White balance (WB) is a key step in the image signal processor (ISP) pipeline that mitigates color casts caused by varying illumination and restores the scene’s true colors. Currently, sRGB-based WB editing for post-ISP WB correction is widely used [2], [18] to address color constancy failures in the ISP pipeline when the original camera RAW is unavailable. However, additive color models (e.g., sRGB) are inherently limited by fixed nonlinear transformations and entangled color channels, which often impede their generalization to complex lighting conditions.To address these challenges, we propose a novel framework for WB correction that leverages a perception-inspired Learnable HSI (LHSI) color space. Built upon a cylindrical color model that naturally separates luminance from chromatic components, our framework further introduces dedicated parameters to enhance this disentanglement and learnable mapping to adaptively refine the flexibility. Moreover, a new Mamba-based network is introduced, which is tailored to the characteristics of the proposed LHSI color space.Experimental results on benchmark datasets demonstrate the superiority of our method, highlighting the potential of perception-inspired color space design in computational photography. The source code is avail-able at https://github.com/YangCheng58/WB_Color_Space.

It is highly challenging to register large-scale, heterogeneous SAR and optical images, particularly across platforms, due to significant geometric, radiometric, and temporal differences, which most existing methods struggle to address. To overcome these challenges, we propose Grid-Reg, a grid-based multimodal registration framework comprising a domain-robust descriptor extraction network, Hybrid Siamese Correlation Metric Learning Network (HSCMLNet), and a grid-based solver (Grid-Solver) for transformation parameter estimation. In heterogeneous imagery with large modality gaps and geometric differences, obtaining accurate correspondences is inherently difficult. To robustly measure similarity between gridded patches, HSCMLNet integrates a hybrid Siamese module with a correlation metric learning module (CMLModule) based on equiangular unit basis vectors (EUBVs), together with a manifold consistency loss to promote modality-invariant, discriminative feature learning. The Grid-Solver estimates transformation parameters by minimizing a global grid matching loss through a dual-loop search strategy to reliably find patch correspondences across entire images. Furthermore, we curate a challenging benchmark dataset for SAR-to-optical registration using UAV MiniSAR data and Google Earth optical imagery. Extensive experiments demonstrate that our proposed approach achieves superior performance over state-of-the-art methods.

Single-modal change detection methods based on optical or Synthetic Aperture Radar (SAR) images face challenges such as degradation due to adverse weather or noise interference. In contrast, multimodal change detection struggles with significant domain gaps between different modalities. Inspired by the SAM2 model’s temporal memory mechanism for video segmentation, this paper introduces the concept of memory into change detection and proposes a novel approach called Memory-based Multimodal Change Detection (MMCD). By treating change detection as a temporal problem and modeling remote sensing images as video sequences, the proposed method integrates historical optical images with current SAR images to enhance detection accuracy. Additionally, a difference map enhancement module is introduced to mitigate false changes caused by modality discrepancies. Experimental results show that this approach achieves state-of-the-art performance in multimodal change detection, demonstrating the effectiveness of the proposed method.

As Moore’s Law approaches its physical limits, Design Technology Co-Optimization(DTCO) has become a critical pathway for advancing semiconductor process nodes. As a communication channel between design and technology, standard cell library design demands rapid and precise estimation for functionally identical cells with variant layouts. However, existing estimation methods face significant limitations: Geometry-based approaches using wirelength estimation, while efficient, lack accuracy and granularity for timing-arc analysis; Post-layout SPICE simulations are accurate but extremely time-consuming. Current machine learning methods confront two major challenges: Most approaches perform prediction solely at the pre-layout stage, neglecting layout-dependent effects; Model training requires massive simulation samples, incurring substantial computational and temporal overhead. To address these issues, this paper proposes ProtoCellLayout, a novel cell layout power, performance, and area(PPA) estimation framework. Core innovations include: 1) Systematically explores the representation methods of standard cell layouts and proposes a graph-based method named Topology-Geometry Graph (TGG), which integrates circuit topology and geometric features. By explicitly encoding transistor-level connections and metal layer geometries, the TGG method overcomes the limitations of image-based and semantic representations in modeling physical effects. 2) A prototype learning mechanism leveraging CMOS logic’s structural regularity and routing pattern similarity, enabling performance generalization across complex cells using minimal simple-cell training data. Experiments on an industrial library demonstrate that ProtoCellLayout achieves <3.5% percentage error while reducing estimation time from hours to under one minute for cell delay, leakage, internal power, and transition time. Its performance rankings align closely with SPICE simulation, achieving over 95% accuracy in layout selection.

