Building Change Detection Research Articles

A building change detection framework with patch-pairing single-temporal supervised learning and metric guided attention mechanism

Building change detection (CD) aims to detect changes in buildings from bi-temporal pairwise images obtained at different times. Typically, a deep learning-based building CD algorithm requires bi-temporal samples with significant building changes for training. However, obtaining such bi-temporal samples is challenging because building changes have a low probability of occurrence. Fortunately, it is relatively simple to obtain single-temporal samples that include a substantial number of buildings. By using these single-temporal building samples, pseudo bi-temporal building change samples can be generated, which can effectively address the problem of limited available bi-temporal building change samples. In view of that, this study proposes a metric guided single-temporal supervised learning framework that uses single-temporal building samples for building CD. In the proposed framework, patch-pairing single-temporal supervised learning (PPSL) adopts a patch-pairing method to construct pseudo bi-temporal building change samples, while equipping the network to effectively suppresses the negative impact of geometric offset and radiation difference in real samples. To further suppress the impact of radiation difference and enhance the effectiveness of our framework, a metric-guided spatial attention module (MGSAM) is designed to minimize the intra-class feature differences between temporal samples and augment the spatial context modeling ability. The proposed method is verified by experiments on different datasets, and the results demonstrate that the proposed method can outperform the existing methods and achieve superior performance.

ADF-Net: An Attention-Guided Dual-Branch Fusion Network for Building Change Detection near the Shanghai Metro Line Using Sequences of TerraSAR-X Images

Building change detection (BCD) plays a vital role in city planning and development, ensuring the timely detection of urban changes near metro lines. Synthetic Aperture Radar (SAR) has the advantage of providing continuous image time series with all-weather and all-time capabilities for earth observation compared with optical remote sensors. Deep learning algorithms have extensively been applied for BCD to realize the automatic detection of building changes. However, existing deep learning-based BCD methods with SAR images suffer limited accuracy due to the speckle noise effect and insufficient feature extraction. In this paper, an attention-guided dual-branch fusion network (ADF-Net) is proposed for urban BCD to address this limitation. Specifically, high-resolution SAR images collected by TerraSAR-X have been utilized to detect building changes near metro line 8 in Shanghai with the ADF-Net model. In particular, a dual-branch structure is employed in ADF-Net to extract heterogeneous features from radiometrically calibrated TerraSAR-X images and log ratio images (i.e., difference images (DIs) in dB scale). In addition, the attention-guided cross-layer addition (ACLA) blocks are used to precisely locate the features of changed areas with the transformer-based attention mechanism, and the global attention mechanism with the residual unit (GAM-RU) blocks is introduced to enhance the representation learning capabilities and solve the problems of gradient fading. The effectiveness of ADF-Net is verified using evaluation metrics. The results demonstrate that ADF-Net generates better building change maps than other methods, including U-Net, FC-EF, SNUNet-CD, A2Net, DMINet, USFFCNet, EATDer, and DRPNet. As a result, some building area changes near metro line 8 in Shanghai have been accurately detected by ADF-Net. Furthermore, the prediction results are consistent with the changes derived from high-resolution optical remote sensing images.

Siamese Transformer-Based Building Change Detection in Remote Sensing Images.

To address the challenges of handling imprecise building boundary information and reducing false-positive outcomes during the process of detecting building changes in remote sensing images, this paper proposes a Siamese transformer architecture based on a difference module. This method introduces a layered transformer to provide global context modeling capability and multiscale features to better process building boundary information, and a difference module is used to better obtain the difference features of a building before and after a change. The difference features before and after the change are then fused, and the fused difference features are used to generate a change map, which reduces the false-positive problem to a certain extent. Experiments were conducted on two publicly available building change detection datasets, LEVIR-CD and WHU-CD. The F1 scores for LEVIR-CD and WHU-CD reached 89.58% and 84.51%, respectively. The experimental results demonstrate that when utilized for building change detection in remote sensing images, the proposed method exhibits improved robustness and detection performance. Additionally, this method serves as a valuable technical reference for the identification of building damage in remote sensing images.

Change Detection of Buildings Using Siamese Neural Networks in Coastal Environments

Change Detection of Buildings Using Siamese Neural Networks in Coastal Environments

Iterative Edge Enhancing Framework for Building Change Detection

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Building Change Detection</i> (BCD) task serves urban planning through monitoring the landuse. However, due to the complexity of remote sensing images and high foreground-background similarity, it leads to inaccurate detection of building edge regions. Existing methods deal with this problem by fusing features of different layers. But the fusing operation cannot separate details information from overall information of buildings, resulting in inaccurate detection of building edge area. To address the above challenges, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iterative Edge Enhancing Framework</i> (IEEF). The IEEF alleviates the building edge detection difficulty by densely implementing Detail Semantic Enhancement Module in decoding part. This module takes differential features between adjacent scales to explicitly represent the building edge information. Simultaneously, to deal with the classes imbalance problem, a Density-Guided Sampling method dedicated for change detection is proposed to increase the proportion of positive samples during training. Our proposed method achieves state-of-the-art performance on the LEVIR-CD dataset and the WHU dataset, and obtains accurate changed building edges.

