SAR Image Change Detection Research Articles

Self-supervised image change detection method based on lightweight capsule network

In response to the significant impact of speckle noise on the accuracy of SAR image change detection, the high complexity of existing capsule network based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposes a self supervised image change detection method based on the Light Capsule Network (SCapsNet). First, the log ratio operator difference graph is generated, and the "pseudo label" of training samples with high confidence is obtained through the maximum inter class variance method and fuzzy C-means clustering method, which lays the foundation for self-supervised learning; Secondly, construct a three channel training sample based on the difference map of two temporal SAR images and logarithmic ratio operators to maximize the preservation of sample information; Then, SCapsNet is designed to extract training sample features through single scale convolution, and a single scale capsule network is used to mine spatial relationships between features; Finally, comparative experiments and ablation experiments were set up and tested on 5 real SAR Datasets. The experimental results show that the The advantage of this method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

Contrastive Learning With Context-Augmented Transformer for Change Detection in SAR Images

Contrastive Learning With Context-Augmented Transformer for Change Detection in SAR Images

SAR Image Change Detection via Heterogeneous Graph With Multiorder and Multilevel Connections

SAR Image Change Detection via Heterogeneous Graph With Multiorder and Multilevel Connections

TDSCCNet: twin-depthwise separable convolution connect network for change detection with heterogeneous images

The task of change detection (CD) in optical and SAR images is an ever-evolving and demanding subject within the realm of remote sensing (RS). It holds great significance to identify the target areas by using complementary information between the two. Due to the distinct imaging mechanisms employed by optical and SAR sensors, effectively and accurately identifying changing regions can be challenging. To this end, a novel heterogeneous RS images CD network (Twin-Depthwise Separable Convolution Connect Network, TDSCCNet) is proposed in this paper. Image domain transformation is a front-end task, while the back-end employs a single-branch bilayer depthwise separable convolution-connected encoder-decoder to accomplish CD work. Specifically, first, the cycle-consistent adversarial network (CycleGAN) serves to integrate the optical and SAR visual domains, and a consistent feature expression is obtained. Second, the single-branch encoder structure of bilayer depthwise separable convolution is employed to realize change feature extraction. Finally, the multiscale connected decoder reconstructed by change map is utilized to reconstruct the original images and solve the local discontinuities and holes in the binary change map. Multiscale loss is designated for optimizing the global and local effects to alleviate the class imbalance problem. It was tested on four representative datasets from Gloucester, Shuguang Village, Italy and WV-3 datasets with an overall accuracy of 97.36%, 97.01%, 97.62%, and 98.01% respectively. By comparing the existing methods, experimental results confirmed the effectiveness of the proposed method.

Concatenated Deep-Learning Framework for Multitask Change Detection of Optical and SAR Images

Concatenated Deep-Learning Framework for Multitask Change Detection of Optical and SAR Images

CausalCD: A Causal Graph Contrastive Learning Framework for Self-Supervised SAR Image Change Detection

CausalCD: A Causal Graph Contrastive Learning Framework for Self-Supervised SAR Image Change Detection

Semi-Supervised SAR Image Change Detection via Structure-Optimized Complex-Valued Graph Contrastive Learning

Semi-Supervised SAR Image Change Detection via Structure-Optimized Complex-Valued Graph Contrastive Learning

Multiscale Self-Supervised SAR Image Change Detection Based on Wavelet Transform

Multiscale Self-Supervised SAR Image Change Detection Based on Wavelet Transform

Spatial Correlation-Constrained Low-Rank Modeling for SAR Image Change Detection

Spatial Correlation-Constrained Low-Rank Modeling for SAR Image Change Detection

Wavelet-Based Bi-Dimensional Aggregation Network for SAR Image Change Detection

Wavelet-Based Bi-Dimensional Aggregation Network for SAR Image Change Detection

SAR image change detection based on Gabor wavelets and convolutional wavelet neural networks

SAR image change detection based on Gabor wavelets and convolutional wavelet neural networks

Deep collaborative learning with class-rebalancing for semi-supervised change detection in SAR images

Deep learning reveals excellent potential for accomplishing change detection in SAR imagery. Yet, it suffers from the problem of requiring large amounts of labeled samples, whilst labeling SAR imagery for change detection requires experts to label individual images at the pixel level, which is extremely tedious and time-consuming. Also, sample imbalance continues to present a serious challenge for the existing change detection techniques. To tackle these problems, in this study, a Deep Collaborative semi-supervised learning Framework with Class-Rebalancing (DCF-CRe) is proposed for SAR imagery change detection, by exploiting Convolutional Neural Network (CNN) and deep clustering. In particular, a Siamese Difference Fusion Network (SDFNet) is devised to implement change detection while effectively reducing the information loss due to the generation of difference images and highlighting features of the changed regions.In so doing, only a tiny batch of labeled samples is utilized to train SDFNet in order to obtain predicted change map and deep features. In addition, the Approximate Rank-Order Clustering (AROC) algorithm is employed to cluster the deep features, generating pseudo-labels for abundant unlabeled samples. DCF-CRe is then applied to select appropriate pseudo-labels and to add labeled samples to train SDFNet. Experimental results evaluated on six challenging datasets show that this proposed approach can achieve performance superior to state-of-the-art change detection methods for SAR imagery.

