PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform

Accurate optical and synthetic aperture radar (SAR) image registration is crucial to multisensor remote sensing applications. Though several algorithms have been proposed, the practical implementation that directly matches large and high-resolution optical and SAR images remains underexplored by the community. Equipped with the satellite positioning parameters, optical and SAR images can be roughly registered based on geographic coordinates. However, the relative positioning accuracy is still dozens, even hundreds of pixels due to the inaccuracies of sensor parameters and elevation. Consequently, we propose a robust registration algorithm, which consists of two stages, where the horizontal positioning errors can be reduced in the first stage, and then, we fine-tune the correspondences in the second stage. Specifically, we propose a novel template matching method based on the dilated convolutional feature (DCF) and epipolar-oriented phase correlation. DCF is constructed by a depthwise-separable dilated convolution with multichannel gradients, which are generated by the Sobel operator for optical images and a ratio of exponentially weighted averages (ROEWA) operator for SAR images. Due to the large reception field of dilated convolution, DCF can retain invariance even for large relative positioning errors. The epipolar-oriented rectangle template, which stretches along the epipolar line, is then proposed to capture more overlapping areas compared to square templates. Furthermore, the outlier removal is implemented in the coordinate system of optical images to avoid the effect of range compression in SAR images. Inliers are finally used to refine the rational polynomial coefficients (RPCs) based on the bundle adjustment technique. Experimental results on high-resolution optical and SAR image products of various scenarios demonstrate the effectiveness of the proposed registration framework. The relative positioning errors of the refined RPCs can be reduced from hundreds of pixels to the subpixel level.

Due to the differences in radiation and geometric characteristics of optical and synthetic aperture radar (SAR) images, there is still a huge challenge for accurate matching. In this paper, we propose a patch-matching network (PM-Net) to improve the matching performance of optical and SAR images. First, a multi-level keypoints detector (MKD) with fused high-level and low-level features is presented to extract more robust keypoints from optical and SAR images. Second, we use a two-channel network structure to improve the image patch matching performance. Benefiting from this design, the proposed method can directly learn the similarity between optical and SAR image patches without manually designing features and descriptors. Finally, the MKD and two-channel net-work are trained separately on GL3D and QXS-SAROPT data sets, and the PM-Net is tested on multiple pairs of optical and SAR images. The experimental results demonstrate that the proposed method outperforms four advanced image matching networks on qualitative and quantitative assessments. The quantitative experiment results show that using our method correct matching points numbers are increased by more than 1.15 times, the value of F1-measure is raised by an average of 7.4% and the root mean squared error (RMSE) is reduced by more than 15.3%. The advantages of MKD and the two-channel network are also verified through ablation experiments.

Ship detection from synthetic aperture radar (SAR) images is a hot topic, but the difficulty in collecting labeled SAR images may hinder the development of deep-learning-based detection methods. Inspired by the idea of domain adaptation, in this article, we propose a hierarchical similarity alignment neural network (HSANet) for ship detection in SAR images, which is a domain adaptive (DA) approach with optical remote sensing images as training samples. The kernel target of HSANet is to mine and align both the global structure and the local instance information between SAR and optical images, where two modules, structural alignment module (SAM) and prototype alignment module (PAM), are designed to, respectively, conduct two hierarchies of alignment process. In general, SAM attempts to extract the global structure similarity which exists in image-level feature representation, while PAM tends to extract the local shape similarity which is instance-level representation. To be specific, SAM is developed by Fourier-based feature alignment, which tries to describe the similar structural relationship between optical and SAR images. Meanwhile, PAM is proposed based on the conjoint confidence analysis where the instance-level ship representations of the source and target domains is aligned. SAM and PAM work together to construct a hierarchical domain adaptation network for SAR ship detection. Experiments on several public datasets may indicate the effectiveness of the proposed method.

