Change Detection In Radar Images Research Articles

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.

Synthetic aperture radar image change detection using saliency detection and attention capsule network

Synthetic aperture radar image change detection using saliency detection and attention capsule network

Clustering-segmentation network: a parallel dual-branch synthetic aperture radar image change detection framework

ABSTRACT Synthetic aperture radar (SAR) image change detection is a key technique for such essential applications as flood disaster assessment and forest fire detection. In SAR image change detection, clustering algorithm is the most applied methods, but clustering algorithm only considers the grey features, therefore it is more susceptible to speckle noise. Deep learning model is difficult to be trained by supervised learning because of lack of labels. To alleviate the above-mentioned challenges of SAR image change detection, a parallel dual-branch SAR image change detection network based on clustering and segmentation (Clustering-Segmentation Network) is proposed in this paper. In the clustering branch, the clustering-based change detection results were obtained by fuzzy c-means (FCM) clustering. In the segmentation branch, the Graph-Based Image Segmentation algorithm was used for pre-segmentation. These results were used as labels of the neural network for training for the segmentation-based change detection. After the fusion of these dual-branch, a double sparse dictionary (DSD) discrimination algorithm is proposed to extract the neighbourhood features for the final discrimination, and obtain the final results. By fusing the dual-branch results, the influence of speckle noise on change detection can be suppressed while maintaining a high accuracy. We show that the Clustering-Segmentation Network exhibited better results compared with existing algorithms on several datasets. The accuracy and kappa coefficients are improved by 0.83% and 3.45% respectively, thereby proving the effectiveness of our proposed method.

Synthetic Aperture Radar Image Change Detection Based on Difference Image Construction of Log-Hyperbolic Cosine Ratio and Multi-Region Feature Convolution Extreme Learning Machine

Synthetic Aperture Radar Image Change Detection Based on Difference Image Construction of Log-Hyperbolic Cosine Ratio and Multi-Region Feature Convolution Extreme Learning Machine

Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection

Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection

Synthetic aperture radar image change detection based on convolutional‐curvelet neural network and partial graph‐cut

Synthetic aperture radar (SAR) images are widely applied in change detection tasks because of SAR's active imaging mechanism. However, SAR images suffer from speckle noise due to SAR reception coherence from distributed targets. This property of SAR increases the uncertainty of the image pixels, making it difficult to accurately detect the changed regions from the background. To address this issue, this letter proposes a novel SAR image change detection method based on a convolutional-curvelet neural network (CurveCNet) and partial graph-cut. The curvelet transform is introduced into the convolutional neural network to retain the structural information in the SAR images and to suppress the speckle noise. In addition, to combine the merits of various difference images, training samples are jointly constructed and fed into the neural network for it to learn the implicit common hierarchical features. Finally, the partial graph-cut approach is proposed to refine the pixel labels in the fuzzy region and inhibit outliers on the background for the resultant change map. Visual and quantitative results obtained on two real SAR image datasets have demonstrated the effectiveness and robustness of the proposed method.

Unsupervised change detection method in SAR images based on deep belief network using an improved fuzzy C‐means clustering algorithm

Deep learning methods have recently displayed ground-breaking results for synthetic aperture radar image change detection problem. However, they still face the challenges of intrinsic noise and the difficulty of acquiring labeled data. To sort out these issues, we aim to develop a change detection approach specifically designed for analyzing synthetic aperture radar images based on Deep Belief Network as the deep architecture which includes unsupervised feature learning and supervised network fine-tuning. The deep neural networks can reach the final change maps directly from the two original images. A pre-classification based on Morphological Reconstruction and Membership Filtering is employed in order to minimize the effect of noise. Appropriate diversity samples are provided by a virtual sample generation method in order to mitigate overfitting raised by limited synthetic aperture radar data. Visual and quantitative analysis as well as comparisons with advanced algorithms show that our algorithm not only achieves better results but also requires less implementation time.

