Characteristics Of SAR Images Research Articles

OGRN: optical-guided residual dense network for SAR image super-resolution reconstruction network

ABSTRACT Although Convolutional Neural Networks have significantly improved the development of SAR image super-resolution (SR) technology in recent years, it is a very challenging problem to reconstruct SAR image with large-scale factors, such as ×4 and ×8 due to limited available information from low-resolution image. The co-registered high-resolution optical image has been successfully applied to enhance the quality of SAR image due to its discriminative characteristics. Compared with single-frame SAR image SR reconstruction technology, optical image-guided SAR image SR reconstruction technology has better performance. This paper proposes an optical-guided residual dense network (OGRN) for SAR image super-resolution reconstruction network. The network is an end-to-end network. Its most important characteristic is that the SR model of SAR images can be obtained through optical image assistance during training, and high-quality SAR images can be generated without optical image assistance during testing. Firstly, a new dense residual backbone network that extracts high-frequency information is constructed. It can extract and propagate high-frequency features of SAR images efficiently to improve the feature representation ability of the network for high-frequency features. Then, the extracted high-frequency features of the SAR image are used to generate an optical image through the designed SAR-to-Optical image translation network. Finally, a reconstruction module is constructed based on the proposed fusion module for optical and SAR images. The fusion module fuses the high-frequency feature information of the extracted optical images with the SAR image features to provide features for SAR image SR reconstruction. Extensive experiments conducted on Sen1-2 and QXS datasets demonstrated that under the guidance of optical image, our OGRN can achieve excellent performance in both quantitative assessment metrics and visual quality.

Physics-guided interpretable CNN for SAR target recognition

Deep Learning (DL) model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) and has achieved excellent performance. However, the black-box nature of DL models has been the focus of criticism, especially in the application of SAR-ATR, which is closely associated with the national defense and security domain. To address these issues, a new interpretable recognition model Physics-Guided BagNet (PGBN) is proposed in this article. The model adopts an interpretable convolutional neural network framework and uses time–frequency analysis to extract physical scattering features in SAR images. Based on the physical scattering features, an unsupervised segmentation method is proposed to distinguish targets from the background in SAR images. On the basis of the segmentation result, a structure is designed, which constrains the model’s spatial attention to focus more on the targets themselves rather than the background, thereby making the model’s decision-making more in line with physical principles. In contrast to previous interpretable research methods, this model combines interpretable structure with physical interpretability, further reducing the model’s risk of error recognition. Experiments on the MSTAR dataset verify that the PGBN model exhibits excellent interpretability and recognition performance, and comparative experiments with heatmaps indicate that the physical feature guidance module presented in this article can constrain the model to focus more on the target itself rather than the background.

LRMSNet: A New Lightweight Detection Algorithm for Multi-Scale SAR Objects

In recent years, deep learning has found widespread application in SAR image object detection. However, when detecting multi-scale targets against complex backgrounds, these models often struggle to strike a balance between accuracy and speed. Furthermore, there is a continuous need to enhance the performance of current models. Hence, this paper proposes LRMSNet, a new multi-scale target detection model designed specifically for SAR images in complex backgrounds. Firstly, the paper introduces an attention module designed to enhance contextual information aggregation and capture global features, which is integrated into a backbone network with an expanded receptive field for improving SAR image feature extraction. Secondly, this paper develops an information aggregation module to effectively fuse different feature layers of the backbone network. Lastly, to better integrate feature information at various levels, this paper designs a multi-scale aggregation network. We validate the effectiveness of our method on three different SAR object detection datasets (MSAR-1.0, SSDD, and HRSID). Experimental results demonstrate that LRMSNet achieves outstanding performance with a mean average accuracy (mAP) of 95.2%, 98.9%, and 93.3% on the MSAR-1.0, SSDD, and HRSID datasets, respectively, with only 3.46 M parameters and 12.6 G floating-point operation cost (FLOPs). When compared with existing SAR object detection models on the MSAR-1.0 dataset, LRMSNet achieves state-of-the-art (SOTA) performance, showcasing its superiority in addressing SAR detection challenges in large-scale complex environments and across various object scales.