Reliable and accurate land use/land cover (LULC) map is a crucial data source for the understanding of coupled human–environment systems, monitoring changes, timely low-cost planning, and management of natural resources. Improvements in sensor technologies and machine learning capabilities have shifted the attention of remote sensing community to data complementarity through fusion of multi-sensor data for accurate feature extraction and mapping. Amalgamation of optical and synthetic aperture radar (SAR) images has shown promising advantages in enhancing the accuracy of extracting LULC as such method allows exploitation of information in sensors. This study investigated the potential of using freely available multisource Sentinel images to extract LULC maps in semi-arid environments through multi-scale geographic object-based image analysis (GEOBIA). A multi-scale classification framework that integrates GEOBIA, correlation-based feature selection (CFS), and random forest (RF)-supervised classification was adopted to extract LULC from assimilation of Sentinel multi-sensor products. First, Sentinel-1 and -2 images were pre-processed. Second, optimum multi-scale segmentation levels were selected using F-score segmentation quality measures. Third, 70 features of various spectral indices and derivatives and geometrical features from optical data and multiple ratios and textural features from dual-polarization SAR images were computed, and a CFS based on wrapper approach was used to select the most significant features at multi-scale levels. Finally, a single and multi-scale RF classifier was used to extract LULC classes using the most relevant features extracted from Sentinel SAR and optical images. Results of multi-scale image segmentation optimization showed that scale parameter (SP) values of 40, 60, and 150 were optimal for extraction of LULC classes. Results of feature selection showed that 22, 24, and 27 features were selected at scale SP values of 40, 60, and 150, respectively. Half of the features were common among the three scales. Single RF classification yielded overall accuracy (OA) values of 92.10%, 93%, and 91% and kappa coefficients of 0.901, 0.912, and 0.89 at scale values of 150, 60, and 40, respectively. Multiscale RF classification from scale values of 150 and 60 produced better LULC classification with OA 96.06% and kappa coefficient of 0.95 compared with other scale SP values. The integrated approach demonstrated an effective and promising method for high-quality LULC extraction from coupling optical and SAR images. Overall, multi-sensor Sentinel images along with the adopted approach feature a remarkable potential for improving LULC extraction and can effectively be used to update geographic information system layers for various applications.

Due to the different imaging mechanism of optical image and Synthetic Aperture Radar (SAR) image, they have the large different characteristics between the images, so fusing optical image and SAR image with image fusion technology could complement advantages and be able to better interpret the scenes information. A fusion algorithm of Synthetic Aperture Radar and optical image with fast sparse representation on low-frequency images was proposed. For the disadvantage of target information easily missing and the contrast low in fused image, and the fusion method with sparse representation could effectively retain target information of Synthetic Aperture Radar image, so the paper fuses low frequency images of Synthetic Aperture Radar and optical images using sparse representation. Moreover a new sparse coefficient fusion rules is proposed, and sparse decomposition process is improved to reduce the algorithm running time. Experimental results demonstrate the effectiveness of the algorithm.

Synthetic aperture radar (SAR) and optical imaging are different remote sensing methods. Given a SAR image, is it possible to predict what the observed scene looks like in an optical image? Transfer between SAR data and optical data seems to be impossible. However, this article shows examples that by applying deep learning techniques on high resolution airborne SAR images and GoogleEarth optical images, the SAR images and optical images can transfer with each other. The transferring help us to better understand the relationship between SAR and optical image, and can be potentially used to transfer detection or classification algorithms for optical image straightforwardly to be applied on SAR image.