The significant domain differences between heterogeneous Synthetic Aperture Radar (SAR) data lead to a serious degradation of the generalization ability of existing detection models in real cross-source scenarios. Traditional domain adaptation methods rely on target domain data and are difficult to cope with multi-source dynamic interference, while existing domain generalization (DG) schemes have not yet adequately addressed the multi-dimensional domain shift coupling problem unique to SAR images. This letter presents a unified framework for cross-scenery DG heterogenous SAR image object detection task. First, we match the SAR image scattering features with dynamic statistics to expand the domain feature space. Next, domain-invariant features and domain-specific features disentanglement module is designed for the above feature space, and redundant domain discriminative information is removed. In this way, the performance enhancement of both for DG algorithm can be maximized. Cross-source scenarios object detection results across multiple SAR/remote sensing image datasets obtained in a detection head with Faster RCNN as the baseline network all achieve the state-of-the-art performance available.

Aircraft detection in synthetic aperture radar (SAR) images is one challenging task due to the discreteness of aircraft scattering, the diversity of aircraft size, and the interference of background. In order to deal with these problems, a novel method named scattering enhancement and feature fusion network (SEFFNet) is here proposed to detect aircraft via combining traditional image processing and deep learning together. At first, a scattering information extraction and enhancement module (SIEEM) is proposed to highlight the scattering points of aircraft targets. Then, to more effectively focus on the location of aircraft targets, a space-to-depth coordinate attention module (SDCAM) is further designed, following which an efficient multi-scale feature fusion pyramid (FFP) is also introduced to fuse the semantic information of different layers. At last, a contextual fusion head (CFH) is built to improve the receptive field for better detecting aircraft. The experiments carried out on the popular datasets SADD and SAR-AIRcraft-1.0 show that SEFFNet is more appropriate for aircraft detection, especially the small-size aircraft detection, in comparison with other state-of-the-art (SOTA) methods. Taking the dataset SADD for example, on average, the precision, recall, F1-score, and APs values are respectively 2.8%, 2.6%, 2.7%, and 2.0% higher than the baseline network YOLOv5.

The change detection aims to identify temporal changes in land cover. In emergency disaster scenarios, acquiring postchange optical images is often difficult due to factors such as adverse weather and illumination conditions. In contrast, the SAR-based change detection is robust to these environmental factors but is prone to speckle noise and often lacks clear semantic interpretation. These challenges highlight the importance of multimodal approaches that integrate the complementary information from different data sources. To address the domain gap between optical and SAR data, we propose change detection prompt (CDPrompt), an automatic prompt-learning framework that leverages in-domain change information as prompts to suppress fake changes caused by the domain gap between the two modalities. CDPrompt incorporates a modality-specific domain tuning module (DTM) to inject the domain knowledge into the segment anything model (SAM), enabling efficient adaptation to multimodal data with minimal labels and training costs. A low-level enhancement module (LwEM) further refines spatial details using historical optical images, while a consistency loss enhances the learning of domain-invariant features between prechange optical and SAR images. To support evaluation in disaster scenarios with missing modalities, we extend the DFC25 dataset and introduce the first disaster-oriented multimodal change detection dataset, DFC25-Extended, comprising DFC25-OS-S and DFC25-O-SO. Extensive experiments on the Onera Satellite Change Detection (OSCD) and DFC25-Extended datasets demonstrate the superior performance and practical value of CDPrompt. The code and dataset will be publicly available at:https://github.com/zhanglimeng13/CDPrompt

The application of deep learning in the synthetic aperture radar (SAR) field is becoming increasingly widespread, but its black-box nature and the existence of adversarial samples limit its practical utility. In order to enhance the explainability and security of deep learning models, we initially selected 3 different deep convolutional neural network (DCNN) structures for training in SAR image classification. Subsequently, we applied Madry Defense Method to obtain robust models, and used 2 adversarial attack methods to attack DCNN classifiers. Finally, we employed 3 explainable artificial intelligence (XAI) methods to explain the predictions of different DCNN classifiers. Across different datasets and DCNNs, the Madry Defense Method helps classification models to reduce Infidelity and focus more on feature regions rich in semantic information when making decisions. The experimental results provide new insights into the adversarial robustness of DCNN classifiers in SAR image classification from an explainability viewpoint.

