An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

Speckle noise is a kind of disturbance which appears as granular noise in Synthetic Aperture Radar (SAR) images. It makes the images quite difficult to interpret. Over the years, many filters have been used to mitigate this noise, with limited success. This work will primarily compare the Mean, Median, Guided Image Filter and a proposed filter for their effectiveness in reducing the speckle noise in SAR images. To measure it quantitatively, standard metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) are used. They help in assessing, and making a base to compare these filters. The comparison shows that the proposed filter and the Guided Image Filter, perform better in reducing noise and maintaining the structure of the image compared to older methods like Mean and Median Filters. This study may help in identification of new strategies for reducing speckle noise in SAR images, offering valuable insights for improving SAR image processing.

Non-Gaussian triplet Markov fields (TMF) model is suitable for dealing with multi-class segmentation of non-stationary and non-Gaussian synthetic aperture radar (SAR) images. Considering the complexity of the model and algorithm, as well as the requirement of real-time, and robust and efficient processing of SAR images, a fast algorithm based on TMF for unsupervised multi-class segmentation of SAR images is proposed in this paper. For the speckle noise in SAR images, numerical characteristic, threshold selection and QuadTree decomposition criterion are researched firstly. With the new method, a SAR image can quickly be mapped into an edge-based pixon-representation, which results in a coarse decomposition in smooth regions, and a fine decomposition in edges. Then by combining TMF model with the pixon-representation of SAR image, a new potential energy function of TMF based on pixon-representation is derived. Finally, the segmentation is finished by Bayesian maximum posterior mode (MPM). The effectiveness of the fast TMF algorithm is demonstrated by applying it to simulated data and real SAR images.

Synthetic aperture radar (SAR) image segmentation is the key to SAR image automatic interpretation. However, speckle noise, intensity inhomogeneity, and irregular shaped objects with changing edge often make the SAR image segmentation very difficult, and existing algorithms have high computational complexity. We propose a region-based level set method using the local image intensity information. To represent the statistical characteristics of speckle noise, we first use a gamma statistical distribution to model every segmented SAR image. We then apply a modified region mean estimation formula to efficiently segment SAR images with inhomogeneity. Finally, Gaussian filtering is employed to regularize the level set function, which can avoid reinitialization. The experimental results on synthetic and real-world SAR images demonstrate that the proposed method has less computation cost, faster convergence rate, and more accurate segmentation results.

During forming synthetic aperture radar (SAR) image, the inherent speckle noise severely affects the application result of SAR images. To deal with this problem, based on the contour bougie morphology (CB morphology) and the generalized multiple structuring elements morphology, a novel series morphological filter was constructed to suppress the speckle noise in SAR images. Firstly, by use of the omnidirectional multiple structuring elements the generalized multi-scale morphology open-max filter was adopted to filter the noise at the cost of minimal loss of image geometrical details, and then a CB open and a CB close series filter were used to filter the remained speckle noise. Thus it can not only effectively suppress the speckle noise in images but also preserve more detailed information of the images. Lastly, the simulated SAR images and the actual SAR images are simulated, and the results prove that the algorithm presented in this paper can efficiently suppresses the speckle noise in SAR images.

Synthetic Aperture Radar (SAR) image segmentation stands as a formidable research frontier within the domain of SAR image interpretation. The fully convolutional network (FCN) methods have recently brought remarkable improvements in SAR image segmentation. Nevertheless, these methods do not utilize the peculiarities of SAR images, leading to suboptimal segmentation accuracy. To address this issue, we rethink SAR image segmentation in terms of sequential information of transformers and cross-modal features. We first discuss the peculiarities of SAR images and extract the mean and texture features utilized as auxiliary features. The extraction of auxiliary features helps unearth the distinctive information in the SAR images. Afterward, a feature-enhanced FCN with the transformer encoder structure, termed FE-FCN, which can be extracted to context-level and pixel-level features. In FE-FCN, the features of a single-mode encoder are aligned and inserted into the model to explore the potential correspondence between modes. We also employ long skip connections to share each modality’s distinguishing and particular features. Finally, we present the connection-enhanced conditional random field (CE-CRF) to capture the connection information of the image pixels. Since the CE-CRF utilizes the auxiliary features to enhance the reliability of the connection information, the segmentation results of FE-FCN are further optimized. Comparative experiments conducted on the Fangchenggang (FCG), Pucheng (PC), and Gaofen (GF) SAR datasets. Our method demonstrates superior segmentation accuracy compared to other conventional image segmentation methods, as confirmed by the experimental results.

