Change Detection Techniques: A Review

The growing rate of breast cancer necessitates immediate global attention. Mammography images are used to determine the stage of malignancy. Breast cancer stages must be identified in order to save a person's life. This article's main goal is to identify different techniques to obtain the difference between two breast cancer mammography images taken of the same individual at different times. This is the first effort to identify breast cancer in mammography images using change detection techniques. The Mammogram Image Change Detection (ICD) technique is also a recent advancement to prevent breast cancer in the early stage and precancerous level in medical images. The main purpose of this work is to observe the changes between breast cancer images in different screening periods using different techniques. Mammogram Breast Cancer Image Change Detection (MBCICD) methods usually start with a Difference Image (DI) and classify the pixels in the DI into changed and unaffected classes using unsupervised fuzzy c means (FCM) clustering methods based on texture features taken from the log and mean ratio difference pictures. Two operators, mean ratio and log ratio, were used to check the changes in the images. The Gabor wavelet is utilized as a feature extraction technique among several standards. Using the Gabor wavelet ratio operators is a useful method for altering the detection of breast cancer in mammography images. Currently, it is challenging to obtain real malignant images of the same person for testing or training. In this study, two images are utilized. To clearly see the changes, one is an image from the MIAS breast cancer mammography images dataset, and the other is a self-generated change image. The research aims to examine the image results and other quantitative analysis results of proposed change detection methods on cancer images. The Mean Ratio Accuracy result is 0.9738, and the Log ratio PCC is 0.9737. The classification results are the Log Ratio + Gabor Filter + FCM is 0.9737, and Mean Ratio +Gabor Filter + FCM is 0.9719. The mean Ratio Accuracy result is 0.9738, Log ratio is 0.9737. Log Ratio + Gabor Filter + FCM is 0.9737, Mean Ratio +Gabor Filter + FCM is 0.9719. Comparing the PCC of proposed change detection methods with the FDA-RMG method on the same dataset, the accuracy is 0.9481 only. The study concludes that variations in mammography breast cancer images could be successfully identified using the ratio operators with Gabor wavelet features.

Change detection method based on fractal model and wavelet transform for multitemporal SAR images

In this paper, we propose a synthetic aperture radar (SAR) image change detection algorithm based on SAR image nonstationary analysis and conditional random field (CRF) model. This algorithm segments the SAR images into two nonstationary regions with the Triplet Markov Field (TMF) model which incorporates a U field to describe the images' nonstationary nature. The combined posterior probability of the CRF model consists of two parts: the unary potential and the pairwise potential. The unary potential is modeled by support vector machine (SVM) using the texture features extracted from the two-temporal SAR images, and the pairwise potential is constructed by the multilevel logistical model to capture the spatial interactions of the log-ratio image. Both the unary and pairwise potentials are improved with the introduction of the U field. The parameters of the pairwise potential are estimated by the iterative conditional estimation (ICE) algorithm. The iterative conditional model (ICM) algorithm is utilized for updating the combined posterior probability, and the final change detection results are obtained when it converges. Finally, experimental results on four two-temporal SAR images validate the effectiveness of the proposed image change detection algorithm.

Abstract. Satellite image resolution has evolved to daily revisit and sub-meter GSD. Main targets of previous remote sensing were forest, vegetation, damage area by disasters, land use and land cover. Developments in satellite images have brought expectations on more sophisticated and various change detection of objects. Accordingly, we focused on unsupervised change detection of small objects, such as vehicles and ships. In this paper, existing change detection methods were applied to analyze their performances for pixel-based and feature-based change of small objects. We used KOMPSAT-3A images for tests. Firstly, we applied two change detection algorithms, MAD and IR-MAD, which are most well-known pixel-based change detection algorithms, to the images. We created a change magnitude map using the change detection methods. Thresholding was applied to determine change and non-change pixels. Next, the satellite images were transformed as 8-bit images for extracting feature points. We extracted feature points using SIFT and SURF methods to analyze feature-based change detection. We assumed to remove false alarms by eliminating feature points of non-changed objects. Therefore, we applied a feature-based matcher and matched feature points on identical image locations were eliminated. We used non-matched feature points for change/non-change analysis. We observed changes by creating a 5x5 size ROI around extracted feature points in the change/non-change map. We determined that change has occurred on feature points if the rate of change pixels with ROI was more than 50%. We analyzed the performance of pixel-based and feature-based change detection using ground truths. The F1-score, AUC value, and ROC were used to compare the performance of change detection. Performance showed that feature-based approaches performed better than pixel-based approaches.

