For some time, CNN was the de facto state-of-the-art method in remote sensing image change detection. Although transformer-based models have surpassed CNN-based models due to their larger receptive fields, CNNs still retain their value for their effi...
Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. Ho...
Change detection based on deep learning has made great progress recently, but there are still some challenges, such as the small data size in open-labeled datasets, the different viewpoints in image pairs, and the poor similarity measures in feature...
Earth observation satellites have been capturing a variety of data about our planet for several decades, making many environmental applications possible such as change detection. Recently, deep learning methods have been proposed for urban change det...
Remote sensing binary change detection aims to identify real changes in bi-temporal images while suppressing pseudo-change responses induced by complex backgrounds and temporal variations in otherwise unchanged regions. Although existing state-space-...
Change detection (CD) with heterogeneous images is currently attracting extensive attention in remote sensing. In order to make heterogeneous images comparable, the image transformation methods transform one image into the domain of another image, wh...
Sequential change detection is a fundamental problem in statistics and signal processing, with the CUSUM procedure widely used to achieve minimax detection delay under a prescribed false alarm rate when pre- and post-change distributions are fully kn...
Change detection in multi-temporal remote sensing images has usually been treated as a problem of explicitly detecting land cover transitions. To date, multi-dimensional change vector analysis has been an effective solution to such problems. However,...
Change detection (CD) is the main task in the remote sensing field. Binary change detection (BCD), which only focuses on the region of change, cannot meet current needs. Semantic change detection (SCD) is pivotal for identifying regions of change in...
The existing change detection (CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information. However, CD is ess...
Change detection (CD), one of the primary applications of multi-temporal satellite images, is the process of identifying changes in the Earth’s surface occurring over a period of time using images of the same geographic area on different dates....
Image Change Detection (ICD) methods are widely adopted to update large area land use/cover products. Uncertainty problems, however, are well known in such techniques, and a transparent assessment is necessary. In this study, a framework was proposed...
When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or...
Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated wit...
Multi-temporal satellite images are available at very high revisit frequency, allowing the characterization of land-cover transitions and trajectories in greater detail. However, most change detection methods aim to capture a snapshot of land cover,...
Remote sensing image change detection is crucial for urban planning, environmental monitoring, and disaster assessment, as it identifies temporal variations of specific targets, such as surface buildings, by analyzing differences between images from...
The most commonly used features in unsupervised change detection are spectral characteristics. Traditional methods describe the degree of the change between two pixels by quantifying the difference in spectral values or spectral shapes (spectral curv...
Change detection (CD) is critical for natural disaster detection, monitoring and evaluation. Video satellites, new types of satellites being launched recently, are able to record the motion change during natural disasters. This raises a new problem f...
Change detection is a fundamental task in remote sensing with broad applications in urban monitoring, agriculture, watershed management, and land use and land cover analysis. In urban environments, accurate change detection is particularly critical f...
Change detection is employed to identify regions of change between two different time phases. Presently, the CNN-based change detection algorithm is the mainstream direction of change detection. However, there are two challenges in current change det...
Detection of small moving objects in long range infrared (IR) videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we present two unsupervised, modular, and flexible frameworks to detect small moving...
Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analy...
The interest in change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In partic...
With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth’s land surface and plays a vital role in...
Change detection (CD) is an essential tool for the accurate understanding of land surface changes using Earth observation data and is extremely important for detecting the interactions between social and natural occurrences in geoscience. Binary chan...
Change detection based on bi-temporal remote sensing images has made significant progress in recent years, aiming to identify the changed and unchanged pixels between a registered pair of images. However, most learning-based change detection methods...
Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are...
Detecting changes in urban areas presents many challenges, including complex features, fast-changing rates, and human-induced interference. At present, most of the research on change detection has focused on traditional binary change detection (BCD),...
Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. Howeve...
As urbanization transitions from incremental expansion to the optimized utilization of existing construction land, the precise identification of land-use status and changes has become a core requirement for enhancing refined land resource management....
Change detection in remote sensing imagery is vital for Earth monitoring but faces challenges such as background complexity and pseudo-changes. Effective interaction between bitemporal images is crucial for accurate change information extraction. Thi...
Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessme...
To enhance the accuracy of agricultural area classification and enable remote sensing monitoring of agricultural regions, this paper investigates classification models and their application in change detection within rural areas, proposing the MC&...
In recent years, deep learning has become the mainstream development direction in the change-detection field, and its accuracy and speed have also reached a high level. However, the change-detection method based on deep learning cannot predict all th...
Remote sensing image change detection (CD) is an essential technique for analyzing surface changes from co-registered images of different time periods. The main challenge in CD is to identify the alterations that the user intends to emphasize, while...
The detection and monitoring of changes in urban buildings, as a major place for human activities, have been considered profoundly in the field of remote sensing. In recent years, comparing with other traditional methods, the deep learning-based meth...
This paper presents a real-time object-based 3D change detection method that is built around the concept of semantic object maps. The algorithm is able to maintain an object-oriented metric-semantic map of the environment and can detect object-level...
Building change detection involves identifying areas where buildings have changed by comparing multi-temporal remote sensing imagery of the same geographical region. Recent advances in Transformer-based methods have significantly improved remote sens...
Land cover change detection based on remote sensing has become increasingly important for protecting the ecological environment. Spatial features of images can be extracted by object-level methods. However, the computational complexity is high when u...
Simulated data were used to investigate the relationships between image properties and change detection accuracy in a systematic manner. The image properties examined were class separability, radiometric normalization and image spectral band-to-band...
Building change detection using remote sensing images is significant to urban planning and city monitoring. The height information extracted from very high resolution (VHR) satellite stereo images provides valuable information for the detection of 3D...
Hyperspectral image (HSI) change detection plays an important role in remote sensing applications, and considerable research has been done focused on improving change detection performance. However, the high dimension of hyperspectral data makes it h...
We consider measuring the number of clusters (cluster size) in the finite mixture models for interpreting their structures. Many existing information criteria have been applied for this issue by regarding it as the same as the number of mixture compo...
For change detection in remote sensing images, supervised learning always relies on bi-temporal images as inputs and 2-class/multi-class classifiers as outputs. On the other hand, change detection can be viewed as a metric learning problem, i.e., cha...
Cultivated land is a vital resource that underpins human survival and sustainable social development. With the widespread use of high-resolution remote sensing imagery, conventional change detection methods often suffer from limited accuracy due to p...
Aiming at tracking sharply maneuvering targets, this paper develops novel variational adaptive state estimators for joint target state and process noise parameter estimation for a class of linear state-space models with abruptly changing parameters....
Laser point cloud change detection is susceptible to spatial–structural uncertainties induced by residual registration errors, uneven point cloud density, viewpoint shift and object occlusion, which frequently produce numerous false alarms of s...
Heterogeneous change detection refers to using image data from different sensors or modalities to detect change information in the same region by comparing images of the same region at different time periods. In recent years, methods based on deep le...
The purpose of change detection is to recognize changed areas from a pair of two remote sensing images. However, since change areas often include multiple terrain features, this demands enhanced feature extraction capability from the model. This pape...
Software Defined Networking (SDN), as a cutting-edge network, splits control and management planes from the data plane for simplifying network manageability as well as programmability. In SDN, network policies change with the passage of time due to c...
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