The Siamese network is becoming the mainstream in change detection of remote sensing images (RSI). However, in recent years, the development of more complicated structure, module and training processe has resulted in the cumbersome model, which hampers their application in large-scale RSI processing. To this end, this paper proposes an extremely lightweight Siamese network (LSNet) for RSI change detection, which replaces standard convolution with depthwise separable atrous convolution, and removes redundant dense connections, retaining only valid feature flows while performing Siamese feature fusion, greatly compressing parameters and computation amount. Compared with the first-place model on the CCD dataset, the parameters and the computation amount of LSNet is greatly reduced by 90.35\% and 91.34\% respectively, with only a 1.5\% drops in accuracy.
University of Twente（特文特大学）；Technical University of Brunswick（布伦瑞克工业大学）；Wageningen University and Research（瓦赫宁根大学及研究中心）
Detecting topographic changes in the urban environment has always been an important task for urban planning and monitoring. In practice, remote sensing data are often available in different modalities and at different time epochs. Change detection between multimodal data can be very challenging since the data show different characteristics. Given 3D laser scanning point clouds and 2D imagery from different epochs, this paper presents a framework to detect building and tree changes. First, the 2D and 3D data are transformed to image patches, respectively. A Siamese CNN is then employed to detec…
Siamese networks are widely used for remote sensing change detection tasks. A vanilla siamese network has two identical feature extraction branches which share weights, these two branches work independently and the feature maps are not fused until about to be sent to a decoder head. However we find that it is critical to exchange information between two feature extraction branches at early stage for change detection task. In this work we present Mutual-Attention Siamese Network (MASNet), a general siamese network with mutual-attention plug-in, so to exchange information between the two feature…
In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual task constrained deep Siamese convolutional network (DTCDSCN) model, which contains three sub-networks: a change detection network and two semantic segmentation networks. DTCDSCN can accomplish both change detection and semantic segmentation at the same time, which can help to learn more discrim…
This paper presents a transformer-based Siamese network architecture (abbreviated by ChangeFormer) for Change Detection (CD) from a pair of co-registered remote sensing images. Different from recent CD frameworks, which are based on fully convolutional networks (ConvNets), the proposed method unifies hierarchically structured transformer encoder with Multi-Layer Perception (MLP) decoder in a Siamese network architecture to efficiently render multi-scale long-range details required for accurate CD. Experiments on two CD datasets show that the proposed end-to-end trainable ChangeFormer architect…
SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images
Nanjing University of Science and Technology（南京理工大学）
Change detection (CD) of remote sensing images is to detect the change region by analyzing the difference between two bitemporal images. It is extensively used in land resource planning, natural hazards monitoring and other fields. In our study, we propose a novel Siamese neural network for change detection task, namely Dual-UNet. In contrast to previous individually encoded the bitemporal images, we design an encoder differential-attention module to focus on the spatial difference relationships of pixels. In order to improve the generalization of networks, it computes the attention weights be…
Central South University（中南大学）；National University of Defense Technology（中国人民解放军国防科技大学）
Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise of deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. To overcome the lack of resistance of current methods to pseudo-changes, in this paper, we propo…
Change detection (CD) is an important problem in remote sensing, especially in disaster time for urban management. Most existing traditional methods for change detection are categorized based on pixel or objects. Object-based models are preferred to pixel-based methods for handling very high-resolution remote sensing (VHR RS) images. Such methods can benefit from the ongoing research on deep learning. In this paper, a fully automatic change-detection algorithm on VHR RS images is proposed that deploys Fully Convolutional Siamese Concatenate networks (FC-Siam-Conc). The proposed method uses pre…
Ethiopian Institute of Technology - Mekelle（埃塞俄比亚理工学院 - 梅克莱）；Institute of Geo-Information and Earth Observation Science（地理信息与地球观测科学研究所）
Change detection is a fast-growing discipline in the areas of computer vision and remote sensing. In this work, we designed and developed a variant of convolutional neural network (CNN), known as Siamese CNN to extract features from pairs of Sentinel-2 temporal images of Mekelle city captured at different times and detect changes due to urbanization: buildings and roads. The detection capability of the proposed was measured in terms of overall accuracy (95.8), Kappa measure (72.5), recall (76.5), precision (77.7), F1 measure (77.1). The model has achieved a good performance in terms of most of…
