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The synthesis of rain fields is essential in multiple research fields and applications, including Single‐image Derain. However, there is a lack of research on simulated rain fields, and the existing rain field generation models struggle to capture complex spatial distributions and generate truly random rain fields. To address this, the authors propose a generative adversarial networks‐based rain field generation network, which consists of a generator, a discriminator, and a feature extraction block that can produce realistic and complex rain fields. The authors' experiments demonstrate that this method achieves an average Frechet Inception Distance score of 0.035, and user studies indicate that the generated rain distribution looks naturally. [ABSTRACT FROM AUTHOR]

A new denoising technique is proposed in this study for synthetic aperture radar (SAR) images corrupted by speckle noise. The authors method extract informative features from a noisy speckled image, and then a denoised version of this image is estimated from the informative gradients, which are restricted to the features of this image. The technique of extracting features is designed on the framework of multifractal formalism followed by a reconstruction technique for the informative gradients based on the total variational (TV) regularisation framework. Experimental results demonstrate that the proposed approach is able to retain the finer details of the original image while removing noise. The superiority of the proposed approach is manifested qualitatively and quantitatively on comparing with state‐of‐the‐art denoising techniques. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) multi‐target interactive motion recognition classifies the type of interactive motion and generates descriptions of the interactive motions at the semantic level by considering the relevance of multi‐target motions. A method for SAR multi‐target interactive motion recognition is proposed, which includes moving target detection, target type recognition, interactive motion feature extraction, and multi‐target interactive motion type recognition. Wavelet thresholding denoising combined with a convolutional neural network (CNN) is proposed for target type recognition. The method performs wavelet thresholding denoising on SAR target images and then uses an eight‐layer CNN named EilNet to achieve target recognition. After target type recognition, a multi‐target interactive motion type recognition method is proposed. A motion feature matrix is constructed for recognition and a four‐layer CNN named FolNet is designed to perform interactive motion type recognition. A motion simulation dataset based on the MSTAR dataset is built, which includes four kinds of interactive motions by two moving targets. The experimental results show that the recognition performance of the authors' Wavelet + EilNet method for target type recognition and FolNet for multi‐target interactive motion type recognition are both better than other methods. Thus, the proposed method is an effective method for SAR multi‐target interactive motion recognition. [ABSTRACT FROM AUTHOR]

The scale‐invariant feature transform (SIFT) algorithm is the most widely used feature extraction as well as a feature matching method in remote sensing image registration. However, the performance of this algorithm is affected by the influence of speckle noise in synthetic aperture radar (SAR) images. It reduces the number of correct matches as well as the correct matching rate in SAR image registration. Moreover, SAR image registration is considered to be a challenging task as the images generally have significant geometric as well as intensity variations. To address these problems, a structure tensor‐based SIFT algorithm is proposed to register the SAR images. At first, the tensor diffusion technique is used to construct the scale layers. Then, the features are extracted in the scale layers. Finally, feature matching is performed between the input SAR images and correct matches are identified. The proposed method can increase the number of correct matches as well as position accuracy in registration. Experiments have been conducted on five SAR image pairs to verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

The traditional range Doppler (RD) imaging algorithm has become the most intuitive and classical method in synthetic aperture radar (SAR) imaging processing because of its easy implementation and high processing efficiency. However, owing to the poor quality of the image produced by RD algorithm, this traditional imaging method is growing increasingly unable to meet practical needs. In an effort to solve the problem of poor azimuth imaging quality, this study proposes a high‐resolution RD azimuth imaging algorithm. The expression of the optimal order of the SAR azimuth signals using fractional Fourier transform (FrFT) is derived in detail herein. A theoretical analysis shows that this optimal order is unique and depends on the azimuth imaging parameters. The experimental results based on airborne SAR simulation data and spaceborne SAR measurement data indicate that the azimuth resolution of the proposed algorithm is improved by 31.6 or 46.8% as against that of the traditional RD algorithm, whereas the peak side lobe ratio (PSLR) and integrated side lobe ratio (ISLR) values obtained via the proposed algorithm are approximately equivalent to those obtained via the traditional RD algorithm. [ABSTRACT FROM AUTHOR]

Speckle noise is an undesirable phenomenon that exhibits granular pattern which reduces the diagnostic capability of clinical ultrasound (US) images. In this study, a new speckle denoising method based on modelling of detail band shearlet coefficients of log‐transformed US images is presented. In each detail shearlet subband, coefficients corresponding to signal and speckle noise are modelled as normal inverse Gaussian and Gaussian priors, respectively. These coefficients, based on image local statistics are segmented into different regions of heterogeneity viz. homogeneous, heterogeneous and strongly heterogeneous regions, respectively, so as to control over smoothing of denoised images. Then, using the prior distributions maximum a posteriori (MAP) estimation is performed over all regions of detail bands except those regions that represent strongly heterogeneous coefficients. For better performance, an adaptive weight function is also used in the MAP expression which reduces the loss of feature information. Experimentation is done on noise‐free synthetic kidney and foetus US images and a set of 60 real US images. The results are presented for objective and subjective quality assessment of the proposed method and five other methods for speckle denoising. The potential of the proposed method in comparison to other methods can easily be ascertained from the obtained denoising results. [ABSTRACT FROM AUTHOR]

Optical‐to‐synthetic aperture radar (SAR) image registration is a challenging task in remote sensing as the images have significant non‐linear intensity variations as well as large geometric differences. Moreover, the influence of speckle noise in the SAR image further affects the registration result. The structural descriptors are very effective to handle the non‐linear intensity variations between optical and SAR images. Although a number of optical‐to‐SAR image registration methods have been proposed in the past few years based on the structural information of image, most of them are ineffective for the images having large geometric differences. To address these problems, a novel optical‐to‐SAR image registration algorithm is proposed by using a new structural descriptor. Initially, the corner features are extracted from the optical and SAR images. Then, the proposed structural descriptor is constructed for the extracted features. Finally, feature matching is performed between the optical and SAR images and the correct match are identified. The proposed method is very effective to register the optical and SAR images having significant intensity variations and large geometric differences. It can increase the number of correct match between the images. Experiments on six sets of optical and SAR image pairs demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Deforestation has become a major problem consisting of a continuous regression of forested areas in the world, and for this purpose, an efficient detection of these changes has become more than necessary. In this work, a new method for deforestation change detection is proposed. This approach is based on a supervised fusion of local texture features extracted from SAR images. ALOS PALSAR (Advanced Land Observation Satellite Phased Array type L‐band Synthetic Aperture Radar) multi‐temporal data have been used in this work. Normalised radar cross‐section (NRCS) and polarimetric features extracted from HH and HV polarised data allowed recognising different categories of land covers termed as NRCS classification. Grey‐level co‐occurrence matrix (GLCM) texture features were extracted by using a different moving window sizes applied on local regions previously obtained by binarisation of the NRCS results. A total of 300 samples of regions and five GLCM characteristics have been used here. The detection of deforestation appears clearly in the resulted images with a very satisfactory precision of the reached regions, and the obtained results of the proposed supervised approach have indeed led to very good detection results of the deforestation change. [ABSTRACT FROM AUTHOR]

In solving change detection problem, unsupervised methods are usually preferred to their supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this paper, a supervised deep learning‐based method is presented for change detection in synthetic aperture radar (SAR) images. A Deep Belief Network (DBN) was employed as the deep architecture in the proposed method, and the training process of this network included unsupervised feature learning followed by supervised network fine‐tuning. From a general perspective, the trained DBN produces a change detection map as the output. Studies on DBNs demonstrate that they do not produce ideal output without a proper dataset for training. Therefore, the proposed method in this study provided a dataset with an appropriate data volume and diversity for training the DBN using the input images and those obtained from applying the morphological operators on them. The great computational volume and the time‐consuming nature of simulation are the drawbacks of deep learning‐based algorithms. To overcome such disadvantages, a method was introduced to greatly reduce computations without compromising the performance of the trained DBN. Experimental results indicated that the proposed method had an acceptable implementation time in addition to its desirable performance and high accuracy. [ABSTRACT FROM AUTHOR]

Interferometric phase filtering represents an indispensable step in interferometric synthetic aperture radar (InSAR) data processing. However, conventional filtering methods fail to make a balance between the noise elimination and phase preserving in strong noise environments or dense fringe regions. This in turn leads to an inaccurate interferometric phase. To overcome this problem, the authors introduce the concept of slope‐compensated filter based on the local adaptive frequency estimation. This method uses the estimation of signal parameters via rotational invariance techniques (ESPRITs), based on the singular value decomposition of the correlation matrix and generalised eigenvalue solution for providing an accurate local fringe frequency. Meanwhile, the size of the estimation window is adaptively determined by the coherence coefficient, and the judgement and modification of the invalid frequency estimation are utilised to further improve the accuracy of the local fringe frequency. Moreover, the authors consider a two‐stage with the back projection technique to further eliminate the noise residual and improve the filtering performance. The simulated and TerraSAR‐X add‐on for Digital Elevation Measurements (TanDEM) data sets are used to verify the effectiveness of the proposed method. The experimental results show that this method is capable of efficiently reducing the noise level as well as minimising the loss of the signal, outperforming other conventional methods. [ABSTRACT FROM AUTHOR]

There have been many published articles describing Interferometry algorithms for DEM generation in different applications. The best uncertainty achieved in most of these articles is greater than ±1 m calculating the elevation. Enabling scientists to gain experience with new techniques at low cost. Interferometry with synthetic aperture radar (InSAR) is one of the best technique for measuring the altimetry surface, its different changes and degradation over time, and the characteristics detailed of the surface. To take a better resolution of the surfaces, the authors propose their approach; it's to merge the results of the interferometry and data from segmentation of an optical high‐resolution image. In this report, they describe a procedure for hybridisation of interferometry algorithm (InSAR) and the use of geospatial data from the image segmentation, with extraction of the building to calculate the zonal statistic parameter and mapping all segments comprising the highest elevations. This report is written in a step by step format to simplify the complicated steps described in this study, with a focus on the hybridisation instead of the interferometry. The tests of this method were carried out on several sites, and showed good results. [ABSTRACT FROM AUTHOR]

Combining the advantage of Contourlet transform with adaptive fuzzy Markov random field (MRF) model, a novel segmentation algorithm is proposed in this study to achieve precise and continuous division for synthetic aperture radar (SAR) image. The classical MRF model is modified to obtain the edge and process adaptive label factor. The adaptive fuzzy MRF (AFMRF) model is proposed to equilibrate the smooth regions segmentation with texture regions segmentation. At the same time, Contourlet domain hidden Markov tree (HMT) model is introduced to perform multi‐scale directional filtering and intra‐scale training on the coefficients of SAR images to achieve precise texture segmentation at each scale. Finally, the AFMRF model is integrated into inter‐scale and intra‐scale HMT training results and the segmented image can be obtained. To verify the validity of the proposed algorithm, several SAR images are experimented and compared with the state‐of‐the‐art algorithms. The experimental results and analysis show that the proposed algorithm can achieve better results on noise suppression, smoothness of target regions, precise and continuous segmentation of fuzzy texture. [ABSTRACT FROM AUTHOR]

Side scan sonar (SSS) image matching plays an important role in underwater applications, such as combining images to form wide range relief images, underwater simultaneous location and mapping, and construction of images of underwater terrain and objects. Because there are differences between the imaging mechanisms of SSS images and optical images, current matching algorithms for optical images do not always work well for SSS images. This study proposes a combined image matching approach for SSS images. First, the images are preprocessed to remove some environmental effects. Second, feature points that are stable for affine transformations are extracted from the SSS matching images based on speeded‐up robust features with prior position knowledge. Third, geometric correction is made by the random sample consensus. After that, a similarity calculation is performed. The proposed approach speeds up the matching process and improves its accuracy by combining feature points matching and similarity calculation. It reduces the mismatching rate and the computation requirement by estimating the location uncertainty with prior knowledge and reducing the searching regions for image matching. Experiments show that the matching algorithm takes less time and has greater reliability and accuracy than traditional algorithms. [ABSTRACT FROM AUTHOR]

For high‐resolution synthetic aperture radar (SAR), constant false alarm rate (CFAR) detectors are widely used to separate targets from background and it is proved that generalised gamma distribution (GΓD) can model the non‐homogeneous clutter appropriately. However, CFAR detectors based on GΓD possess two problems. First, the methods for solving highly non‐linear equations of GΓD resort to numerical iterative algorithms, which are computationally expensive. To avoid this, the authors present a novel analytic solution for parameter estimation of GΓD by exploiting a third‐order approximation of the polygamma function. This novel analytic solution can result in more accurate parameter estimation and can fit a wider range of the parameters. The second problem is that in a multi‐target SAR image, some target pixels may be classified as clutter pixels. To select pixels of interest, an iterative sliding window approach is often used in CFAR. They analyse the relationship between the detection probability and the number of iterations, and prove that this strategy can reduce the miss rate and false detection rate effectively. On the basis of the aforementioned parameter estimation method and the iterative sliding window approach, an innovative and effective CFAR detector is proposed in this study and its superiority is demonstrated by experiments. [ABSTRACT FROM AUTHOR]

Triplet Markov fields (TMF) model is widely used to deal with non‐stationary synthetic aperture radar (SAR) images. However, its ability to capture global information remains limited due to the non‐causal property. A hierarchical TMF model is proposed in this study based on the non‐linear diffusion (ND) strategy, which is denoted as ND‐hierarchical TMF (HTMF). ND is adopted to generate multiscale decomposition according to local image content, and that is superior to traditional wavelet decomposition in reflecting hierarchical nature of image structure and detailed features. The auxiliary field in ND‐HTMF is redefined and initialised on the finest scale to characterise edge information and that enhances the prior modelling ability for non‐stationary local image features. The multiscale likelihood and multiscale causal prior energy functions are then defined respectively in bottom‐up and top‐down procedures to capture local and global information for performing segmentation. Segmentation experiments on simulated and real SAR images demonstrate the effectiveness of ND‐HTMF in both edge characterisation accuracy and robustness against speckle noise. [ABSTRACT FROM AUTHOR]

Since inverse synthetic aperture radar (ISAR) imaging is a valuable technique in the identification of space satellites, it can potentially detect interesting components of space satellites in ISAR images to further conduct identification. This study proposes a novel method, defined as feature probabilistic estimation (FPE), to detect interesting components of space satellites based on ISAR image registration. In FPE, area feature registration is provoked to establish the relationship between space satellites and off‐line templates of interesting components, followed by detection accuracy based on weighted Gaussian probabilistic density function. Electromagnetic simulations with different aspects, interesting components' structures and scenery noise demonstrate the efficiency and robustness of the proposed FPE, compared with the normalised cross coefficient. [ABSTRACT FROM AUTHOR]

Speckle reduction is an important task in synthetic aperture radar. One extensively used approach is based on total variation (TV) regularisation, which can realise significantly sharp edges, but on the other hand brings in the undesirable staircasing artefacts. In essence, the TV‐based methods tend to create piecewise‐constant images even in regions with smooth transitions. In this study, a new method is proposed for speckle reduction via total generalised variation (TGV) penalty. This is reasonable from the fact that the TGV‐based model can reduce the staircasing artefacts of TV by being aware of higher‐order smoothness. An efficient numerical scheme based on the Nesterov's algorithm is also developed for solving the TGV‐based optimisation problem. Monte Carlo experiments show that the proposed scheme yields state‐of‐the‐art results in terms of both performance and speed. Especially when the image has some higher‐order smoothness, the authors' scheme outperforms the TV‐based methods. [ABSTRACT FROM AUTHOR]

High‐resolution millimetre wave images of contiguous targets often suffer from the influence of sidelobe artefacts, partial correlation between targets and so on. Owing to the characteristics of near‐field synthetic aperture imaging radiometers [such as the changing point spread function (PSF) and slender sideline], the existing CLEAN algorithms are unsuitable for near‐field synthetic aperture imaging. This study is devoted to establishing a novel CLEAN algorithm (named adaptive CLEAN) to clean the reconstructed millimetre wave images accurately. First, the characteristics of near‐field synthetic aperture imaging are studied. Then, the adaptive CLEAN algorithm is established based on these characteristics. In this study, the authors amend the amplitude of the targets and select the matching PSF for them according to their azimuths. Unlike other CLEAN algorithms, the parameters of this adaptive CLEAN algorithm are calculated by a formula, which is concluded from a lot of simulation experiments. Finally, the effectiveness of the proposed adaptive CLEAN algorithm is tested by several simulation experiments, and the superiority is also demonstrated by comparing it with the existing CLEAN algorithm. The results demonstrate that the proposed method is an efficient, feasible algorithm for cleaning the reconstructed images, irrespective of the point or contiguous targets. [ABSTRACT FROM AUTHOR]

The recently proposed triplet Markov random fields (TMF) model is very suitable for dealing with non‐stationary image segmentation. However, influenced by multiplicative speckle noise, synthetic aperture radar image (SAR) is dim and blurred in the boundaries of different areas, making it difficult to locate boundary accurately in the segmentation process. Thus, in this study, the authors propose a new segmentation algorithm using fuzzy label field‐based TMF model for SAR images. In the proposed algorithm, the value of each site in the label field is extended from a finite discrete set in the classical TMF model to a continuous one, in order to describe the memberships of each pixel to different classes. A fuzzy energy function is constructed to describe the joint prior distribution of the fuzzy label field and the auxiliary field. The construction of fuzzy energy function also takes into account four direction information and degree of difference between neighbouring pixels. Iterative conditional estimation method and maximum posterior mode criterion are applied to implement parameter estimation and segmentation. Experimental results on simulated data and real SAR images demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) appropriate image processing in conjunction with noise reduction is crucial in proper image segmentation. The authors present a new algorithm, logarithmic adaptive soft morphological (LASM) filter, utilising collectivity and flexibility of order‐statistic soft morphological filters. This method not only reduces the speckle noise of the single‐look SAR imagery considerably, but it significantly enhances the segmentation results. To verify the performance, a simulated SAR image is first created by applying an imagery method to an original noiseless image. The resulting image has characteristics identical to a real SAR image. The LASM method, as well as several well‐known and state‐of‐the‐art filtering algorithms, is then applied to the image to filter out the speckles. The LASM filtered results are then compared with other results by evaluating them against the original image. Some of these methods are also applied to a real SAR image and the results are also compared. Segmentation is then applied to the filtered images and the results are used as a deterministic evaluation metric. The results show marked improvement using the LASM processing the amplitude images compared with well‐known methods, whereas the superiority of LASM algorithm is even more pronounced in processing the intensity images. [ABSTRACT FROM AUTHOR]

This study proposes two adaptive vectorial total variation models for multi‐channel synthetic aperture radar (SAR) images despeckling with the help of prior knowledge of the image amplitude. Besides despeckling the multi‐channel SAR images efficiently, the proposed new models have advantages over other total variation methods in many aspects, such as preserving the radar reflectivity, the targets and edges contrast. The Bermudez‐Moreno algorithm and the accelerated fast iterative shrinkage thresholding algorithm are employed to implement the new two models, respectively. Experimental results on multi‐polarimetric, multi‐temporal RADARSAT‐2 images show that the visual quality and evaluation indexes of the proposed models and the corresponding algorithms outperform the other methods with edge preservation. [ABSTRACT FROM AUTHOR]

In image coregistration of synthetic aperture radar (SAR) interferometry, a set of points is selected for tie point matching. Generally, some special points are possibly selected as tie points to improve the reliability of coregistration. However, special points cannot always be found in an image. Therefore points in grid form are commonly selected for image coregistration and this makes the results not as reliable as special points do. In this study, hence, a series of points detected by Harris interest operator (HIO) are used as tie points for SAR image coregistration. After wavelet decomposition of a SAR image, the basic energy is reserved in the low‐pass subimage and this benefits the extraction of interest points conducted by HIO on the highest level. Three pairs of SAR image in Hong Kong area are used to prove the efficiency of the proposed method. For comparison, image coregistration based on grid points and interest points are implemented, respectively, in which different numbers of points are adopted. Based on the analysis of experimental results, it is found that the quality of interferogram is greatly improved by interest points and coregistration with interest points is more reliable. [ABSTRACT FROM AUTHOR]