Unlike natural images, synthetic aperture radar (SAR) images exhibit a more scattered and uneven spatial distribution of objects, making semantic segmentation of SAR images a valuable topic of research. This paper presents a SAR image semantic segmentation method that incorporates the attention mechanism assisted by self-supervised scene classification. The self-supervised scene classification provides coarse scene classification at a higher semantic level, while the attention mechanism utilizes high-level semantic features to guide fine-grained classification of lower-level spatial structures. Overall, this approach improves the pixel-level classification performance of SAR images. We validate this method on the WHU-OPT-SAR dataset and compare its performance with previous works, providing a detailed analysis of its effectiveness.

Water body extraction is an important issue in flood surveillance and environmental protection. With the development of neural network, deep learning has been widely used in the water body extraction task because of its powerful feature extraction ability. Even so, most of existing deep learning networks only take into account the translation equivariance of convolution kernel for water body extraction. Actually, in terms of water body, its orientations imaged by optical sensor are usually various. So, when the orientated images are not well contained in the training set, the networks may yield some unsatisfactory extraction results. To solve this problem, in this paper, we propose an information-expanding network IE-Unet based on the traditional network U-net, where the rotation equivariant convolution, rotation-based channel attention mechanism, and the optimized Batchnorm are adopted jointly. To quantitatively evaluate its edge extraction capability, a new edge index AOD is proposed as well. The experimental results on one public dataset of water body demonstrate the effectiveness of IE-Unet. Compared with the original U-net, the IOU value of IE-Unet is increased by 7%, and the A0D value is reduced by 0.76.

Ship classification is usually a challenging task due to the small sizes of ship targets and the lack of significant differences between different categories. In terms of synthetic aperture radar (SAR) images, most existing deep learning-based methods are not designed from the angle of polarimetric characteristics to achieve ship classification. Thus, when facing the ship classification task of dual-polarized SAR images, these networks are often unsatisfactory. To cure this shortcoming, we here propose a novel dual branch deep network DBDN specifically designed for dual-polarized SAR ship classification. Our approach consists of three key modules: the image construction module ICM, the feature extraction module FEM, and the feature fusion and classifier module FFCM. In ICM, two novel pseudo RGB images are constructed for the first time, i.e., the polarimetric features-guided pseudo RGB image (PF-RGB) and the texture features-guided pseudo RGB image (TF-RGB), which can more accurately and comprehensively reflect ships’ characteristics. FEM enables the network to focus on important ship features and suppress irrelevant noise through transferred layers and designed ConvNeXt-Attention block (CNABlock), enhancing the discriminative capability of different ships. Finally, FFCM extracts and combines various ship features for classification, wherein the enhanced inverted residual block (EIRBlock) and the channel spatial attention module (CSAM) components are proposed as well. The performance of DBDN is evaluated on the OpenSARShip2.0 dataset, and experimental results show that DBDN achieves excellent performance in all evaluation metrics in comparison with some state-of-the-art (SOTA) algorithms. For example, compared to the recently proposed method DSN, DBDN further improves the accuracy by 4.74% and 4.07% in the three-class and six-class classification tasks, respectively.

The long-standing theory that a colour-naming system evolves under dual pressure of efficient communication and perceptual mechanism is supported by more and more linguistic studies, including analysing four decades of diachronic data from the Nafaanra language. This inspires us to explore whether machine learning could evolve and discover a similar colour-naming system via optimising the communication efficiency represented by high-level recognition performance. Here, we propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining the accuracy of machine recognition on the quantised images. Given an RGB image, Annotation Branch maps it into an index map before generating the quantised image with a colour palette; meanwhile the Palette Branch utilises a key-point detection way to find proper colours in the palette among the whole colour space. By interacting with colour annotation, CQFormer is able to balance both the machine vision accuracy and colour perceptual structure such as distinct and stable colour distribution for discovered colour system. Very interestingly, we even observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages. Besides, our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage while maintaining high performance in high-level recognition tasks such as classification and detection. Extensive experiments demonstrate the superior performance of our method with extremely low bit-rate colours, showing potential to integrate into quantisation network to quantities from image to network activation. The source code is available at https://github.com/ryeocthiv/CQFormer

Nowadays, deep learning has been widely used for water body detection because of its high precision databased water segmentation ability. Although the networks based on deep learning have shown higher automation, applicability and extraction accuracy than the traditional threshold methods in water body detection, only the translation equivariance of the convolution kernel is considered in these networks. Actually, for the detection of water body, its rotation equivariance also needs to be considered. For this goal, we here propose a new convolutional neural network by improving the U-Net with the rotation equivariant convolution and attention mechanism, which is simplified as GACNN. Experimental results on optical water body images demonstrate the effectiveness of the improved network based on U-net.