Multi-Scale Feature Fusion Network with Symmetric Attention for Land Cover Classification Using SAR and Optical Images

The complementary characteristics of SAR and optical images are beneficial in improving the accuracy of land cover classification. Deep learning-based models have achieved some notable results. However, how to effectively extract and fuse the unique features of multi-modal images for pixel-level classification remains challenging. In this article, a two-branch supervised semantic segmentation framework without any pretrained backbone is proposed. Specifically, a novel symmetric attention module is designed with improved strip pooling. The multiple long receptive fields can better perceive irregular objects and obtain more anisotropic contextual information. Meanwhile, to solve the semantic absence and inconsistency of different modalities, we construct a multi-scale fusion module, which is composed of atrous spatial pyramid pooling, varisized convolutions and skip connections. A joint loss function is introduced to constrain the backpropagation and reduce the impact of class imbalance. Validation experiments were implemented on the DFC2020 and WHU-OPT-SAR datasets. The proposed model achieved the best quantitative values on the metrics of OA, Kappa and mIoU, and its class accuracy was also excellent. It is worth mentioning that the number of parameters and the computational complexity of the method are relatively low. The adaptability of the model was verified on RGB–thermal segmentation task.

A variational model to remove multiplicative noise based on SAR image feature preservation

A variational model to remove multiplicative noise based on SAR image feature preservation

Ship Classification Based on Density Features in SAR Images

Ship classification in SAR images has attracted much attention by researchers. In this paper, a SAR target classification method for three commercial ships (container ships, bulk carriers and oil tanker) is proposed by analyzing their scattering features. Firstly, the ship slice is preprocessed to obtain the binary image, from which the density features can be extracted, which describing the ship scattering point distribution. Finally, the support vector machine (SVM) classifier is applied to classify these three types of commercial ships. The experimental results show that the classification accuracy of structure feature and strength feature is low, while the proposed density feature can reach 80% for three types of ships. The combination of structure features and strength features with density features can improve the classification accuracy. Combining the three features has the best classification performance.

SAR minimum entropy autofocusing based on Prewitt operator.

Current autofocus algorithms utilizing image criteria impose a significant computational burden. Therefore, this paper proposes a computationally efficient autofocus algorithm combined with SAR image feature points, employing the Prewitt operator to obtain the SAR image features. The range cell with the number of feature points in the front row as the input of the autofocus method to perform motion error estimation and compensation on SAR imagery. Our method's key feature is to optimize the selection criteria of range cells by acquiring the feature points of SAR images,reduces the number of input range cell,reduce the computational complexity of the autofocus algorithm and ultimately enhance the focusing effect of SAR images. Trials involving simulation and measured data demonstrate the effectiveness of the developed method.

Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park

Continuous monitoring of oil discharges in coastal and open ocean waters using Earth Observation (EO) has undeniably contributed to diminishing their occurrence wherever a detection system was in place, such as in Europe (EMSA's CleanSeaNet) or in the United States (NOAA's OR&R). This study describes the development and testing of a semi-automated oil slick detection system tailored to the Great Barrier Reef (GBR) marine park solely based on EO data as no such service was routinely available in Australia until recently. In this study, a large, curated, historical global dataset of SAR imagery acquired by Sentinel-1 SAR, now publicly available, is used to assess classification techniques, namely an empirical approach and a deep learning model, to discriminate between oil-like features and look-alikes in the scenes acquired over the marine park. An evaluation of this detection system on 10 Sentinel-1 SAR images of the GBR using two performance metrics - the detection accuracy and the false-positive rate (FPR) - shows that the classifiers perform best when combined (accuracy >98 %; FPR 0.01) rather than when used separately. This study demonstrates the benefit of sequentially combining classifiers to improve the detection and monitoring of unreported oil discharge events in SAR imagery. The workflow has also been tested outside the GBR, demonstrating its robustness when applied to other regions such as Australia's Northwest Shelf, Southeast Asia and the Pacific.

A Novel Lightweight Attention-Discarding Transformer for High-Resolution SAR Image Classification

Vision Transformer (ViT) has been introduced in high-resolution synthetic aperture radar (HR SAR) image classification due to its excellent global feature extraction ability. However, small samples of SAR images make it difficult to fit the ViT with excessive trainable parameters, which easily results in over-fitting in training. Meanwhile, poor capability in capturing local features of ViT limits its accuracy in SAR image classification. To solve these problems, this letter proposes a new Lightweight Attention-Discarding Transformer (LAD Transformer) for the classification of HR SAR images. In the proposed model, the backbone of the advanced Swin transformer is used to model global information and extract hierarchical features. Moreover, the vital feature extraction part of the LAD Transformer completely discards the self-attention mechanism and extracts local features of SAR images by introducing lighter group convolution and channel shuffle (GC-CS Block). In addition, to address the estimation shift caused by consecutive batch normalization (BN) layers, a new composite normalization method consisting of Batch normalization and Layer Normalization (BLN) in GC-CS Block is proposed. The experiments show that the proposed network has fewer parameters and higher classification accuracy on two real HR SAR data.