Synthetic aperture radar (SAR) and optical sensing are two important means of Earth observation. SAR-to-optical image translation (S2OIT) can integrate the advantages of both and assist SAR image interpretation under all-day and all-weather conditions. The existing S2OIT methods generally follow a paired training paradigm, which is difficult when dealing with the unpaired S2OIT application scenarios. Moreover, the generator and discriminator in current S2OIT methods have insufficient scene memory for SAR images, resulting in regional landform deformation in the generated images. To address these issues, we propose a novel generative adversarial network capable of memorizing scene for unpaired S2OIT called MS-GAN. The cycle learning framework based on cycle generative adversarial network for unpaired S2OIT is designed to construct the translation mapping between unpaired SAR and optical images. The multiscale representation generator is constructed for multiscale fusion and utilization of scene features of SAR images. The proposed multireceptive field discriminator has the ability to enhance scene memory and generate higher quality optical images in different landforms. In addition, the designed subbands shrinkage denoising module can further suppress the effect of speckle noise in SAR images on the quality of the generated results. Extensive experiments conducted on three challenging datasets SEN1-2, WHU-SEN-City, and QXS-SAROPT demonstrate that the proposed MS-GAN outperforms the state-of-the-art methods on both subjective and objective evaluation metrics.

Synthetic Aperture Radar (SAR) images are strongly corrupted by the speckle noise due to random electromagnetic waves interference. The speckle noise reduces the quality of images and makes their interpretation and analysis really difficult, so it's necessary to filter images to remove the noise in order to preserve as much as possible the most important features of the signal. To achieve this goal, in this paper we present an efficient method that reduces the speckle noise in SAR images, based on the Contourlet Transform (CT). The CT is a new image decomposition scheme that provides sparse representation of the data, constructed by combining two successive stages, applying in first a Laplacian pyramidal decomposition followed by a directional filter bank. This non-linear approach is designed to give a good representation of the geometrical content of the image. Recently, the Stationary version of the Contourlet Transform (SCT) has been proposed to preserve the shift-invariant property. In the present paper, we explore two different de-noising methods: the Bayesian Shrinkage based on a weighting factor that reduces noise by using the contourlet coefficients, and the Soft Thresholding based on the choice of the threshold that ensures adaptation to the noiseless signals. Hence, we present a comparative study of the results obtained through the SCT considering different stages of decomposition's levels and different kind of filters, and the Lee Adaptive Filter. A performance evaluation is realized to validate our methods.

Synthetic aperture radar (SAR) images are inherently affected by a signal multiplicative noise known as speckle, which is due to the radar wave coherence. In this paper, we introduce a new method to reduce speckle noise in SAR images, based on statistical modeling of wavelet coefficients. We use two-dimensional generalized autoregressive conditional heteroscedasticity (GARCH) model for statistical modeling of images wavelet coefficients. By using this model, we are capable of taking into account important characteristics of wavelet coefficients, such as heavy tailed marginal distribution, and the dependencies between the coefficients. Furthermore, we use maximum a-posteriori (MAP) estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with some denoising methods, and quantify the achieved performance improvement.