Deep neural networks are widely used in SAR image classification and recognition, achieving state-of-the-art performance. But it remains a challenging task to recognize occluded targets. In this letter, we propose a novel robust SAR recognition method against occlusion. Specifically, we design a scattering excitation learning module that encourages the network to learn more robust features responding to the scattering centers of targets. In addition, we adopt a random feature channel dropout technique which can further improve robustness to occlusion. Our method makes the network more robust against occlusion but without any occlusion-simulated data for training. Experimental results on MSTAR dataset shows that our proposed method achieves remarkably improved robustness even under severe occlusions. Code is made available at https://github.com/koervcor/SEL-CD.

In recent years, deep learning has been extensively used in hyperspectral image (HSI) classification. The representative method is the convolutional neural network (CNN). However, due to the limitations of its inherent network backbone, CNNs still easily fail to mine some important information of HSIs, such as the sequence attributes of spectral signatures. To deal with this problem and make full use of the spectral-spatial information of HSIs, we propose a novel network named Multi-dimensional Information Expansion and Processing Network (MIEPN) for HSI classification, which is mainly composed of one information expansion module (IEM), one feature information expansion and extraction module (FEEM), and one ViT module. Briefly speaking, IEM expands and fuses HSI information in a three-dimensional (3D) space, yet FEPM pays more attention to digging deeper information. After these, the extracted information is input into the ViT module for HSI classification. Experiments carried out on several typical datasets demonstrate that the proposed network MIEPN can provide competitive results compared to the other state-of-the-art CNN-based methods.

The remote sensing community has shown increasingly interest in self-supervised learning for its ability to learn representations without labeled data. These representations can be easily adapted to downstream tasks through pre-training and fine-tuning. Recently, Masked Autoencoders (MAE) achieve better semantic representation by masking out a significant portion of the input image. However, the original design of MAE for RGB natural images may not be optimal for remote sensing (RS) images, which exhibit considerable variation between modalities like SAR and optical. To address this, we propose a 3D mask that enhances feature extraction along the vertical dimension. After fine-tuning, our 3DMAE model outperforms state-of-the-art contrastive and MAE-based models on BigEarthNet-MM classification and significantly reduces input data volume by at least 50% with the vertical mask, resulting in a more efficient model. Generalization experiments show a 5.9% F1-score improvement when applied to the SEN12MS dataset, which has diverse data distributions.

Supervised Deep Neural Networks (DNNs) have proven to be powerful tools for SAR image interpretation tasks. However, they present a formidable challenge in acquiring a substantial amount of labeled data. In this paper, we investigate the promising technique of contrastive self-supervised learning for SAR image classification. This approach allows us to take advantage of a large number of available unlabeled images to pre-train a SAR image classification model. Our novel contrastive learning framework conducts both instance-level and cluster-level pretext tasks, which not only enforce consistency between the images and their augmented "views" at the instance level but also their representation within clusters. Besides generating different views through random, low-level image transformations, we proposed two new strategies to construct positive sample pairs to improve contrastive SAR image feature learning: middle-level optical assistance and high-level graph searching. The middle-level optical assistance strategy is inspired by the observation that domain experts typically interpret SAR images with the aid of optical images. This insight spurs us to generate intermediate SAR images as positive samples from geographically matched optical data using CycleGAN. Furthermore, we augment the positive samples of the image with their KNN (K-Nearest Neighbor) counterparts, following the idea that the KNN samples should belong to the same cluster. Extensive experimental results conducted on the SEN12MS land cover classification benchmark dataset demonstrate that our method is competitive with state-of-the-art self-supervised methods for SAR image classification. Even with only a small amount of labeled data for fine-tuning the model, our method rapidly improves classification performance, surpassing models pre-trained on natural image datasets.