IIQ-CNN-based cross-domain change detection of SAR images

IIQ-CNN-based cross-domain change detection of SAR images

Multidimensional parallel capsule network for SAR image change detection

Multidimensional parallel capsule network for SAR image change detection

Automatic binary and ternary change detection in SAR images based on evolutionary multiobjective optimization

In most of the previous works, changed and unchanged regions are detected by analyzing the changes of backscattering coefficients for SAR images, which is termed as binary change detection. In fact, due to the increase and decrease of backscattering coefficients, the changed regions can be further analyzed as two kinds of changes, which is termed as ternary change detection. In this paper, a change detection method based on evolutionary multiobjective optimization is proposed to automatically perform binary and ternary change detection of multitemporal SAR images. First, the log-likelihood function of the Gaussian mixture model and the Bhattacharyya distance are designed as two objectives, respectively. In particular, a novel measurement method based on Bhattacharyya distance is designed for the ternary change detection task. Not only the separability between each two classes is maximized, but also the Bhattacharyya distance between two changed classes and unchanged class is kept closer to obtain a more balanced classification performance. Then a multiobjective optimization method based on non-dominated sorting is used to optimize these two objectives simultaneously. In the proposed approach, chromosome ranking and perturbation probability selection operators are designed to make high-quality solutions with a high probability of being exploited and improve the performance of the algorithm. In addition, a one-step local search strategy based on the expectation–maximization method is integrated into the proposed algorithm to accelerate the convergence. Experimental results on simulated and real-world datasets demonstrate the effectiveness and robustness of the proposed algorithm.

Deep Shearlet Network for Change Detection in SAR Images

Convolutional neural networks (CNN) can extract shift-invariant features, and have been widely applied in change detection task. However, common CNN lacks noise robustness and needs supervised data, to alleviate these problems, in this paper, we propose a novel deep shearlet network (ShearNet) for change detection in SAR images. In the network, a shearlet denoising layer (SDL) is designed to enhance the representation ability of common CNN. In SDL, feature maps are decomposed into subband coefficients by shearlet transform (ST). Due to optimal sparse representation property and highly direction sensitivity of ST, the network can capture important geometric information. Then, hard-threshold shrinkage is applied to high frequency subbands to drop small coefficients that are most likely to be noise, so that reduce the effect of noise. Finally, ShearNet is trained by introducing a noise-robust loss with noisy labels. The noisy labels are obtained by deep clustering that shows more robustness than existing preclassification methods. This fine-tuning process novelly follows the paradigm of learning from noisy labels to aside the difficulty of precisely labeling samples. Our experimental results on multiple real SAR datasets show that ShearNet can boost accuracy, and have better applicability for change detection in SAR images. The source code is available at https://github.com/yizhilanmaodhh/ShearNet.

A Contrastive Learning Transformer for Change Detection in High-Resolution Sar Images

A Contrastive Learning Transformer for Change Detection in High-Resolution Sar Images

Sar Image Change Detection Based on Saliency Detection and an Attention Capsule Network

Sar Image Change Detection Based on Saliency Detection and an Attention Capsule Network

Change Detection in Water reservoir using Image Fusion and Supervised Classifier

The research provides a method for improving change detection in SAR images using a fusion object and a supervised classification system. To remove noise from the input image, we use the DnCNN denoising approach. The data from the first image is then processed with the mean ratio operator. The log ratio operator is used to process the second image. These two images are fused together using SWT-based image fusion, and the output is sent to a supervise classifier for change detection.

SAR Images Change Detection based on Sparse Coding and NMF

There are many applications for SAR image change detection, from military and agriculture to detection and management. But in fact, there is the speckle noise in SAR images inevitably. Therefore, the difficulty to detect change is increased. For purpose of reducing the interference of noise, we propose an unsupervised feature learning method using the non-negative matrix factorization algorithm and an improved sparse coding algorithm. First, non-negative matrix factorization method is used to obtain a dictionary which contains spatial structure information. Then, in order to increase the discriminate ability, we extract features for each pixel and apply sparse coding. Finally, the result of SAR image change detection is generated by applying simple k-means clustering method to divide the learned features into two different clusters. The superior performance of the proposed method is verified on several real SAR image datasets through comparisons with several existing change detection techniques.