EDTRS: A Superpixel Generation Method for SAR Images Segmentation Based on Edge Detection and Texture Region Selection

The generation of superpixels is becoming a critical step in SAR image segmentation. However, most studies on superpixels only focused on clustering methods without considering multi-feature in SAR images. Generating superpixels for complex scenes is a challenging task. It is also time consuming and inconvenient to manually adjust the parameters to regularize the shapes of superpixels. To address these issues, we propose a new superpixel generation method for SAR images based on edge detection and texture region selection (EDTRS), which takes into account the different features of SAR images. Firstly, a Gaussian function is applied in the neighborhood of each pixel in eight directions, and a Sobel operator is used to determine the redefined region. Then, 2D entropy is introduced to adjust the edge map. Secondly, local outlier factor (LOF) detection is used to eliminate speckle-noise interference in SAR images. We judge whether the texture has periodicity and introduce an edge map to select the appropriate region and extract texture features for the target pixel. A gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA) are combined to extract texture features. Finally, we use a novel approach to combine the features extracted, and the pixels are clustered by the K-means method. Experimental results with different SAR images show that the proposed method outperforms existing superpixel generation methods with an increase of 5–10% in accuracy and produces more regular shapes.

Sar2color: Learning Imaging Characteristics of SAR Images for SAR-to-Optical Transformation

Optical images are rich in spectral information, but difficult to acquire under all-weather conditions, while SAR images can overcome adverse meteorological conditions, but geometric distortion and speckle noise will reduce the quality of SAR images and thus make image interpretation more challenging. Therefore, transforming SAR images to optical images to assist SAR image interpretation will bring opportunities for SAR image application. With the advancement of deep learning technology, the ability of SAR-to-optical transformation has been greatly improved. However, most of the current mainstream transformation methods do not consider the imaging characteristics of SAR images, and there will be failures such as noisy color spots and regional landform deformation in the generated optical images. Moreover, since the SAR image itself does not contain color information, there also exist many color errors in these results. Aiming at the above problems, Sar2color, an end-to-end general SAR-to-optical transformation model, is proposed based on a conditional generative adversarial network (CGAN). The model uses DCT residual block to reduce the effect of coherent speckle noise on the generated optical images, and constructs the Light atrous spatial pyramid pooling (Light-ASPP) module to mitigate the negative effect of geometric distortion on the generation of optical images. These two designs ensure the precision of texture details when the SAR image is transformed into an optical image, and use the correct color memory block (CCMB) to improve the color accuracy of transformation results. Towards the Sar2color model, we have carried out evaluations on the homologous heterogeneous SAR image and optical image pairing dataset SEN1-2. The experimental results show that, compared with other mainstream transformation models, Sar2color achieves the state-of-the-art effect on all three objective and one subjective evaluation metrics. Furthermore, we have carried out various ablation experiments, and the results show the effectiveness of each designed module of Sar2color.

Deep Learning for SAR Ship Detection: Past, Present and Future

After the revival of deep learning in computer vision in 2012, SAR ship detection comes into the deep learning era too. The deep learning-based computer vision algorithms can work in an end-to-end pipeline, without the need of designing features manually, and they have amazing performance. As a result, it is also used to detect ships in SAR images. The beginning of this direction is the paper we published in 2017BIGSARDATA, in which the first dataset SSDD was used and shared with peers. Since then, lots of researchers focus their attention on this field. In this paper, we analyze the past, present, and future of the deep learning-based ship detection algorithms in SAR images. In the past section, we analyze the difference between traditional CFAR (constant false alarm rate) based and deep learning-based detectors through theory and experiment. The traditional method is unsupervised while the deep learning is strongly supervised, and their performance varies several times. In the present part, we analyze the 177 published papers about SAR ship detection. We highlight the dataset, algorithm, performance, deep learning framework, country, timeline, etc. After that, we introduce the use of single-stage, two-stage, anchor-free, train from scratch, oriented bounding box, multi-scale, and real-time detectors in detail in the 177 papers. The advantages and disadvantages of speed and accuracy are also analyzed. In the future part, we list the problem and direction of this field. We can find that, in the past five years, the AP50 has boosted from 78.8% in 2017 to 97.8 % in 2022 on SSDD. Additionally, we think that researchers should design algorithms according to the specific characteristics of SAR images. What we should do next is to bridge the gap between SAR ship detection and computer vision by merging the small datasets into a large one and formulating corresponding standards and benchmarks. We expect that this survey of 177 papers can make people better understand these algorithms and stimulate more research in this field.