A novel synthetic aperture radar image change detection system using radial basis function-based deep convolutional neural network

Today, the automatic change detection and also classification as of the Synthetic Aperture Radar (SAR) images remain a hard process. In the existing research, the availability of Speckle Noise (SN), high time-consumption, and low accuracy are the chief issues. To resolve such issues, this paper proposed a novel SAR image change detection system utilizing a Radial Basis Function-based Deep Convolutional Neural Network (RBF-DCNN). The proposed methodology comprises six phases, namely, pre-processing, obtaining difference image, pixel-level image fusion, Feature Extraction (FE), Feature Selection (FS), and also change detection (CD) utilizing the classifier. Initially, the noise is eliminated as of the input, SAR image 1 and SAR image 2, utilizing the NLMSTAF approach. Subsequently, the difference image is attained by utilizing a Log-ratio operator (LRO) and Gauss-LRO, and the attained difference image is then fused. Next, the LTrP, WST, edge, and MSER features are extracted from the fused image. As of those features that were extracted, the necessary features are selected utilizing the Hybrid GWO-GA algorithm. The features (selected) are finally inputted to the RBF-DCNN classifier for detecting the changes in an image. Experimental outcomes established that the proposed work renders better performance on considering the existing system.

SAR Image Change Detection Based on Multiscale Capsule Network

Traditional synthetic aperture radar image change detection methods based on convolutional neural networks (CNNs) face the challenges of speckle noise and deformation sensitivity. To mitigate these issues, we proposed a Multiscale Capsule Network (Ms-CapsNet) to extract the discriminative information between the changed and unchanged pixels. On the one hand, the multiscale capsule module is employed to exploit the spatial relationship of features. Therefore, equivariant properties can be achieved by aggregating the features from different positions. On the other hand, an adaptive fusion convolution (AFC) module is designed for the proposed Ms-CapsNet. Higher semantic features can be captured for the primary capsules. Feature extracted by the AFC module significantly improves the robustness to speckle noise. The effectiveness of the proposed Ms-CapsNet is verified on three real SAR datasets. The comparison experiments with four state-of-the-art methods demonstrate the efficiency of the proposed method. Our codes are available at https://github.com/summitgao/SAR_CD_MS_CapsNet .

Synthetic aperture radar image change detection based on Kalman filter and nonlocal means filter in the nonsubsampled shearlet transform domain

An unsupervised change-detection method specifically oriented to improve the change-detection accuracy of synthetic aperture radar (SAR) images is proposed. This method has four main steps. First, a preprocessing method is proposed based on the Kalman filter. We concentrate on reducing the false detection or missing detection rate caused by only using a single filter. Second, the difference image containing explicit information about the changed region is generated based on a standard log-ratio operator. Third, an improved nonsubsampled shearlet transform (NSST) algorithm based on the nonlocal means (NLM) filter is proposed. Simultaneously, we decompose the difference image into low-frequency and high-frequency subbands by the improved NSST algorithm. In particular, in order to effectively preserve the detailed information while denoising, the NLM filter is used to suppress the noise of the high-frequency subbands. Fourth, the final difference image is obtained by the fuzzy C-means algorithm, which is selected because of its high clustering performance and wide range of applications. Experiments conducted on three real datasets of images demonstrate the effectiveness of the proposed method.

Superpixel cosegmentation algorithm for synthetic aperture radar image change detection

Image segmentation has gained increasing popularity and has been widely applied in various image processing tasks recently. A simple linear iterative clustering (SLIC) superpixel generating algorithm for optical images based on k-means clustering approach works well in terms of computational simplicity and segmentation speed. However, due to the inherent speckle noise, it may generate poor segmentations for synthetic aperture radar (SAR) images. In this paper, an improved similarity measure combining pixel intensity and location similarity with edge information is utilised to replace the Euclidean distance in CIELAB colour space for performing local clustering to generate superpixels by using SLIC. Additionally, a constructed image is used to perform the superpixel cosegmentation, simultaneously segmenting the SAR image pairs. The generated superpixels can be taken as basic units for the subsequent change detection. The experimental results conducted on one simulated dataset and one real-world SAR dataset demonstrate the feasibility and effectiveness of the proposed algorithm.

