Differential evolution algorithm with learning selection strategy for SAR image change detection

Context. The problem of clustering (classification without a teacher) is often occures when processing data arrays of various natures, which is quite an interesting and integral part of artificial intelligence. To solve this problem, there are many known methods and algorithms based on the principles of the distribution density of observations in the analyzed data. However, these methods are rather complicated in software implementation and are not without drawbacks, namely: the problem of determining significant clusters in datasets of different densities, multiepoch self-learning, getting stuck in local extrema of goal functions, etc. It should be noted that the methods based on the analysis of the peaks of the data distribution density are clear in nature, therefore, to expand the capabilities of these methods, it is advisable to introduce their fuzzy modification. Objective. The aim of the work is to introduce fast fuzzy data clustering using density peaks distribution of the datasets, that can find the prototypes (centroids) of clusters that overlapping regardless of the amount of incoming data. Method. The problem of fuzzy clustering data arrays based on a hybrid method that based on the simultaneous use of a credibilistic approach to fuzzy clustering and an algorithm for finding the types of distribution density of the initial data is proposed. A feature of the proposed method is computational simplicity and high speed, due to the fact that the entire array is processed only once, that is, eliminates the need for multi-era self-learning, implemented in traditional fuzzy clustering algorithms. Results. A feature of the proposed method of fast fuzzy credibilistic clustering using of density peaks distribution is characterized by computational simplicity and high speed due to the fact that the entire array is processed only once, that is, the need for multiepoch self-learning is eliminated, which is implemented in traditional fuzzy clustering algorithms. The results of the computational experiment confirm the effectiveness of the proposed approach in clustering problems under conditions in the case when the clusters are ovelap. Conclusions. The experimental results allow us to recommend the developed method for solving the problems of automatic clustering and data classification, as quickly as possible to find the centroids of clusters. The proposed method of fast fuzzy credibilistic clustering using of density peaks distribution of dataset is intended for use in computational intelligence systems, neuro-fuzzy systems, in training artificial neural networks and in clustering problems.

ABSTRACTThe speckle noise of synthetic aperture radar (SAR) images limits its application in change detection. Compared with improved ratio (IR) and log-ratio (LR) operators, the neighborhood-based ratio (NR) technique can restrain the influence of speckle noise and is more suitable for change detection in SAR images. However, we find three drawbacks of NR by analyzing this method carefully. To overcome these defects, we propose an improved neighborhood-based ratio (INR) approach for change detection in SAR images. INR restructures the NR operator to exploit the neighborhood information more reasonably and is expected to reduce the impact of speckle noise better. IR, LR, mean ratio operator, NR, and INR are tested on two data sets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can obtain better difference image than other state-of-art methods and improve the accuracy of change detection in SAR images effectively.

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

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.

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.

With its synoptic view and the repeatability, satellite remote sensing can provide timely, accurate and consistent information about earth’s surface for cost-effective monitoring of environmental changes. In this chapter, recent development in change detection techniques using multitemporal remotely sensed images were reviewed. The chapter covers change detection methods for both optical and SAR images. Various aspects of change detection processes were presented including data preprocessing, change image generation and change detection algorithms such as unsupervised and supervised change detection as well as pixel-based and object-based change detection. The review shows that significant progress has been made in the field of change detection and innovative methods have been developed for change detection using both multitemporal SAR and optical data. Attempts have been made for change detection using multitemporal multisensor/cross-sensor images. The review also identified a number of challenges and opportunities in change detection.

ABSTRACTChange detection in synthetic aperture radar (SAR) images can be made as a matrix factorisation model, and it can detect the changes based on the foreground information in the image. However, these methods cannot obtain satisfactory results in the change detection of SAR images because reliable background data are often not available. In this article, we propose a matrix factorisation model based on a naïve Bayes classifier to explore the low-rank and sparse information, and then detect the changes in SAR images. The factorisation model of the low-rank and sparse matrix extracts both background and foreground information from images. From the low-rank and sparse matrices, we can get the background and foreground information recovered, respectively. Then by computing the mean and variance matrix of the unchanged and changed region information, we will obtain the statistical features. The statistical features are then used to build a naïve Bayes classifier, which is used to distinguish the change detection results, and all of them are based on the acquired data distribution. The experiments, which are based on four real data sets, indicate that the approach gets a better performance than some other state-of-the-art algorithms.