An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions

NDVI is an important parameter for environmental assessment and precision agriculture that well-describes the status of vegetation. Nevertheless, the clouds in optical images often result in the absence of NDVI information at key growth stages. The integration of SAR and optical image features will likely address this issue. Although the mapping of different data sources is complex, the prosperity of deep learning technology provides an alternative approach. In this study, the double-attention RNN architecture based on the recurrent neural network (RNN) and attention mechanism is proposed to retrieve NDVI data of cloudy regions. Overall, the NDVI is retrieved by the proposed model from two aspects: the temporal domain and the pixel neighbor domain. The performance of the double-attention RNN is validated through different cloud coverage conditions, input ablation, and comparative experiments with various methods. The results conclude that a high retrieval accuracy is guaranteed by the proposed model, even under high cloud coverage conditions (R2 = 0.856, RMSE = 0.124). Using SAR images independently results in poor NDVI retrieval results (R2 = 0.728, RMSE = 0.141) with considerable artifacts, which need to be addressed with auxiliary data, such as IDM features. Temporal and pixel neighbor features play an important role in improving the accuracy of NDVI retrieval (R2 = 0.894, RMSE = 0.096). For the missing values of NDVI data caused by cloud coverage, the double-attention RNN proposed in this study provides a potential solution for information recovery.

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

Accurate registration between SAR images is the basis for high-precision geometric correction of SAR images. The feature points extracted by conventional feature extraction methods is unsatisfactory, which is affected by imaging geometric characteristics and speckle noise of SAR images. This paper innovatively proposes a spaceborne SAR image feature learning framework to realize automatic sample generation and model training. It mainly includes two modules: The feature sample generation module based on the initial geometric information of spaceborne SAR. The initial RPC parameters of the spaceborne SAR are adopted to realize the initial positioning of SAR image, and a variety of feature extraction operators are used to match the overlapping areas to obtain high-precision matching points, which are employed as training samples for image-pairs; Pseudo-Siamese feature learning network SARPointNet for SAR image feature learning. The pseudo-siamese network is used to extract the feature points and descriptors of the sample image-pairs. The feature optimization process is realized through the descriptor constraints between image pairs, which promotes the network to improve the accuracy of feature extraction. The proposed method has been tested in mountain, hilly, flatland and urban scenarios, respectively. The results demonstrate that the correspondence points extracted by SARPointNet are evenly distributed, large quantities (at least 10 times that of other methods) and achieve high precision (The root mean square error is less than 1 pixels), which shows great advantages over other methods.

Retraction Note: Evolution of dissolved total solids in groundwater based on high resolution image processing and evaluation of urban English translation

In recent years, learning based super-high resolution image reconstruction technology has been rapidly applied. It also has good development and application effect in related fields. So in this paper, by combining the characteristics of SAR image, the learning based super-high resolution image reconstruction technology is added to the field of super-high resolution image reconstruction, and has been applied in reality. Image classification and ultra-high resolution technology are closely integrated. However, because the hardware facilities of some equipment are relatively expensive, and will inevitably be affected by other factors in the imaging process, in order to better study the influence of the increase of dissolved total solids in groundwater on the water level in the core of the funnel in the plain area, and the main components of the increase of dissolved total solids in groundwater. Every country in the world is carrying on a kind of propaganda to its national image, which is also a main manifestation of the overall level of the country. This paper studies the process of City English translation, makes corresponding analysis and preparation before city English translation, and pays attention to relevant reply and report after city English translation. This paper focuses on the construction system of City English translation and the improvement of the ways and methods of using English to publicize the city. Through the construction of City English translation system, this paper expounds the influence of Public English translation in City publicity construction, and changes the corresponding strategies for high-resolution image processing, so as to make English translation more accurate for the change process of total dissolved solids in groundwater, and improve the ability and level of City English translation.

Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model

The elimination of hunger is the top concern for developing countries and is the key to maintain national stability and security. Paddy rice occupies an essential status in food supply, whose accurate monitoring is of great importance for human sustainable development. As one of the most important paddy rice production countries in the world, Thailand has a favorable hot and humid climate for paddy rice growing, but the growth patterns of paddy rice are too complicated to construct promising growth models for paddy rice discrimination. To solve this problem, this study proposes a large-scale paddy rice mapping scheme, which uses time-series Sentinel-1 data to generate a convincing annual paddy rice map of Thailand. The proposed method extracts temporal statistical features of the time-series SAR images to overcome the intra-class variability due to different management practices and modifies the U-Net model with the fully connected Conditional Random Field (CRF) to maintain the edge of the fields. In this study, 758 Sentinel-1 images that covered the whole country from the end of 2018 to 2019 were acquired to generate the annual paddy rice map. The accuracy, precision, and recall of the resultant paddy rice map reached 91%, 87%, and 95%, respectively. Compared to SVM classifier and the U-Net model based on feature selection strategy (FS-U-Net), the proposed scheme achieved the best overall performance, which demonstrated the capability of overcoming the complex cultivation conditions and accurately identifying the fragmented paddy rice fields in Thailand. This study provides a promising tool for large-scale paddy rice monitoring in tropical production regions and has great potential in the global sustainable development of food and environment management.

RETRACTED ARTICLE: Evolution of dissolved total solids in groundwater based on high resolution image processing and evaluation of urban English translation

In recent years, learning based super-high resolution image reconstruction technology has been rapidly applied. It also has good development and application effect in related fields. So in this paper, by combining the characteristics of SAR image, the learning based super-high resolution image reconstruction technology is added to the field of super-high resolution image reconstruction, and has been applied in reality. Image classification and ultra-high resolution technology are closely integrated. However, because the hardware facilities of some equipment are relatively expensive, and will inevitably be affected by other factors in the imaging process, in order to better study the influence of the increase of dissolved total solids in groundwater on the water level in the core of the funnel in the plain area, and the main components of the increase of dissolved total solids in groundwater. Every country in the world is carrying on a kind of propaganda to its national image, which is also a main manifestation of the overall level of the country. This paper studies the process of City English translation, makes corresponding analysis and preparation before city English translation, and pays attention to relevant reply and report after city English translation. This paper focuses on the construction system of City English translation and the improvement of the ways and methods of using English to publicize the city. Through the construction of City English translation system, this paper expounds the influence of Public English translation in City publicity construction, and changes the corresponding strategies for high-resolution image processing, so as to make English translation more accurate for the change process of total dissolved solids in groundwater, and improve the ability and level of City English translation.

Mapping flood by the object-based method using backscattering coefficient and interference coherence of Sentinel-1 time series

The SAR has the ability of all-weather and all-time data acquisition, it can penetrate the cloud and is not affected by extreme weather conditions, and the acquired images have better contrast and rich texture information. This paper aims to investigate the use of an object-oriented classification approach for flood information monitoring in floodplains using backscattering coefficients and interferometric coherence of Sentinel-1 data under time series. Firstly, the backscattering characteristics and interference coherence variation characteristics of SAR time series are used to analyze whether the flood disaster information can be accurately reflected and provide the basis for selecting input classification characteristics of subsequent SAR images. Subsequently, the contribution rate index of the RF model is used to calculate the importance of each index in time series to convert the selected large number of classification features into low dimensional feature space to improve the classification accuracy and reduce the data redundancy. Finally, the SAR image features in each period after multi-scale segmentation and feature selection are jointly used as the input features of RF classification to extract and segment the water in the study area to monitor floods' spatial distribution and dynamic characteristics. The results showed that the various attributes of backscatter coefficients and interferometric coherence under time series could accurately correspond with the actual flood risk, and the combined use of backscattering coefficient and interferometric coherence for flood extraction can significantly improve the accuracy of flood information extraction. Overall, the object-based random forest method using the backscattering coefficient and interference coherence of Sentinel-1 time series for flood extraction advances our understanding of flooding's temporal and spatial dynamics, essential for the timely adoption of adaptation and mitigation strategies for loss reduction.

Enhancement method of oceanic front features in SAR images based on nonpolarized components

Enhancement method of oceanic front features in SAR images based on nonpolarized components

Building detection by local region features in SAR images

The buildings are very complex for detection on SAR images, where the basic features of those are shadows. There are many different representations for SAR shadow. As result it is no possible to use convolutional neural network for building detection directly. In this article we give property analysis of SAR shadows of different type buildings. After that, each region (ROI) prepared for training of building detection is corrected with its own SAR shadow properties. Reconstructions of ROI will be put in a modified YOLO network for building detection with better quality result.