Synthetic aperture radar (SAR) image super-resolution (SR) has broad application prospects in military reconnaissance, marine monitoring, crop monitoring, and other fields. However, when the scale factor is large, current SAR image SR has limitations, such as insufficient feature extraction, difficulty of feature representation, and modeling difficulty of SR reconstruction. These problems have severely restricted the SR performance of SAR images. In addition, optical remote sensing images share higher resolution and richer high-frequency information than SAR remote sensing images. Therefore, we adopt high-resolution (HR) optical images as auxiliary images of SAR image SR to improve the performance of SAR image SR. Our research is conducted from two aspects: the extraction efficiency of HR optical image-guided information and the representation ability of low-resolution (LR) SAR image features. We propose an optical guidance residual networks (OGRNs) based on residual learning for SAR image SR. The OGRN uses optical images during network training, which enhances the performance of SAR image SR, even when optical images are missing during network testing. First, we extract the texture feature of SAR images through the designed residual learning module. Second, we extract the weight map of feature information for SAR image SR from HR optical images by the convolutional layer. Finally, the weight map is used to guide the weighted supervised SR reconstruction for SAR images. Extensive experimental results demonstrated that, under the guidance of optical images, OGRN achieves excellent performance in terms of both quantitative assessment metrics and visual quality.

Synthetic aperture radar (SAR) is an important remote sensing sensor whose application is becoming more and more extensive. Compared with traditional optical sensors, it is not easy to be disturbed by the external environment and has a strong penetration. Limited by its working principles, SAR images are not easily interpreted, and fusing SAR images with optical multispectral images is a good solution to improve the interpretability of SAR images. This paper presents a novel image fusion method based on non-subsampled shearlet transform and activity measure to fuse SAR images with multispectral images, whose aim is to improve the interpretation ability of SAR images easily obtained at any time, rather than producing a fused image containing more information, which is the pursuit of previous fusion methods. Three different sensors, together with different working frequencies, polarization modes and spatial resolution SAR datasets, are used to evaluate the proposed method. Both visual evaluation and statistical analysis are performed, the results show that satisfactory fusion results are achieved through the proposed method and the interpretation ability of SAR images is effectively improved compared with the previous methods.

The combined use of synthetic aperture radar (SAR) and optical images for surface observation is gaining increasing attention. Optical images, with their distinct edge features, can accurately classify different objects, while SAR images reveal deeper internal variations. To address the challenge of differing feature distributions in multi-source images, we propose an edge enhancement network, OSNet (network for optical and SAR images), designed to jointly extract features from optical and SAR images and enhance edge feature representation. OSNet consists of three core modules: a dual-branch backbone, a synergistic attention integration module, and a global-guided local fusion module. These modules, respectively, handle modality-independent feature extraction, feature sharing, and global-local feature fusion. In the backbone module, we introduce a differentiable Lee filter and a Laplacian edge detection operator in the SAR branch to suppress noise and enhance edge features. Additionally, we designed a multi-source attention fusion module to facilitate cross-modal information exchange between the two branches. We validated OSNet’s performance on segmentation tasks (WHU-OPT-SAR) and regression tasks (SNOW-OPT-SAR). The results show that OSNet improved PA and MIoU by 2.31% and 2.58%, respectively, in the segmentation task, and reduced MAE and RMSE by 3.14% and 4.22%, respectively, in the regression task.

Applying deep learning methods to achieve synthetic aperture radar (SAR) image ship target detection has become a current hot research topic. However, most of the existing detection networks are designed based on optical images, so when they are directly applied to SAR image target detection, there will be the following two problems: 1) There are big differences between SAR image and optical image in imaging mechanism, geometric characteristics and radiation characteristics, etc, which makes the detection network have more redundant imformation; 2) Real-time performance and accuracy are both very important in SAR image target detection task, and applying a network designed based on optical image cannot balance the performance of the two well. In response to the above problems, this paper proposes a lightweight network LSSNet specially designed for SAR image ship detection task. In earlier layers, we use depthwise separable convolution instead of conventional convolution to design dense block; in deeper layers, stacked dense blocks with shortcuts are used to enhance deep supervision, and finally achieve high-speed and high-accuracy detection. This paper uses SSDD dataset as baseline for expeiment and results show that LSSNet have higher accurracy and detecition speed. Its lightweight structure will also help transplant to hardware devices in the future.