In the last decades, how to detect ships with polarimetric synthetic aperture radar (PolSAR) has become one hot topic. Unfortunately, most of the existing ship detection methods cannot well detect small ships with weak backscattering. To deal with this issue, a ship detection matrix named complete polarimetric covariance matrix [CP] was recently proposed from the perspective of spatial information utilization. Although it is able to improve small ships’ target-to-clutter ratio (TCR) values, its calculation strategy still needs to be rethought due to the possible information loss of some ships. Besides, its mathematical characteristic (i.e., not positive semidefinite) also limits the successful applications of some existing polarimetric theories to it. To overcome these two drawbacks, we here develop an information reconstruction-based polarimetric covariance matrix [IC]. In brief, one new difference calculation strategy is first performed on the Sinclair matrix [$S$], so as to reconstruct its information, by which a feature vector$v$is subsequently extracted with the Lexicographic matrix basis. Then, via further performing an outer product operation on$v$, the matrix [IC] is proposed. Meanwhile, to demonstrate the effectiveness of [IC] in ship detection, two different [IC]-based intensity detectors, respectively, named SPANIC and PEDIC, are designed as well. Experiments carried out on three GF-3 PolSAR datasets show that: 1) the proposed matrix [IC] has a better performance than [CP] and the original polarimetric covariance matrix [$C$] in ship detection and 2) compared to the total power detector SPAN and geometrical perturbation-polarimetric notch filter (GP-PNF), both SPANIC and PEDIC can better detect ships, especially the small ships.

Multiple object tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. MOT remains a challenging task as noisy and confusing detection results often hinder the final performance. Furthermore, most existing research are focusing on improving detection algorithms and association strategies. As such, we propose a novel framework that can effectively predict and mask-out the noisy and confusing detection results before associating the objects into trajectories. In particular, we formulate such "bad" detection results as a sequence of events and adopt the spatio-temporal point process to model such events. Traditionally, the occurrence rate in a point process is characterized by an explicitly defined intensity function, which depends on the prior knowledge of some specific tasks. Thus, designing a proper model is expensive and time-consuming, with also limited ability to generalize well. To tackle this problem, we adopt the convolutional recurrent neural network (conv-RNN) to instantiate the point process, where its intensity function is automatically modeled by the training data. Furthermore, we show that our method captures both temporal and spatial evolution, which is essential in modeling events for MOT. Experimental results demonstrate notable improvements in addressing noisy and confusing detection results in MOT data sets. An improved state-of-the-art performance is achieved by incorporating our baseline MOT algorithm with the spatio-temporal point process model.

We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.

A sensing-assisted predictive beamforming scheme for vehicle-to-infrastructure (V2I) communication is considered, which is built upon massive multi-input-multi-output (mMIMO) and millimeter wave (mmWave) techniques. In practical V2I networks, vehicles cannot be modeled as point targets in terms of the narrow beamwidth and high range resolution. Accordingly, the communication receiver (CR) may be beyond the beam even the vehicle is accurately tracked, which makes robust beam alignment and tracking challenging. We thus consider the extended target case, in which the beamwidth should be adjusted in real-time to cover the entire vehicle. Then an extended Kalman filtering (EKF) is presented to track the CR according to the resolved high-resolution geometry results. Finally, numerical results are provided to validate the effectiveness of the proposed approach.

In this paper, we deal with the issue of discovering data of novel categories for Synthetic Aperture Radar (SAR) images under open-set conditions. The traditional SAR image classification methods are trained under the closed-set setting where all categories in testing data are seen in training data. It does not always meet the requirements of the real SAR imagery interpretation applications. With a labelled SAR image dataset, we propose a multi-stage approach to effectively pick out images belonging to new classes in another unlabelled dataset and then cluster them into correct number of novel categories. To do so, our pipeline is composed of three major steps: (1) train a powerful feature extractor leveraging both the labelled and unlabelled dataset by semi-supervised inference; (2) identify the unknown data by openset detection; (3) cluster these unknown data based on the features generated by the extractor to discover novel categories. The proposed method is validated on a Sentinel-1 SAR image dataset OpenSARUrban [1].

In this paper, we deal with the problem of heterogeneous image classifiers transferring between SAR and optical im-agery through a novel multi-stage domain adaptation technique. The problem of transferring the optical image classifier to SAR and vice-versa is of practical importance because it allows us to leverage plenty of labelled data in the source do-main for the target domain task, but gains little attention. Be-cause there is a drastic distribution-gap between both the opti-cal and SAR imaging modalities, it is non-trivial to apply do-main adaption directly. We propose a multi-stage adversarial feature alignment procedure that firstly performs global ad-versarial feature alignment and then a class-conditional adver-sarial feature alignment is conducted to further enable class-discriminative feature adaption. The proposed method is val-idated on the SEN12MS dataset, and some discussions are provided about heterogeneous domain adaption between SAR and optical imagery.