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 (CD) represents a critical task in the analysis of remote sensing (RS) images. In particular, the CD of heterogeneous images that integrate optical and synthetic aperture radar (SAR) modalities has attracted growing attention owing to their complementary characteristics. However, the optical and SAR images (OSIs) CD tasks face specific issues including structural inconsistency between modalities, speckle noise in SAR images, and insufficient utilization of complementary cross-modal features, resulting in limitations in feature representation, temporal causal modeling, and feature fusion for existing methods. Therefore, proposes an end-to-end causal attention and frequency-domain gating fusion enhanced dual-branch OSIs CD network (CAFGFNet). This network adopts a dual-branch encoding architecture with modality-specific designs, where the optical branch emphasizes preserving spectral and texture features, and the SAR branch integrates denoising with deformable convolution to mitigate speckle noise and geometric deformation. On this basis, the Causal Attention (CA) mechanism is introduced to model temporal dependencies to suppress false changes, and the Frequency-domain Gating Fusion Module (FGFM) is designed to adaptively fuse complementary frequency components from optical and SAR features, thus effectively boosting the accuracy and robustness of CD. Compared with six state-of-the-art (SOTA) methods, CAFGFNet has increased the relative suboptimal indicators of Precision by 2.2% and 8.93% on two public datasets. The experimental results show that CAFGFNet can achieve high-precision detection of changes in OSIs while ensuring the full expression and effective fusion of cross-modal features.

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.

In order to effectively solve the instability of clustering results caused by random initial clustering centers of fuzzy clustering, a new method of fuzzy clustering based on improved snake optimization algorithm is proposed. Firstly, the population initialization of the snake optimization algorithm is improved; then the improved algorithm is applied to preprocess the dataset to obtain the initial clustering centers based on the dataset; finally, the generative clustering centers are used to carry out the iterative updating of fuzzy clustering. By comparing with the traditional fuzzy clustering algorithm, the results show that the initial clustering center generated based on the improved snake optimization algorithm can effectively avoid falling into the local optimal solution and has better robustness, which can effectively improve the stability and accuracy of the clustering algorithm.

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.

Compared with the synthetic aperture radar (SAR) image processing theory based on local neighborhood, the nonlocal theory is not limited to a local neighborhood of an image and has great potential in change detection of SAR images. In this study, an approach using ratio-based nonlocal information (RNLI) is proposed for change detection in multitemporal SAR images. First, the RNLI is extracted from a spatial-temporal nonlocal neighborhood where the similarity of two pixels in the nonlocal neighborhood is well characterized by the proposed ratio-based Gaussian kernel function. The parameters of RNLI: noise level and matching window size are adaptively determined to avoid the uncertainty of the change detection result caused by user experience. Second, the difference image is generated by using the RNLI and the ratio operator. Finally, the change map is obtained by segmenting the difference image with a threshold. Experiments conducted on two real datasets and two simulated datasets showed that the proposed method performed better than the other advanced change detection methods, which can better retain the edge information of the changed area while reducing the overall error of the change detection results.

Change detection in remote-sensing images is used to detect changes during different time periods on the surface of the Earth. Because of the advantages of synthetic aperture radar (SAR), which is not affected by time, weather or other conditions, change-detection technology based on SAR images has important research value. At present, this technology has attracted the attention of increasingly more researchers, and has also been used extensively in diverse fields, such as urban planning, disaster assessment, and forest early warning systems. Our objective in this paper is to combine both the change detection of SAR images with the deep neural networks to compare its efficiency with fuzzy clustering method and deep belief network. Our experiments, conducted on real data sets and theoretical analysis, indicates the advantages of the proposed method. Our results appear that proposed deep-learning algorithms can further improve the change-detection process.

In this paper, we propose and present a novel unsupervised change detection(CD) algorithm for synthetic aperture radar(SAR) images based on regularized dictionary learning and fuzzy clustering. The regularized sparse reconstruction technique is introduced to generate a de-noised, low time consuming reconstructed image by using K-SVD dictionary learning. In order to obtain proper difference image, minus and ratio maps are discussed with the comparison of the other state-of-the-art approaches. Finally, to transfer the difference map into change map, we employ the optimized FCM called FLICM algorithm to undertake the task which aims to segment the difference map into two classes: changed and unchanged. Experimental results clearly show that the proposed approach consistently yields superior performance (accuracy, efficiency and robustness) compared to several well-known change detection techniques on both noise-free and noisy satellite images, further optimization methods are discusses in the end.