In the field of remote sensing, cloud cover severely reduces the quality of satellite observations of the earth. Due to the complete absence of information in cloud-covered regions, cloud removal with a single optical image is an ill-posed problem. Since the synthetic aperture radar (SAR) can effectively penetrate clouds, fusing SAR and optical remote sensing images will effectively alleviate this problem. However, existing SAR-based optical cloud removal methods fail to effectively leverage the global information provided by the SAR image, resulting in limited performance gains. In this paper, we introduce a novel cloud removal method named the Multi-Level SAR-Guided Contextual Attention Network (MSGCA-Net). MSGCA-Net is designed with a multi-level architecture that integrates a SAR-Guided Contextual Attention (SGCA) module to fuse the dependable global contextual information from SAR images with the local features of optical images effectively. In the module of SGCA, the SAR image provides reliable global contextual information and genuine structure of cloud-covered regions, while the optical image provides the local feature information. The proposed model can efficiently extract and fuse global and local contextual information in SAR and optical images. We trained and evaluated the performance of the model on both simulated and real-world datasets. Both qualitative and quantitative experimental evaluation demonstrated that the proposed method can yield high quality cloud-free images and outperform state-of-the-art cloud removal methods.

Relationships between internal solitary wave surface features in optical and SAR satellite images: Insights from remote sensing and laboratory

Abstract. Complex geometric distortions and nonlinear radiation differences between optical and synthetic aperture radar (SAR) images present challenges for the matching of sufficient and evenly distributed corresponding points. To address this problem, this paper proposes a deep convolutional network based on an attention mechanism for matching optical and SAR images. In order to obtain robust feature points, we employ phase consistency instead of image intensity and gradient information for feature detection. A deep convolutional network (DCN) is designed to extract high-level semantic features between optical and SAR images, providing robustness to geometric distortion and nonlinear radiation changes. Notably, incorporating multiple inverted residual structures in the DCN facilitates efficient extraction of local and global features, promoting feature reuse, and reducing the loss of key features. Furthermore, a dense feature fusion module based on coordinate attention is designed, focusing on the spatial positional information of effective features, integrating key features into deep descriptors to enhance the robustness of deep descriptors to nonlinear radiometric differences. A coarse-to-fine strategy is then employed to enhance accuracy by eliminating mismatches. Experimental results demonstrate that the proposed network performs better than the manually designed descriptors-based methods and the stateof- the-art deep learning networks in both matching effectiveness and accuracy. Specifically, the number of matches achieved is approximately 2 times greater than that of other methods, with a 10% improvement in F-measure.

Synthetic aperture radar (SAR) offers all-weather and all-day high-resolution imaging, yet its unique imaging mechanism often necessitates expert interpretation, limiting its broader applicability. Addressing this challenge, this letter proposes a generative model that bridges SAR and optical imaging, facilitating the conversion of SAR images into more human-recognizable optical aerial images. This assists in the interpretation of SAR data, making it easier to recognize. Specifically, our model backbone is based on the recent diffusion models, which have powerful generative capabilities. We have innovatively tailored the diffusion model framework, incorporating SAR images as conditional constraints in the sampling process. This adaptation enables the effective translation from SAR to optical images. We conduct experiments on the satellite GF3 and SEN12 datasets and use structural similarity (SSIM) and Fréchet inception distance (FID) for quantitative evaluation. The results show that our model not only surpasses previous methods in quantitative evaluation but also significantly improves the visual quality of the generated images. This advancement underscores the model’s potential to enhance SAR image interpretation.

The existence of clouds is one of the main factors that contributes to missing information in optical remote sensing images, restricting their further applications for Earth observation, so how to reconstruct the missing information caused by clouds is of great concern. Inspired by the image-to-image translation work based on convolutional neural network model and the heterogeneous information fusion thought, we propose a novel cloud removal method in this paper. The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function. Between the first step and the second step, the contrast and luminance of the simulated optical image are randomly altered to make the model more robust. Two simulation experiments and one real-data experiment are conducted to confirm the effectiveness of the proposed method on Sentinel 1/2, GF 2/3 and airborne SAR/optical data. The results demonstrate that the proposed method outperforms state-of-the-art algorithms that also employ SAR images as auxiliary data.