Object-level Change Detection Research Articles

Methodology for Object-Level Change Detection in Post-Earthquake Building Damage Assessment Based on Remote Sensing Images: OCD-BDA

Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-level change detection methods is significantly dependent on the consistency between pre- and post-disaster building images, particularly regarding variations in resolution, viewing angle, and lighting conditions; in object-level feature recognition methods, the low richness of semantic details of damaged buildings in images leads to a poor detection accuracy. This paper proposes a novel method, OCD-BDA (Object-Level Change Detection for Post-Disaster Building Damage Assessment), as an alternative to pixel-level change detection and object-level feature recognition methods. Inspired by human cognitive processes, this method incorporates three key steps: an efficient sample acquisition for object localization, labeling via HGC (Hierarchical and Gaussian Clustering), and model training and prediction for classification. Furthermore, this study establishes a change detection dataset based on Google Earth imagery of regions in Hatay Province before and after the Turkish earthquake. This dataset is characterized by pixel inconsistency and significant differences in photographic angles and lighting conditions between pre- and post-disaster images, making it a valuable test dataset for other studies. As a result, in the experiments of comparative generalization capabilities, OCD-BDA demonstrated a significant improvement, achieving an accuracy of 71%, which is twice that of the second-ranking method. Moreover, OCD-BDA exhibits superior performance in tasks with small sample amounts and rapid training time. With only 1% of the training samples, it achieves a prediction accuracy exceeding that of traditional transfer learning methods with 60% of samples. Additionally, it completes assessments across a large disaster area (450 km²) with 93% accuracy in under 23 min.

Improving Unsupervised Object-Based Change Detection via Hierarchical Multi-Scale Binary Partition Tree Segmentation: A Case Study in the Yellow River Source Region

Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics.

Automatic Urban Scene-Level Binary Change Detection Based on a Novel Sample Selection Approach and Advanced Triplet Neural Network

Change detection is a process of identifying changed ground objects by comparing image pairs obtained at different times. Compared with the pixel-level and object-level change detection, scene-level change detection can provide the semantic changes at image level, so it is important for many applications related to change descriptions and explanations such as urban functional area change monitoring. Automatic scene-level change detection approaches do not require ground truth used for training, making them more appealing in practical applications than nonautomatic methods. However, the existing automatic scene-level change detection methods only utilize low-level and mid-level features to extract changes between bitemporal images, failing to fully exploit the deep information. This article proposed a novel automatic binary scene-level change detection approach based on deep learning to address these issues. First, the pretrained VGG-16 and change vector analysis are adopted for scene-level direct predetection to produce a scene-level pseudo-change map. Second, pixel-level classification is implemented by using decision tree, and a pixel-level to scene-level conversion strategy is designed to generate the other scene-level pseudo-change map. Third, the scene-level training samples are obtained by fusing the two pseudo-change maps. Finally, the binary scene-level change map is produced by training a novel scene change detection triplet network (SCDTN). The proposed SCDTN integrates a late-fusion subnetwork and an early fusion subnetwork, comprehensively mining the deep information in each raw image as well as the temporal correlation between two raw images. Experiments were performed on a public dataset and a new challenging dataset, and the results demonstrated the effectiveness and superiority of the proposed approach

Optimization methods on dynamic monitoring of mineral reserves for open pit mine based on UAV oblique photogrammetry

The dynamic monitoring of mineral reserves for open pit mine by oblique photogrammetry is challenged by data-matching accuracy, illumination and terrain depression. In this paper, an object-level change detection method based on neighborhood windows is proposed to improve the accuracy of change detection in open pit mines. On this basis, a mining volume calculation method that considers mine type is established to solve the problem of lack of volume calculation caused by the depression of the mined terrain. A case study of eight open pit mines in the Shanxi Province indicated that the Precision (correctness), Recall (completeness) and F1-score for change detection were up to 92.38%, 87.22% and 89.73%, respectively. The relative errors in the calculated mined volume were between 0.38% and 0.77%. Our study provides the basis for creating an automated, highly robust workflows suited for monitoring mineral resource using 3D models acquired from UAV oblique photogrammetry.