SFGT-CD: Semantic Feature-Guided Building Change Detection From Bitemporal Remote-Sensing Images With Transformers

SFGT-CD: Semantic Feature-Guided Building Change Detection From Bitemporal Remote-Sensing Images With Transformers

Bitemporal Attention Transformer for Building Change Detection and Building Damage Assessment

Bitemporal Attention Transformer for Building Change Detection and Building Damage Assessment

Remote Sensing Building Change Detection With Global High-Frequency Cues Guidance and Result-Aware Alignment

Remote Sensing Building Change Detection With Global High-Frequency Cues Guidance and Result-Aware Alignment

MFFNet: a building change detection method based on fusion of spectral and geometric information

Accurate detection and extraction of changes in buildings heights is important in monitoring construction (both legal and illegal) and assessing disasters. It is also important information for updating real 3D scenes. However, when using remote sensing images, shadows, vegetation and objects with similar spectral and morphological characteristics as buildings can cause false detections, omissions and incomplete patch edges. To address this issue, we develop the multiscale feature fusion network for dual-modal data (MFFNet), which has two main aspects: (1) The multi-dual-modal feature fusion module detects changes in features with similar spectral and morphological characteristics as buildings. This mitigates false detections by making the model more aware of areas where the elevation has changed over time. (2) Because building extraction is affected by shadows and vegetation, we designed a multiscale feature shuffle module. It takes multiscale features and establishes relationships between neighbouring pixels using the pixel-shuffle algorithm, then fuses and reorganizes the multiscale features to highlight the relationships between global contexts, thereby mitigating the problem of building occlusion by shadows. Comparative experiments show that MFFNet achieves better results on GF7-CD and 3DCD datasets than other similar methods. The proposed method can more accurately monitor building changes over large areas.

Multiscale Attention Fusion Graph Network for Remote Sensing Building Change Detection

Multiscale Attention Fusion Graph Network for Remote Sensing Building Change Detection

Hybrid Attention-Aware Transformer Network Collaborative Multiscale Feature Alignment for Building Change Detection

Hybrid Attention-Aware Transformer Network Collaborative Multiscale Feature Alignment for Building Change Detection

SEDANet: A New Siamese Ensemble Difference Attention Network for Building Change Detection in Remotely Sensed Images

SEDANet: A New Siamese Ensemble Difference Attention Network for Building Change Detection in Remotely Sensed Images

Detection of Changes in Buildings in Remote Sensing Images via Self-Supervised Contrastive Pre-Training and Historical Geographic Information System Vector Maps

The detection of building changes (hereafter ‘building change detection’, BCD) is a critical issue in remote sensing analysis. Accurate BCD faces challenges, such as complex scenes, radiometric differences between bi-temporal images, and a shortage of labelled samples. Traditional supervised deep learning requires abundant labelled data, which is expensive to obtain for BCD. By contrast, there is ample unlabelled remote sensing imagery available. Self-supervised learning (SSL) offers a solution, allowing learning from unlabelled data without explicit labels. Inspired by SSL, we employed the SimSiam algorithm to acquire domain-specific knowledge from remote sensing data. Then, these well-initialised weight parameters were transferred to BCD tasks, achieving optimal accuracy. A novel framework for BCD was developed using self-supervised contrastive pre-training and historical geographic information system (GIS) vector maps (HGVMs). We introduced the improved MS-ResUNet network for the extraction of buildings from new temporal satellite images, incorporating multi-scale pyramid image inputs and multi-layer attention modules. In addition, we pioneered a novel spatial analysis rule for detecting changes in building vectors in bi-temporal images. This rule enabled automatic BCD by harnessing domain knowledge from HGVMs and building upon the spatial analysis of building vectors in bi-temporal images. We applied this method to two extensive datasets in Liuzhou, China, to assess its effectiveness in both urban and suburban areas. The experimental results demonstrated that our proposed approach offers a competitive quantitative and qualitative performance, surpassing existing state-of-the-art methods. Combining HGVMs and high-resolution remote sensing imagery from the corresponding years is useful for building updates.

ENTROPY-BASED INDOOR CHANGE DETECTION USING LIDAR DATA AND A 3D MODEL

Abstract. Indoor change detection is important for building monitoring, building management and model-based localization and navigation systems because the real building environment may not always be the same as the design model. This paper presents a novel indoor building change detection method based on entropy. A sequence of real LiDAR scans is acquired with a static LiDAR scanner and the pose of the LiDAR scanner for each scan is then estimated. Synthetic LiDAR scans are generated with the pose of the LiDAR scanner using the 3D model. The real LiDAR scans and synthetic LiDAR scans are sliced horizontally with a certain angular interval and the entropy of all slices of LiDAR scans is then calculated. Differenced entropy between two corresponding slices of real LiDAR scans and synthetic LiDAR scans is calculated for the classification of the changes. Each slice of real LiDAR scans will be classified into one of the four categories of changes: unchanged, moving objects, structural change and non-structural change. Experimental results show that unchanged slices and slices containing moving objects can be accurately detected, achieving 100% accuracy while non-structural and structural changes are detected with an accuracy of 98.5% and 86.3% respectively.