Change detection using deep learning approach with object-based image analysis

A general and extensible framework for assessing change detection techniques

This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of “changed” areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire’s effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data.

A service relation model for web-based land cover change detection

This study evaluates the performance of a Support Vector Machine (SVM) classifier to learn and detect changes in single- and multi-temporal X- and L-band Synthetic Aperture Radar (SAR) images under varying conditions. The purpose is to provide guidance on how to train a powerful learning machine for change detection in SAR images and to contribute to a better understanding of potentials and limitations of supervised change detection approaches. This becomes particularly important on the background of a rapidly growing demand for SAR change detection to support rapid situation awareness in case of natural disasters. The application environment of this study thus focuses on detecting changes caused by the 2011 Tohoku earthquake and tsunami disaster, where single polarized TerraSAR-X and ALOS PALSAR intensity images are used as input. An unprecedented reference dataset of more than 18,000 buildings that have been visually inspected by local authorities for damages after the disaster forms a solid statistical population for the performance experiments. Several critical choices commonly made during the training stage of a learning machine are being assessed for their influence on the change detection performance, including sampling approach, location and number of training samples, classification scheme, change feature space and the acquisition dates of the satellite images. Furthermore, the proposed machine learning approach is compared with the widely used change image thresholding. The study concludes that a well-trained and tuned SVM can provide highly accurate change detections that outperform change image thresholding. While good performance is achieved in the binary change detection case, a distinction between multiple change classes in terms of damage grades leads to poor performance in the tested experimental setting. The major drawback of a machine learning approach is related to the high costs of training. The outcomes of this study, however, indicate that given dynamic parameter tuning, feature selection and an appropriate sampling approach, already small training samples (100 samples per class) are sufficient to produce high change detection rates. Moreover, the experiments show a good generalization ability of SVM which allows transfer and reuse of trained learning machines.

The majority of pixel-level hyperspectral change detection algorithms have risen out of probabilistic models developed for the data. These algorithms typically operate in two stages. In the first stage, the illumination differences and other changes due to atmospheric and environmental conditions between the two scenes are removed. In the second stage, a hypothesis test is performed on the difference between these normalized pixels. These particular change detection methods often suffer due to local variability within the data. As an alternative to these statistical-based change detection algorithms, this paper examines the use of a parametric physical model towards change detection. For a single hyperspectral data set, the number of unknown parameters in the model is greater than the number of measurements. However, if a second data set exists and the underlying material reflectance of each pixel is assumed to remain constant between the two, one can develop a problem for which the number of measurements is greater than the number of unknowns allowing for application of standard constrained optimization methods for parameter estimation. Assuming the validity of the physical model used, any residual error remaining after obtaining the optimal parameter estimates must result from noise or a violation of the reflectance assumption made, i.e., a change in material reflectance from time-1 to time-2. Accordingly, the fit error for each pixel is an indicator of reflectance change. Additionally, the proposed framework allows for incorporating spatial information at some later point. This paper provides a preliminary look at the proposed change detection method and associated challenges.

This tutorial presents a state-of-the-art survey of advanced applications of Synthetic Aperture Radar (SAR). Applications include: the detection and recognition of targets in SAR imagery, and the detection of targets under trees (and in the open) using change detection (coherent change detection, full-polarization change detection, etc). A fully polarimetric SAR sensor is described. Models of clutter and targets are developed from analysis of full-polarization SAR imagery. Algorithms for optimum (GLRT) processing of full-polarization data are derived, including the polarimetric whitening filter (PWF) and the polarimetric matched filter (PMF). Polarization-based discrimination features (polarization entropy, anisotropy, etc.) are developed and applied to SAR image terrain classification. Model-based and template-based target recognition systems are described and classifier performance results versus resolution and polarization are summarized for 10-and 20-target classifiers. Super-resolution algorithms are applied to SAR imagery and target recognition performance improvements demonstrated. Multi-polarization and multi-pass change detection algorithms are developed and demonstrated using UHF SAR imagery of targets under trees; fusion of multi-polarization coherent and non-coherent change detection algorithms is demonstrated. The effect of SAR image compression on change detection performance is quantified. Change detection performance using multi-pass VHF SAR imagery of targets under trees is demonstrated using data gathered by the Swedish CARABAS SAR system.