Detecting Object-Level Scene Changes in Images with Viewpoint Differences Using Graph Matching

We developed a robust object-level change detection method that could capture distinct scene changes in an image pair with viewpoint differences. To achieve this, we designed a network that could detect object-level changes in an image pair. In contrast to previous studies, we considered the change detection task as a graph matching problem for two object graphs that were extracted from each image. By virtue of this, the proposed network more robustly detected object-level changes with viewpoint differences than existing pixel-level approaches. In addition, the network did not require pixel-level change annotations, which have been required in previous studies. Specifically, the proposed network extracted the objects in each image using an object detection module and then constructed correspondences between the objects using an object matching module. Finally, the network detected objects that appeared or disappeared in a scene using the correspondences that were obtained between the objects. To verify the effectiveness of the proposed network, we created a synthetic dataset of images that contained object-level changes. In experiments on the created dataset, the proposed method improved the F1 score of conventional methods by more than 40%. Our synthetic dataset will be available publicly online.

Semantics-aided 3D change detection on construction sites using UAV-based photogrammetric point clouds

As the key to the construction progress monitoring, methods and strategies for change detection using 3D point clouds from various sources have been investigated for years. However, how to achieve object-level change detection with uncertainty evaluation is still an unsolved topic. Occlusions and noise in 3D points and other attribute information, such as colors, lead to problems in the task of change detection. In this paper, we present a semantic-aided change detection method aimed at monitoring construction progress using UAV-based photogrammetric point clouds. Our framework consists of two key parts, which identify changes in a progressive manner: The first part consists of the detection of geometric changes using occupancy-based spatial difference identification, which indicates the changes of occupancy in 3D space, comprising changes in appearance or shape of building objects. In occupancy-based change detection, occupancy conflicts of occupied space and empty space along the viewing rays of cameras can be detected by considering the sensor positions. At the same time, occlusions can be handled implicitly. The second part involves changes in semantics, which are used to detect changes where the occupancy-based change detection is not sufficient for presenting changes, due to limitations of parameter settings or lack of attribute information. By utilizing semantic segmentation results presented by class probabilities, the uncertainty of the semantic changes can be estimated. For the detection of geometric and semantic changes, Dempster–Shafer theory is applied to fuse information from data acquired in different time epochs to detect changes. Using the two different types of changes, we can fully consider the changes that may happen at the construction sites and define the differences between the changes. By utilizing the proposed change detection methods, changes with different characteristics, including geometric changes and semantic changes, can be correctly identified. In a specific example, for a construction period from Dec 12, 2014 to Jan 16, 2015, 97.8% of the changed areas could be successfully detected. For the other construction period, from Jan 16, 2015 to Feb 26, 2015, 93.6% of changes were correctly detected.

Object-level change detection with a dual correlation attention-guided detector

Automatic change detection from remotely sensed imagery is extremely important for many applications, including land use mapping. In recent years, a growing number of researchers have applied capable deep-learning methods to the research on change detection. The majority of deep learning-based change detection methods currently perform pixel-by-pixel classification at the original image scale, but they can hardly avoid the false changes caused by strong parallax effects and projected shadows, without considering the totality of changed objects/regions. In this study, we propose an object-level change detection framework to detect changed geographic entities (such as newly built buildings or changed artificial structures) by paying more attention to the overall characteristics and context association of changed object instances. The detected changed objects are represented as bounding boxes, which are simple, regular, and convenient to use in object feature extraction. In terms of data handling, a special data augmentation method for change detection called Alternative-Mosaic is proposed to effectively accelerate model training and improve model performance. For the model, we propose a one-stage change detection network called dual correlation attention-guided detector (DCA-Det) to detect the changed objects. In particular, we feed the dual-temporal images into a weight-shared backbone network to extract the change features of different scales. The change features on the same scale are further refined, and then the features between different scales are fused by the correlation attention-guided feature fusion neck. Finally, the change detection heads output the prediction results of the changed objects/regions of different scales. Experiments were conducted on public LEVIR building change detection and aerial imagery change detection (AICD) datasets. The quantitative evaluation and visualization results proved the superiority and robustness of our framework. Our DCA-Det can obtain state-of-the-art performance on object-level metrics (99.50% APIoU=.50 and 79.72% APIoU=.50:.05:.95) on the AICD-2012 dataset.

