of the CART-CVPAS method without NDVI timing analysis. The CART-CVAPS-NDVI method has more detailed and accurate resolutions for land-change detection.

Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia

Abstract: Land use planners require up-to-date and spatially accurate time series land resources information and changing pattern for future management. As a result, assessing the status of land cover change...

Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection

Abstract: A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled. Following the intuitive idea of detecting changes by directly comparing dissimilarities between a pair of features, we propose a novel fully Convolutional siamese metric Network(CosimNet) to measure changes by customizing implicit metrics. To learn more discriminative metrics, we utilize contrastive loss to reduce the distance between the unchanged feature pairs and to enlarge the distance between the changed feature pairs. Specifically, to address the issue of large viewpoint differences, we propose Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish noisy changes. We demonstrate the effectiveness of the proposed approach with experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our approach is robust to lots of challenging conditions, such as illumination changes, large viewpoint difference caused by camera motion and zooming. In addition, we incorporate the distance metric into the segmentation framework and validate the effectiveness through visualization of change maps and feature distribution. The source code is available at this https URL.

Object-Based Change Detection for VHR Images Based on Multiscale Uncertainty Analysis

Abstract: Scale is of great significance in image analysis and interpretation. In order to utilize scale information, multiscale fusion is usually employed to combine change detection (CD) results from different scales. However, CD results from different scales are usually treated independently, which ignores the scale contextual information. To overcome this drawback, this letter introduces a novel object-based change detection (OBCD) technique for unsupervised CD in very high-resolution (VHR) images by incorporating multiscale uncertainty analysis. First, two temporal images are stacked and segmented using a series of optimal segmentation scales ranging from coarse to fine. Second, an initial CD result is obtained by fusing the pixel-based CD result and OBCD result based on Dempter–Shafer (DS) evidence theory. Third, multiscale uncertainty analysis is implemented from coarse scale to fine scale by support vector machine classification. Finally, a CD map is generated by combining all the available information in all the scales. The experimental results employing SPOT5 and GF-1 images demonstrate the effectiveness and superiority of the proposed approach.

Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks

Abstract: The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery

Abstract: Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities.

Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds.

Abstract: This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.

A Coarse-to-Fine Semi-Supervised Change Detection for Multispectral Images

Abstract: Change detection is an important technique providing insights to urban planning, resources monitoring, and environmental studies. For multispectral images, most semi-supervised change detection methods focus on improving the contribution of training samples hard to be classified to the trained classifier. However, hard training samples will weaken the discrimination of the training model for multispectral change detection. Besides, these methods only use the spectral information, while the limited spectral information cannot represent objects very well. In this paper, a method named as coarse-to-fine semi-supervised change detection is proposed to solve the aforementioned problems. First, a novel multiscale feature is exploited by concatenating the spectral vector of the pixel to be detected and its adjacent pixels by different scales. Second, the enhanced metric learning is proposed to acquire more discriminant metric by strengthening the contribution of training samples easy to be classified and weakening the contribution of training samples hard to be classified to the trained model. Finally, a coarse-to-fine strategy is adopted to detect testing samples from the viewpoint of distance metric and label information of neighborhood in spatial space. The coarse detection result obtained from the enhanced metric learning is used to guide the final detection. The effectiveness of our proposed method is verified on two real-life operating scenarios, Taizhou and Kunshan data sets. Extensive experimental results demonstrate that our proposed algorithm has better performance than those of other state-of-the-art algorithms.

Log-Based Transformation Feature Learning for Change Detection in Heterogeneous Images

Abstract: With the rapid development of remote sensing technology, how to accurately detect changes that have occurred on the land surface has been a critical task, particularly when images come from different satellite sensors. In this letter, we propose an unsupervised change detection method for heterogeneous synthetic aperture radar (SAR) and optical images based on the logarithmic transformation feature learning framework. First, the logarithmic transformation is applied to the SAR image that aims to achieve similar statistical distribution properties as the optical image. Then, high-level feature representations can be learned from the transformed image pair via joint feature extraction, which are used to select reliable samples for training a neural network classifier. When it is trained well, a robust change map can be obtained, thus identifying changed regions accurately. The experimental results on three real heterogeneous data sets demonstrate the effectiveness and superiority of the proposed method compared with other existing state-of-the-art approaches.