Change detection (CD) is one of the most vital applications in remote sensing. Recently, deep learning has achieved promising performance in the CD task. However, the deep models are task-specific and CD data set bias often exists, hence it is inevitable that deep CD models would suffer degraded performance after transferring it from original CD data set to new ones, making manually label numerous samples in the new data set unavoidable, which costs a large amount of time and human labor. How to learn a transferable CD model in the data set with enough labeled data (original domain) but can we…
Deep learning based change detection methods have received wide attentoion, thanks to their strong capability in obtaining rich features from images. However, existing AI-based CD methods largely rely on three functionality-enhancing modules, i.e., semantic enhancement, attention mechanisms, and correspondence enhancement. The stacking of these modules leads to great model complexity. To unify these three modules into a simple pipeline, we introduce Relational Change Detection Transformer (RCDT), a novel and simple framework for remote sensing change detection tasks. The proposed RCDT consists…
School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Instrumentation Science and Engineering, Harbin Institute of Technology（哈尔滨工业大学仪器科学与工程学院）；Harbin Institute of Technology Suzhou Research Institute（哈尔滨工业大学苏州研究院）；Ningbo Haichuang Group Co., Ltd.（宁波海创集团有限公司）；School of Software Technology, Zhejiang University（浙江大学软件学院）；School of Information Engineering, North China University of Water Resources and Electric Power（中国北方水利电力大学信息工程学院）；Department of Geo-spatial Information, Information Engineering University（信息工程大学地理信息学院）；Innovation Center of Yangtze River Delta, Zhejiang University（长三角创新中心，浙江大学）
KTH Royal Inst. of Technology（KTH皇家理工学院）；University of Rwanda（卢旺达大学）
To better understand current trends of urban population growth in Sub-Saharan Africa, high-quality spatiotemporal population estimates are necessary. While the joint use of remote sensing and deep learning has achieved promising results for population distribution estimation, most of the current work focuses on fine-scale spatial predictions derived from single date census, thereby neglecting temporal analyses. In this work, we focus on evaluating how deep learning change detection techniques can unravel temporal population dynamics at short intervals. Since Post-Classification Comparison (PCC…
STeInFormer: Spatial-Temporal Interaction Transformer Architecture for Remote Sensing Change Detection
School of Software Technology, Zhejiang University（浙江大学软件学院）；School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Earth Sciences, Zhejiang University（地球科学学院）；Innovation Center of Yangtze River Delta, Zhejiang University（浙江大学长三角创新中心）
ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery
1 Inst.\ of Data Science, National Cheng Kung University, \ , Taiwan - 2 Inst.\ of Intelligent Systems College of Artificial Intelligence, \ Yang Ming Chiao Tung University, Hsinchu, Taiwan
PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与地理信息工程学院）；Guangxi Water & Power Design Institute CO., Ltd.（广西水电设计院有限公司）
Deep learning techniques have achieved great success in remote sensing image change detection. Most of them are supervised techniques, which usually require large amounts of training data and are limited to a particular application. Self-supervised methods as an unsupervised approach are popularly used to solve this problem and are widely used in unsupervised binary change detection tasks. However, the existing self-supervised methods in change detection are based on pre-tasks or at patch-level, which may be sub-optimal for pixel-wise change detection tasks. Therefore, in this work, a pixel-wi…
Space Applications Centre（空间应用中心）；Indian Space Research Organisation（印度空间研究组织）
In the past few years supervised and adversarial learning have been widely adopted in various complex computer vision tasks. It seems natural to wonder whether another branch of artificial intelligence, commonly known as Reinforcement Learning (RL) can benefit such complex vision tasks. In this study, we explore the plausible usage of RL in super resolution of remote sensing imagery. Guided by recent advances in super resolution, we propose a theoretical framework that leverages the benefits of supervised and reinforcement learning. We argue that a straightforward implementation of RL is not a…
University of Electronic Science and Technology of China（电子科技大学）；Leshan Normal University（乐山师范学院）
In this work, a discriminatively learned CNN embedding is proposed for remote sensing image scene classification. Our proposed siamese network simultaneously computes the classification loss function and the metric learning loss function of the two input images. Specifically, for the classification loss, we use the standard cross-entropy loss function to predict the classes of the images. For the metric learning loss, our siamese network learns to map the intra-class and inter-class input pairs to a feature space where intra-class inputs are close and inter-class inputs are separated by a marg…
Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks
Institute of Advanced Research in Artificial Intelligence（人工智能高级研究院）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心）；Helmholtz Institute Freiberg for Resource Technology (HIF)（弗赖贝格亥姆霍兹资源技术研究所）；Wuhan University（武汉大学）
A Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness
School of Information Science and Technology, Southwest Jiaotong University（信息科学与技术学院，西南交通大学）；School of Computer Science and Technology, Xidian University（计算机科学与技术学院，西安电子科技大学）
An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Institute of Technology（北京理工大学）；China Aero Geophysical Survey and Remote Sensing Center for Natural Resources（中国自然资源航空物探遥感中心）；La Trobe University（乐卓博大学）
Sapienza University of Rome（罗马第一大学）；Sapienza School for Advanced Studies（罗马第一大学高等研究院）
Change detection is one of the most active research areas in Remote Sensing (RS). Most of the recently developed change detection methods are based on deep learning (DL) algorithms. This kind of algorithms is generally focused on generating two-dimensional (2D) change maps, thus only identifying planimetric changes in land use/land cover (LULC) and not considering nor returning any information on the corresponding elevation changes. Our work goes one step further, proposing two novel networks, able to solve simultaneously the 2D and 3D CD tasks, and the 3DCD dataset, a novel and freely availab…
Accurate and fine-grained information about the extent of damage to buildings is essential for humanitarian relief and disaster response. However, as the most commonly used architecture in remote sensing interpretation tasks, Convolutional Neural Networks (CNNs) have limited ability to model the non-local relationship between pixels. Recently, Transformer architecture first proposed for modeling long-range dependency in natural language processing has shown promising results in computer vision tasks. Considering the frontier advances of Transformer architecture in the computer vision field, in…
Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict Zones
School of Computer Science and Technology（计算机科学与技术学院）；Zhejiang University（浙江大学）；School of Software Technology（软件学院）；School of Information Engineering（信息工程学院）；North China University of Water Resources and Electric Power（北方水利电力大学）；Polytechnic Institute；Institute of Systems Engineering（系统工程院）；Academy of Military Sciences（军事科学院）；Department of Geo-spatial Information（测绘信息学院）；Information Engineering University（信息工程大学）；School of Instrumentation Science and Engineering（仪器科学与工程学院）；Harbin Institute of Technology（哈尔滨工业大学）；Harbin Institute of Technology Suzhou Research Institute（哈尔滨工业大学苏州研究院）
SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective
School of Earth Sciences and Engineering, Hohai University（河海大学地球科学与工程学院）；School of Geosciences and Info-physics, Central South University（中南大学地球科学与信息物理学院）
Technical University of Munich（慕尼黑工业大学）；Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）
In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated dataset that…
University of East Anglia（东英吉利大学）；Newcastle University（纽卡斯尔大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Recognising remote sensing scene images remains challenging due to large visual-semantic discrepancies. These mainly arise due to the lack of detailed annotations that can be employed to align pixel-level representations with high-level semantic labels. As the tagging process is labour-intensive and subjective, we hereby propose a novel Multi-Granularity Canonical Appearance Pooling (MG-CAP) to automatically capture the latent ontological structure of remote sensing datasets. We design a granular framework that allows progressively cropping the input image to learn multi-grained features. For …
Changes to Captions: An Attentive Network for Remote Sensing Change Captioning
Institute of Advanced Research in Artificial Intelligence（人工智能高级研究院）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology (HIF)（亥姆霍兹弗赖贝格资源技术研究所）
School of Ocean Engineering, Harbin Institute of Technology（哈尔滨工业大学（威海）海洋工程学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学（威海）计算机科学与技术学院）
With the continuous development of underwater vision technology, more and more remote sensing images could be obtained. In the underwater scene, sonar sensors are currently the most effective remote perception devices, and the sonar images captured by them could provide rich environment information. In order to analyze a certain scene, we often need to merge the sonar images from different periods, various sonar frequencies and distinctive viewpoints. However, the above scenes will bring nonlinear intensity differences to the sonar images, which will make traditional matching methods almost in…
Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery
Urban Resilience.AI Lab（城市韧性人工智能实验室）；Zachry Department of Civil and Environmental Engineering（扎克里土木与环境工程系）；Texas A&M University（德克萨斯A&M大学）；College Station, TX（学院站, 德克萨斯）