BCD-Net: building change detection based on fully scale connected U-Net and subpixel convolution

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

Fusion-Former: Fusion Features across Transformer and Convolution for Building Change Detection

Change detection (CD) in remote sensing images is a technique for analyzing and characterizing surface changes from remotely sensed data from different time periods. However, due to the diverse nature of targets in complex remote sensing scenarios, the current deep-learning-based methods still sometimes suffer from the problem of the extracted features not being discriminative enough, resulting in false detections and detail loss. To solve these challenges, we propose a method called Fusion-Former for building change detection. Our approach fuses window-based self-attention with depth-wise convolution, which is named Fusion-Block and which combines convolutional neural networks (CNN) and a transformer to integrate information at different scales effectively. Moreover, in order to significantly enhance the performance of the transformer and the effect of Fusion-Block, an innovative attention module called Vision-Module is introduced. On the LEVIR-CD dataset and WHU-CD dataset, our model achieved F1-scores of 89.53% and 86.00%, respectively, showcasing its superior performance over state-of-the-art methods.

EFP-Net: A Novel Building Change Detection Method Based on Efficient Feature Fusion and Foreground Perception

Over the past decade, deep learning techniques have significantly advanced the field of building change detection in remote sensing imagery. However, existing deep learning-based approaches often encounter limitations in complex remote sensing scenarios, resulting in false detections and detail loss. This paper introduces EFP-Net, a novel building change detection approach that resolves the mentioned issues by utilizing effective feature fusion and foreground perception. EFP-Net comprises three main modules, the feature extraction module (FEM), the spatial–temporal correlation module (STCM), and the residual guidance module (RGM), which jointly enhance the fusion of bi-temporal features and hierarchical features. Specifically, the STCM utilizes the temporal change duality prior and multi-scale perception to augment the 3D convolution modeling capability for bi-temporal feature variations. Additionally, the RGM employs the higher-layer prediction map to guide shallow layer features, reducing the introduction of noise during the hierarchical feature fusion process. Furthermore, a dynamic Focal loss with foreground awareness is developed to mitigate the class imbalance problem. Extensive experiments on the widely adopted WHU-BCD, LEVIR-CD, and CDD datasets demonstrate that the proposed EFP-Net is capable of significantly improving accuracy in building change detection.

Multi-Scale Discrete Cosine Transform Network for Building Change Detection in Very-High-Resolution Remote Sensing Images

With the rapid development and promotion of deep learning technology in the field of remote sensing, building change detection (BCD) has made great progress. Some recent approaches have improved detailed information about buildings by introducing high-frequency information. However, there are currently few methods considering the effect of other frequencies in the frequency domain for enhancing feature representation. To overcome this problem, we propose a multi-scale discrete cosine transform (DCT) network (MDNet) with U-shaped architecture, which is composed of two novel DCT-based modules, i.e., the dual-dimension DCT attention module (D3AM) and multi-scale DCT pyramid (MDP). The D3AM aims to employ the DCT to obtain frequency information from both spatial and channel dimensions for refining building feature representation. Furthermore, the proposed MDP can excavate multi-scale frequency information and construct a feature pyramid through multi-scale DCT, which can elevate multi-scale feature extraction of ground targets with various scales. The proposed MDNet was evaluated with three widely used BCD datasets (WHU-CD, LEVIR-CD, and Google), demonstrating that our approach can achieve more convincing results compared to other comparative methods. Moreover, extensive ablation experiments also present the effectiveness of our proposed D3AM and MDP.

A Siamese Multiscale Attention Decoding Network for Building Change Detection on High-Resolution Remote Sensing Images

The objective of building change detection (BCD) is to discern alterations in building surfaces using bitemporal images. The superior performance and robustness of various contemporary models suggest that rapid development of BCD in the deep learning age is being witnessed. However, challenges abound, particularly due to the diverse nature of targets in urban settings, intricate city backgrounds, and the presence of obstructions, such as trees and shadows, when using very high-resolution (VHR) remote sensing images. To overcome the shortcomings of information loss and lack of feature extraction ability, this paper introduces a Siamese Multiscale Attention Decoding Network (SMADNet). This network employs the Multiscale Context Feature Fusion Module (MCFFM) to amalgamate contextual information drawn from multiscale target, weakening the heterogeneity between raw image features and difference features. Additionally, our method integrates a Dual Contextual Attention Decoding Module (CADM) to identify spatial and channel relations amongst features. For enhanced accuracy, a Deep Supervision (DS) strategy is deployed to enhance the ability to extract more features for middle layers. Comprehensive experiments on three benchmark datasets, i.e., GDSCD, LEVIR-CD, and HRCUS-CD, establish the superiority of SMADNet over seven other state-of-the-art (SOTA) algorithms.

Detection and Classification of Buildings by Height from Single Urban High-Resolution Remote Sensing Images

Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications.