Employing a Method on SAR and Optical Images for Forest Biomass Estimation

Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

This paper proposes a speckle-suppression method for ocean internal solitary wave (ISW) synthetic aperture radar (SAR) images by using the curvelet transform. The band-shaped signatures of ocean ISWs in SAR images show obvious scale and directional characteristics. The curvelet transform possesses a very high scale and directional sensitivity. Therefore, the curvelet transform is very efficient in analyzing wave signals in SAR images. A noisy ocean ISW SAR image can be decomposed at different scales, directions, and positions using the curvelet transform. The information of the ISWs is centralized in the curvelet coefficients of certain directions under certain scales, whereas the speckle noise is distributed in every scale and direction. By manipulating the curvelet coefficients, the signals of the ISWs can be extracted from the noisy SAR image. Finally, the speckle noise is suppressed and the ISW feature is enhanced by adding the signals of the ISWs back to the original SAR image. Experiments demonstrate the effectiveness of this method.

The quality of synthetic aperture radar (SAR) images generally suffers from speckle noise, which damages the radiometric resolution of SAR images. A new speckle reduction method is proposed by thresholding the speckled SAR image coefficients in the Shear let transform domain. As a powerful multiscale image representation tool, Shear let tranform enables better preservation of significant detail information in despeckled results. Compared with the wavelet transform, Shear let transform is more suitable for image restoration applications where detail preservation is highly demanding. Experimental results on real X-band amplitude and intensity SAR images demonstrate the efficacy of the proposed method. A quantitative analysis is also given to show the superiority of the Shear let-based thresholding method over the translation-invariant wavelet-based counterpart.

Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. In this paper, we proposed a speckle reduction technique based on sparse representation and dictionary learning. Firstly, an adaptive dictionary was learned by performing KSVD algorithm through a large amount of training patches extracted from the noisy SAR image. Considering the inaccurate recovery of point targets which is brought by the inadequate number of training samples, we employed a point target enhancing scheme to highlight the important point targets in the SAR image. Some experiments were conducted on real SAR images, and the results shows that our proposed algorithm can effectively reduce the speckle noise as well as preserve details. Some comparisons are made to prove its superiority to the available algorithms.

The unavoidable noise often present in synthetic aperture radar (SAR) images, such as speckle noise, negatively impacts the subsequent processing of SAR images. Further, it is not easy to find an appropriate application for SAR images, given that the human visual system is sensitive to color and SAR images are gray. As a result, a noisy SAR image fusion method based on nonlocal matching and generative adversarial networks is presented in this paper. A nonlocal matching method is applied to processing source images into similar block groups in the pre-processing step. Then, adversarial networks are employed to generate a final noise-free fused SAR image block, where the generator aims to generate a noise-free SAR image block with color information, and the discriminator tries to increase the spatial resolution of the generated image block. This step ensures that the fused image block contains high resolution and color information at the same time. Finally, a fused image can be obtained by aggregating all the image blocks. By extensive comparative experiments on the SEN1–2 datasets and source images, it can be found that the proposed method not only has better fusion results but is also robust to image noise, indicating the superiority of the proposed noisy SAR image fusion method over the state-of-the-art methods.

Visual and automatic analyses using synthetic aperture radar (SAR) images are challenging because of inherently formed speckle noise. Thus, reducing speckle noise in SAR images is an important research area for SAR image analysis. During speckle noise reduction, homogeneous regions should be smoothed while details such as edges and point scatterers need to be preserved. General speckle noise model contains gamma distributed multiplicative part which is dominant and Gaussian distributed additive part which is in low amount and mostly neglected in literature. In this study, a novel sparsity-driven speckle reduction method is proposed that takes both multiplicative noise model and additive noise model into consideration. The proposed speckle reduction method uses a cost function with multiplicative and additive data terms besides the total variation smoothness term. Also, an efficient and stable numerical minimization scheme is proposed for the proposed cost function that deals with multiplicative and additive noise. Speckle reduction performance of the proposed method is shown on synthetically generated SAR images and real-world SAR images.

Multiplicative speckle noise diminishes the radiometric resolution of the synthetic aperture radar (SAR) images and all the coherent images. Speckle removal adds an extra value to an automated SAR image interpretation and analysis. In this paper, dual-tree complex wavelet-transform-based Bayesian method is proposed for despeckling the SAR images. In each subband, the reflectance and noise of the logarithmically transformed wavelet coefficients are modeled using heavy-tailed Burr and zero-mean Gaussian distributions. The closed-form expression for the shape parameter of Burr distribution is derived by employing the Mellin transform. The resultant complex-free quadratic maximum a posteriori solution with suitable shrinkage function yields despeckled SAR images. Extensive experiments are carried out using real SAR images as well as simulated images. The proposed method performs well in terms of equivalent number of looks with 3.5751 dB improvement in homogeneous region1 of Pipe river SAR image, edge preservation with 0.6158 improvement, peak signal to noise ratio of 51.3305 dB, and mean structural similarity index measure of 0.9397 at 0.05 noise variance for synthetically speckled image in comparison to the existing methods and takes averagely 2.3461 times less computing time. The proposed method provides a computationally efficient better speckle reduction in homogeneous regions while still preserving the edge.

In the domain of synthetic aperture radar (SAR) image processing, a prevalent issue persists wherein research predominantly focuses on single-task learning, often neglecting the concurrent impact of speckle noise and low resolution on SAR images. Currently, there are two main processing strategies. The first strategy involves conducting speckle reduction and super-resolution processing step by step. The second strategy involves performing speckle reduction as an auxiliary step, with a focus on enhancing the primary task of super-resolution processing. However, both of these strategies exhibit clear deficiencies. Nevertheless, both tasks jointly focus on two key aspects, enhancing SAR quality and restoring details. The fusion of these tasks can effectively leverage their task correlation, leading to a significant improvement in processing effectiveness. Additionally, multi-temporal SAR images covering imaging information from different time periods exhibit high correlation, providing deep learning models with a more diverse feature expression space, greatly enhancing the model’s ability to address complex issues. Therefore, this study proposes a deep learning network for integrated speckle reduction and super-resolution in multi-temporal SAR (ISSMSAR). The network aims to reduce speckle in multi-temporal SAR while significantly improving the image resolution. Specifically, it consists of two subnetworks, each taking the SAR image at time 1 and the SAR image at time 2 as inputs. Each subnetwork includes a primary feature extraction block (PFE), a high-level feature extraction block (HFE), a multi-temporal feature fusion block (FFB), and an image reconstruction block (REC). Following experiments on diverse data sources, the results demonstrate that ISSMSAR surpasses speckle reduction and super-resolution methods based on a single task in terms of both subjective perception and objective evaluation metrics regarding the quality of image restoration.

Road detection of synthetic aperture radar (SAR) image is an important part of SAR image interpretation. Thus, in the influence of sophisticated background and speckle noise, it is hard to directly extract the road information from the SAR image. So a SAR image registration method using existing geographic information system (GIS) road data is proposed to assist road detection for SAR image in this paper. By making full use of road geometry structure information, a road structure support is defined. Then all the positions in the SAR image are searched to find the biggest structure support as the optimum matching centre. The image registration for SAR image and road vector data is finally realised. Experimental results show that the registration accuracy of the proposed method is ∼2 pixels, which can be used for shoreline extraction, target positioning and so on.

<p>The velocity of glaciers is commonly derived by offset tracking using pairwise cross correlation or feature matching of either optical or synthetic aperture radar (SAR) images.  SAR images, however, are inherently affected by noise-like radar speckle and require therefore much larger images patches for successful tracking compared to the patch size used with optical data. As a consequence, glacier velocity maps based on SAR offset tracking have a relatively low resolution compared to the nominal resolution of SAR sensors. Moreover, tracking may fail because small features on the glacier surface cannot be detected due to radar speckle. Although radar speckle can be reduced by applying spatial low-pass filters (e.g. 5x5 boxcar), the spatial smoothing reduces the image resolution roughly by an order of magnitude which strongly reduces the tracking precision. Furthermore, it blurs out small features on the glacier surface, and therefore tracking can also fail unless clear features like large crevasses are visible.</p><p>In order to create high resolution velocity maps from SAR images and to generate speckle-free radar images of glaciers, we present a new method that derives the glacier surface velocity field by correlating temporally averaged sub-stacks of a series of SAR images. The key feature of the method is to warp every pixel in each SAR image according to its temporally increasing offset with respect to a reference date. The offset is determined by the glacier velocity which is obtained by maximizing the cross-correlation between the averages of two sub-stacks. Currently, we need to assume that the surface velocity is constant during the acquisition period of the image series but this assumption can be relaxed to a certain extend.</p><p>As the method combines the information of multiple images, radar speckle are highly suppressed by temporal multi-looking, therefore the signal-to-noise ratio of the cross-correlation is significantly improved. We found that the method outperforms the pair-wise cross-correlation method for velocity estimation in terms of both the coverage and the resolution of the velocity field. At the same time, very high resolution radar images are obtained and reveal features that are otherwise hidden in radar speckle.</p><p>As the reference date, to which the sub-stacks are averaged, can be arbitrarily chosen a smooth flow animation of the glacier surface can be generated based on a limited number of SAR images. The presented method could build a basis for a new generation of tracking methods as the method is excellently suited to exploit the large number of emerging free and globally available high resolution SAR image time series.</p>

Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the images and makes interpretation, analysis and classification of SAR images harder. Therefore, some speckle reduction is necessary prior to the processing of SAR images. The speckle noise can be modeled as multiplicative i.i.d. Rayleigh noise. Sveinsson and Benediktsson [1996], proposed an adaptive sigmoid thresholding method for SAR images in the wavelet domain. The coefficients thresholding for this method is based on the choice of parameters in the sigmoid thresholding function. They were chosen according to a visual appreciation, i.e., by an <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> method. We propose to select these parameters by minimizing an estimate of square error between the clean image and the denoised one. The key point is that we have in our proposal computable, statistically unbiased, MSE estimate - Stein's Unbiased Risk Estimate (SURE) - that depends on the noisy image alone, not on the clean image. We apply the proposed method on an SAR images, both simulated and real data.

Nowadays Synthetic Aperture Radar (SAR) images are used extensively for various important applications like terrain navigation , land cover classification, environment monitoring like oil spill detection, flood detection, military surveillance etc. SAR images get corrupted by the speckle noise, which appears as bright and dark spots on images and hence the visual quality of the images get degraded. This leads to the improper functioning of various feature extraction and classification algorithms which are used to classify SAR images into desired classes depending on the nature of applications. So removal of speckle noise from SAR images is one of the crucial steps in the pre-processing of SAR images. Although many despeckling filters have been proposed by various researchers working in this field till date but still there is a need for a filter that can deal with all the constraints associated with the process. In this paper we have discussed and summarized all the advantages and technicalities associated with various filters and have also performed a comparative study of the results obtained by performing qualitative and quantitative analyses of output images generated by applying various filtering algorithms on same set of noisy SAR images.

A hierarchical method is proposed for synthetic aperture radar (SAR) and optical image coregistration. We use local invariant salient points extracted by the binary robust invariant scalable keypoints (BRISK) algorithm for SAR and optical image coregistration. However, the matched points are highly erroneous because of the speckle noise in SAR images and the different structures of SAR and optical images. Therefore, an adaptive and elliptical bilateral filter is used to remove the speckle noise. Additionally, a hierarchical approach is used for coregistration using the local and global geometrical relationship of BRISK features. For each salient point in the optical image, three closest matched points are found in the SAR image. The geometrical relationship of the matched points is determined in the local areas around the salient and matched points, and matched pairs with fewer geometrical matching scores are removed. At the final stage of the algorithm, the projective global model between optical and SAR images is obtained using a robust statistic and the remaining false matches are refined. Experimental results and the comparison of the results of the proposed algorithm with those of the existing approaches show that the proposed algorithm is more efficient.

Speckle noise is primarily due to the phase fluctuations of the electromagnetic return signals. Since inherent spatial-correlation characteristics of speckle in synthetic aperture radar (SAR) images are not exploited in existing multiplicative models for speckle noise, a speckle noise model is proposed here that provides a new framework for modelling and reducing the speckle noise. Both quantitative and qualitative criteria, including speckle reduction and texture preservation, are used to evaluate the performance of the proposed filter; one PALSAR (new Japanese sensor) image and a JERS-1 image are employed in the evaluation. The results showed that the proposed filter is slightly better than commonly used filters such as the Kuan, gamma, enhanced Lee, and enhanced Frost filters. The proposed filter can be used in different applications, including mapping and forestry biomass estimation. Furthermore, one of the benefits of the proposed filter is that it is independent of the threshold, which is required in most commonly used filters. The proposed filter was tested with SAR images of different sites in the northern forests of Iran.

Potential of texture from SAR tomographic images for forest aboveground biomass estimation