High-Resolution Remote Sensing Image Change Detection Combined With Pixel-Level and Object-Level

High-resolution remote sensing images are abundant in texture information, and the detection method of the change of pixel-level mainly analyzes the spectral information of the image, which has certain limitations. In this paper, a high-resolution remote sensing image change detection method combining pixel and object levels is proposed to solve the problem that many pepper and salt phenomenon and false detection in the change detection of pixel-level and object-level change detection method are cumbersome for image segmentation process. We integrate the multi-dimensional features of high-resolution remote sensing images and use random forest classifiers to classify to obtain the pixel-level change detection results. Then, we use the improved U-net network to semantically segment the post-phase remote sensing image to obtain the image object segmentation result. Finally, the consequences of pixel-level change detection and image object segmentation result are fused to obtain the image changing area and the unchanging area. The experimental results demonstrate that the algorithm has a higher accuracy rate and detection precision.

Natural hazard damage detection based on object-level support vector data description of optical and SAR Earth observations

ABSTRACTThe Earth’s land covers are exposed to several types of environmental changes, issued by either human activities or natural disasters. On 11 March 2011, an earthquake occurred at about 130 km of the east coast of Sendai City in Japan. This earthquake has been followed by a huge tsunami, which caused devastating damages over the wide areas in the eastern coastlines of Japan. In order to manage such crises quickly and efficiently, change maps of affected areas are really crucial for damage estimation and proposing the essential services. Therefore, an automatic extraction of inundated and damaged areas from satellite images, with less user interaction, is essential and helpful. So far, the existing change detection (CD) approaches have a low degree of automation and are not optimal and applicable to high resolution optical and radar remote-sensing data. In order to resolve these problems, an integrated object-level CD method based on an object-based classifier and support vector data description method is proposed. In addition, parameter determination of the proposed method is addressed automatically by using an inter-cluster distance based approach. In order to evaluate the efficiency of the proposed method and extract the damaged areas, various optical and radar remote-sensing images from before and after of Sendai 2011’s tsunami, acquired by IKONOS, Radarsat-2, and TerraSAR-X, were used. The accuracy analysis of results showed a great flexibility for CD of by finding nonlinear object-level solutions to the problem. Furthermore, the comparative analysis of experimental results from IKONOS, Radarsat-2, and TerraSAR-X (kappa coefficient (κ): 0.85, 0.82, 0.76) and Support Vector Machine-based CD techniques (κ = 0.83, 0.73, 0.84), showed that the accuracy of the change maps is relatively improved. Finally, we came to the conclusion that the proposed method is considerably automated and less influenced by the errors in the classification process.

OBJECT-ORIENTED CHANGE DETECTION FOR REMOTE SENSING IMAGES BASED ON MULTI-SCALE FUSION

In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.

OBJECT-ORIENTED CHANGE DETECTION FOR REMOTE SENSING IMAGES BASED ON MULTI-SCALE FUSION

Abstract. In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.

BUILDING CHANGE DETECTION BY COMBINING LiDAR DATA AND ORTHO IMAGE

The elevation information is not considered in the traditional building change detection methods. This paper presents an algorithm of combining LiDAR data and ortho image for 3D building change detection. The advantages of the proposed approach lie in the fusion of the height and spectral information by thematic segmentation. Furthermore, the proposed method also combines the advantages of pixel-level and object-level change detection by image differencing and object analysis. Firstly, two periods of LiDAR data are filtered and interpolated to generate their corresponding DSMs. Secondly, a binary image of the changed areas is generated by means of differencing and filtering the two DSMs, and then thematic layer is generated and projected onto the DSMs and DOMs. Thirdly, geometric and spectral features of the changed area are calculated, which is followed by decision tree classification for the purpose of extracting the changed building areas. Finally, the statistics of the elevation and area change information as well as the change type of the changed buildings are done for building change analysis. Experimental results show that the completeness and correctness of building change detection are close to 81.8% and 85.7% respectively when the building area is larger than 80 <i>m</i><sup>2</sup>, which are increased about 10% when compared with using ortho image alone.