Due to the extensive application and good performance of the synthetic aperture radar(SAR), the study to SAR image becomes more and more important. But the intensive analysis to the SAR image is hampered by the speckle noise within it. Based on the knowledge of SAR imaging system, an improved method that uses the adaptive window to suppress the speckle noise in SAR images is proposed in this paper. Compared with the classical fix\|window spatial filtering method, this proposed one can adjust the size of filtering window adaptively according to the statistical of pixels within the window, and it can well suppress the speckle noise in the homogenous area and preserve the detail information in heterogenous area. In simulating experiment, this new method is proved good performance in reducing speckle and preserving edges at the same time.

This paper proposes a new approach for Synthetic Aperture Radar (SAR) image segmentation. Segmenting SAR images can be challenging because of the blurry edges and the high speckle. The segmentation proposed is based on a machine learning technique. Gaussian Mixture Models (GMMs) were already used to segment images in the visual field and are here adapted to work with single channel SAR images. The segmentation suggested is designed to be a first step towards feature and model based classification. The recall rate is the most important as the goal is to retain most target's features. A high recall rate of 88%, higher than for other segmentation methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, was obtained. The next classification stage is thus not affected by a lack of information while its computation load drops. With this method, the inclusion of disruptive features in models of targets is limited, providing computationally lighter models and a speed up in further classification as the narrower segmented areas foster convergence of models and provide refined features to compare. This segmentation method is hence an asset to template, feature and model based classification methods. Besides this method, a comparison between variants of the GMMs segmentation and a classical segmentation is provided.

As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. Deep learning has been demonstrated as an advanced method and technology for SAR image despeckling. Most existing deep-learning-based methods adopt supervised learning and use synthetic speckled images to train the despeckling networks. This is because they need clean images as the references, and it is hard to obtain purely clean SAR images in real-world conditions. However, significant differences between synthetic speckled and real SAR images cause the domain gap problem. In other words, they cannot show superior performance for despeckling real SAR images as they do for synthetic speckled images. Inspired by recent studies on self-supervised denoising, we propose an advanced SAR image despeckling method by virtue of Bernoulli-sampling-based self-supervised deep learning, called SSD-SAR-BS. By only using real speckled SAR images, Bernoulli-sampled speckled image pairs (input–target) were obtained as the training data. Then, a multiscale despeckling network was trained on these image pairs. In addition, a dropout-based ensemble was introduced to boost the network performance. Extensive experimental results demonstrated that our proposed method outperforms the state-of-the-art for speckle noise suppression on both synthetic speckled and real SAR datasets (i.e., Sentinel-1 and TerraSAR-X).

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 paper proposes a novel approach for Synthetic Aperture Radar (SAR) image segmentation by incorporating known statistical properties of SAR into deep learning models. We generate synthetic data using the Generalized Gamma distribution, modify the U-Net architecture to encompass statistical moments, and employ stochastic distance losses for improved segmentation performance. Evaluation against traditional methods will reveal the potential of this approach to advance SAR image analysis, with broader applications in environmental monitoring and general image segmentation tasks.

The nonlocal (NL) means filter as a recent denoising approach has demonstrated its empirical merit for additive Gaussian noise. In this paper, a new nonlocal means despeckling method for synthetic aperture radar (SAR) image is proposed, which is adapted to the multiplicative model of speckle noise. The proposed method still uses Euclidean distance based similarity measure but adopting a strategy of pixel classification, which can effectively reduce the influence of the multiplicative speckle model and improve the effectiveness in searching of similar patches, thus contributes to the final results. By this strategy, image pixels are first classified into different classes such as point, line, edge, etc., and then different smooth parameters of nonlocal means filter are used according to the class information. In addition, a searching method for rotation-invariant similar patches is designed through the use of directional information. We validate the proposed method on real synthetic aperture radar (SAR) images and confirm the excellent despeckling performance through comparisons with other classical despeckling methods, such the Enhanced Lee filter, Enhanced Gamma MAP filter, wavelet thresholding, as well as original NL mean filter.