The purpose of this study was to examine the influence of image properties on the accuracy of remote sensing change detection methods. Spectral class separability, radiometric normalization and image band correlation were evaluated through experiments with simulated data. The experimental results were then evaluated as a tool for predicting the relative accuracy of change detection results obtained from Landsat TM satellite image pairs of three U.S. cities: Las Vegas, Nevada; Phoenix, Arizona; and Atlanta, Georgia. The change detection methods used were post-classification comparison, direct classification, image differencing, principal component analysis, and change vector analysis. Results of the simulated experiments confirmed that the relative accuracy of the change detection methods varied with changes in image properties. For the class separability experiments, post-classification comparison, direct classification, image differencing, and PCA with a large number of the principal components, were found generally to have higher accuracies than CVA and PCA with a small number of the principal components. For classes with very good separability, image differencing is an excellent method; for classes with poor spectral separability, image differencing was found to have the lowest accuracy. The influence of the error in radiometric normalization on the accuracy of change detection techniques varied greatly with different degrees of class separability. This can be seen particularly well with image differencing, which showed the highest sensitivity to large changes in radiometric error and very poor class separability. Image differencing and PCA were also found to be more sensitive to band correlation. The classification of the real change detection data from the Landsat pairs showed complex and varying patterns, depending on whether complete (mapping all unchanged and changed transitions) or partial (grouping all unchanged transitions into a single class) change analysis was conducted. However, image differencing was relatively consistent in producing good results for the real data. Also, PCA produced satisfactory results for all three cities. On the other hand, the CVA was found to have amongst the lowest accuracies of the partial change detection methods, though this was higher than most of the complete change accuracy. The variation of accuracy results obtained from the change detection methods used in this study suggests that the contradictory results found in previous change detection studies is likely at least partially a result of varying image properties.

This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

Geographic information system (GIS) and change detection techniques were used in this study to evaluate the efficiency of an automatic change detection method in detecting and mapping the changes that took place after the implementation of newly irrigated areas in dry zones. Two satellite images were used: Landsat Thematic Mapper (TM) 1983 and Landsat Enhanced Thematic Mapper Plus (ETM+) 2004. The newly implemented irrigated areas were identified using the differencing pixel-by-pixel change detection method. All of these areas were detected over the period 1983–2004. The accuracy of the results obtained from the change detection method applied was determined by spectral and spatial assessment (image interpretation and field surveying). It was shown that all the newly implemented irrigated areas were detected. The assessment of the areas of the new implemented parcels obtained by the current change detection method showed an error of less than 8%. This result is reasonably good if we consider the speed of the process and its low cost for large zones.

Detecting and monitoring changes in open-pit mines is crucial for efficient mining operations. Indeed, these changes comprise a broad spectrum of activities that can often lead to significant environmental impacts such as surface damage, air pollution, soil erosion, and ecosystem degradation. Conventional optical sensors face limitations due to cloud cover, hindering accurate observation of the mining area. To overcome this challenge, synthetic aperture radar (SAR) images have emerged as a powerful solution, due to their unique ability to penetrate clouds and provide a clear view of the ground. The open-pit mine change detection task presents significant challenges, justifying the need for a model trained for this specific task. First, different mining areas frequently include various features, resulting in a diverse range of land cover types within a single scene. This heterogeneity complicates the detection and distinction of changes within open-pit mines. Second, pseudo changes, e.g., equipment movements or humidity fluctuations, which show statistically reliable reflectivity changes, lead to false positives, as they do not directly correspond to the actual changes of interest, i.e., blasting, collapsing, or waste pile operations. In this paper, to the best of our knowledge, we present the first deep learning model in the literature that can accurately detect changes within open-pit mines using SAR images (TerraSAR-X). We showcase the fundamental role of data augmentations and a coherence layer as a critical component in enhancing the model’s performance, which initially relied solely on amplitude information. In addition, we demonstrate how, in the presence of a few labels, a pseudo-labeling pipeline can improve the model robustness, without degrading the performance by introducing misclassification points related to pseudo changes. The F1-Score results show that our deep learning approach is a reliable and effective method for SAR change detection in the open-pit mining sector.