BUILDING CHANGE DETECTION BY COMBINING LiDAR DATA AND ORTHO IMAGE

Abstract. The elevation information is not considered in the traditional building change detection methods. This paper presents an algorithm of combining LiDAR data and ortho image for 3D building change detection. The advantages of the proposed approach lie in the fusion of the height and spectral information by thematic segmentation. Furthermore, the proposed method also combines the advantages of pixel-level and object-level change detection by image differencing and object analysis. Firstly, two periods of LiDAR data are filtered and interpolated to generate their corresponding DSMs. Secondly, a binary image of the changed areas is generated by means of differencing and filtering the two DSMs, and then thematic layer is generated and projected onto the DSMs and DOMs. Thirdly, geometric and spectral features of the changed area are calculated, which is followed by decision tree classification for the purpose of extracting the changed building areas. Finally, the statistics of the elevation and area change information as well as the change type of the changed buildings are done for building change analysis. Experimental results show that the completeness and correctness of building change detection are close to 81.8% and 85.7% respectively when the building area is larger than 80 m2, which are increased about 10% when compared with using ortho image alone.

OBJECT-LEVEL CHANGE DETECTION BASED ON HIGH-RESOLUTION REMOTE-SENSING IMAGES AND ITS APPLICATION IN JAPANESE EARTHQUAKE ON MARCH 11, 2011

Abstract. In accordance with the characteristics of change detection based on high-resolution remote-sensing images, this paper has put forward an object-level change detection method that is based on multi-feature integration and can take into account the properties of different types of object. This method classifies the most essential change information in applications into artificial objects related change information, water-related change information and vegetation-related change information. Direct association of object types and radiation, texture and geometric features is established by analyzing the characteristics of the three types of objects. During the application of object-level change detection method, first, feature vectors of objects are constructed by controlling the weight of radiation, texture and geometric features in different ways; then feature vectors of objects in multi-temporal images are analyzed with the method of object change vector analysis to obtain the change information of object types that are sensitive to a certain feature. In order to verify the validity of this method, this paper uses the high-resolution remote-sensing images from the Internet captured before and after the Japanese earthquake on March 11, 2011 to conduct some change detection experiments based on multifeature integration. Damage information is extracted and by controlling the weight of features, building damage, damage caused by submergence of seawater and vegetation damage are detected respectively. Experiments show that the method and processing put forward in this paper, flexible, practical and adaptable, are effective in such applications as the extraction of information about damage caused by earthquake and tsunami, and investigation of land use change.

Object-level Change Detection Based on Multiscale Fusion

Urban change detection based on multiscale fusion is discussed in this paper.The limitations of traditional change detection methods and the difficulties of urban change detection of very high resolution images are first analyzed. To address the above limitations,a novel object-level framework based on multiseale fusion is presented for urban change detection.Taking advantages of the object-level change detection,the framework can improve the separability of changed class and unchanged class.To further improve the change detection accuracy,two different multiscale fusion strategies are applied in the framework.Experimental results obtained on QuickBird images confirm the effectiveness of the proposed approach.

Object-level change detection based on full-scale image segmentation and its application to Wenchuan Earthquake

Aiming at object fragmentation and poor detection results caused by discontinuous segmentation scale in object-level change detection, a new object-level change detection method based on the full-scale object tree is presented in this paper. The core idea of this new algorithm is to establish the full-scale object tree based on convexity model theory and integrate full-scale image segmentation techniques and change detection into the whole process. Some Wenchuan Earthquake images are taken as an example to discuss the new method for earthquake damage detection and evaluation in urban area, landslide detection, and extraction of barrier lake boundary. The application shows that the new method is robust and it provides an advanced tool for the quantitative detection and evaluation of earthquake damage.

Object-level change detection in spectral imagery

Multitemporal monitoring of sites using spectral imagery is addressed. A comprehensive architecture is presented for the detection of significant changes in scene composition described at the object level of spatial scale. An object-level scene description is obtained by applying a statistical spectral anomaly detector followed by a competitive region growth object extractor. The competitive region growth algorithm is derived as the solution to an approximate maximum likelihood image segmentation problem. Gaussian spectral clustering is used to model the scene background. A digital site model is constructed that contains image segmentation maps and extracted object features. Object-level change detection (OLCD) is accomplished by comparing objects extracted from a new image to objects recorded in the site model. A restricted implementation of the architecture is described and tested on long-wave infrared hyperspectral imagery. It is demonstrated that spectral OLCD can eliminate false alarms based on their multitemporal persistence. Incorporating multiple images in the site model is observed to improve OLCD performance.