Synthetic aperture radar image change detection based on frequency-domain analysis and random multigraphs

With the development of earth observation programs, many multitemporal synthetic aperture radar (SAR) images over the same geographical area are available. It is demanding to develop automatic change detection techniques to take advantage of these images. Most existing techniques directly analyze the difference image (DI), and therefore, they are easily affected by the speckle noise. We proposed an SAR image change detection method based on frequency-domain analysis and random multigraphs. The proposed method follows a coarse-to-fine procedure: in the coarse changed regions localization stage, frequency-domain analysis is utilized to select distinctive and salient regions from the DI. Therefore, nonsalient regions are neglected, and noisy unchanged regions incurred by the speckle noise are suppressed. In the fine changed regions classification stage, random multigraphs are employed as the classification model. By selecting a subset of neighborhood features to create graphs, the proposed method can efficiently exploit the nonlinear relations between multitemporal SAR images. The experimental results on two real SAR datasets and one simulated dataset have demonstrated the effectiveness of the proposed method.

Synthetic aperture radar image change detection based on improved bilateral filtering and fuzzy C mean

This paper presents an unsupervised synthetic aperture radar (SAR) image change detection method based on improved bilateral filtering and fuzzy C means (FCM). Many previous approaches to change detection are based on a difference image. Unlike conventional approaches, based on difference images, our method demonstrates superior ability to reduce speckle noise and suppress background information, while still retaining edge information effectively. First, the two images are preprocessed using a Lee filter to remove some of the speckle noise. Second, we use the neighbor-log ratio and the Gauss-log ratio to produce initial change maps. Third, we use the improved bilateral to fuse the two change maps, to obtain an initial difference image. Next, we apply a median filter on the initial difference image, to obtain the final difference image. The above method makes full use of the field information, and it can effectively remove speckle noise while still preserving edge information. Finally, an improved FCM algorithm is used to cluster the denoised difference image. Denoising prior to clustering overcomes the main deficiency of conventional clustering algorithms, which is that they are noise sensitive. Empirical experiments, on three groups of SAR images, suggest that the proposed algorithm outperforms several other methods from the literature, in terms of noise suppression, accuracy, and lower change detection error rates.

Parallel unsupervised Synthetic Aperture Radar image change detection on a graphics processing unit

Change detection is now routinely applied in many application domains, such as damage assessment, environmental monitoring and agricultural surveys. As the number of remote sensing images and the complexity of algorithms rise, the demand for processing power is increasing. In this paper, we propose PLog-FLICM , a parallel algorithm for change detection, which includes two steps: (1) generate the difference image based on the log-ratio operator; (2) detect changes in the difference image by using a modified fuzzy c-means clustering algorithm. PLog-FLICM is implemented on AMD Accelerated Parallel Processing SDK based on Open Computing Language. The parallel characteristics and implementation details of the proposed PLog-FLICM algorithm are presented. Experiments on several Synthetic Aperture Radar images demonstrate that the proposed algorithm outperforms other algorithms, and the designed parallel algorithm can greatly reduce the computational time of the change detection algorithm. Furthermore, we investigate the performance portability of PLog-FLICM in the different central processing unit and graphics processing unit platforms. Experimental results show that they have also achieved good parallel performance.

Swarm intelligence and fractals in dual-pol synthetic aperture radar image change detection

We present a novel systematic method for change detection in dual polarimetric (Dual-pol) synthetic aperture radar (SAR) images based on swarm intelligence techniques and fractal geometry. As the two main algorithms of swarm intelligence, ant colony optimization (ACO) and particle swarm optimization (PSO) have great potential in change detection. Additionally, fractal geometry appears to be a highly effective means of characterizing textural features in Dual-pol SAR images. The proposed method exploits fractal images to form a new difference image. Fractal images are computed based on wavelet multiresolution analysis. Moreover, by minimizing an optimal function value in the iteration process, the changes are detected by applying ACO and PSO to the difference image. Experimental results of detecting changes in Dual-pol SAR images reveal that the proposed method is a highly effective and efficient means of change detection in Dual-pol SAR images.