Deep learning methods exhibit outstanding performance in synthetic aperture radar (SAR) image interpretation tasks. However, these are black box models that limit the com-prehension of their predictions. Therefore, to meet this challenge, we have utilized explainable artificial intelli-gence (XAI) methods for the SAR image classification task. Specifically, we trained state-of-the-art convolutional neural networks for each polarization format on OpenSARUrban dataset and then investigate eight explanation methods to analyze the predictions of the CNN classifiers of SAR images. These XAI methods are also evaluated qualitatively and quantitatively which shows that Occlusion achieves the most reliable interpretation performance in terms of Max-Sensitivity but with a low-resolution explanation heatmap. The explanation results provide some insights into the in-ternal mechanism of black-box decisions for SAR image classification.

In this paper, we focus on synthetic aperture radar automatic target recognition for ships, and modify the Simple Siamese (SimSiam) framework, a contrastive self-supervised representation learning method, to improve ship classification accuracy. We design a novel sampling method that takes polarization information into account, in addition to the image augmentation based positive pair sampling method that is commonly used in contrastive learning approaches. The results of the experiments show that positive pairs of VH and VV polarized images can provide complementary information about ship targets to strengthen the classifiers. Besides, different similarity measurement functions are analyzed in the experiments.

Polarimetric synthetic aperture radar (PolSAR) designed to obtain the polarimetric information of scenes is a crucial tool for microwave remote sensing. Recently, a complete polarimetric covariance difference matrix [CP] was built to detect ships of PolSAR image. Along this work, this paper extends its application to the spectrum domain. Briefly speaking, four sub-aperture images are first separated from the original PolSAR data. Then, four different power values corresponding to the [CP] matrices of these sub-aperture images are respectively calculated. At last, via multiplying these values together, a PolSAR ship detector named MPS (Multiplicative Polarimetric SPAN) is proposed. The experiment carried out on one real PolSAR image demonstrates that, compared to traditional power detectors SPAN and$SPAN_{CP,}$MPS holds a better ability to detect small ships.

Aiming at the problem of ship classification in Synthetic Aperture Radar (SAR) images crossing different imaging satellites, we propose a novel domain adaptation (DA) network. For the proposed DA network, we consider one labeled SAR image dataset as source domain and another unlabeled dataset acquired by a different imaging satellite as target do-main. First, the structural regularization of the source domain is achieved by jointly training the feature classifier and the domain classifier. Then, by minimizing the KL-divergence between the label distribution predicted by the network and the introduced auxiliary distribution, the cluster alignment of the target domain is further realized. The experimental results on the datasets obtained from different satellites verify the performance of proposed method is better than state-of-the-art DA method.

This paper proposes an advanced Bayesian algorithm for estimation of mutant altimetric parameters. A sparse prior is introduced to enforce a mutant evolution of the altimetric parameters. Amaximum a posterior(MAP) estimator based on an alternating optimization algorithm is carried out to fulfill our proposed hierarchical Bayesian model. The proposed Bayesian method and the corresponding estimation algorithm are evaluated using both synthetic and real altimetric data associated with a delay/Doppler altimetric model. The experimental results show that the proposed method brings an improvement on mutant parameter estimation and tracking when compared to smooth estimation and other state-of-the-art estimation algorithms.

This letter focuses on remote sensing image interpretation and aims to promote the use of contrastive self-supervised learning in varied applications of remote sensing image classification. The proposed method is a contrastive self-supervised pre-training framework that encourages the network to learn image representations by comparing image embeddings extracted by different encoders and predictors. Experiments were carried out on a variety of remote sensing image datasets to determine the efficacy of the proposed method for classification tasks. Results show that the proposed framework exploits the capabilities of encoders and outperforms the supervised learning method in terms of classification accuracy. Besides, it takes a few pre-training epochs to find a suboptimal initialization of network weights, and the pre-trained encoders use a little training data to get outstanding classification results, which shows the time and data efficiency of the proposed framework. Code is available at https://github.com/yinxu98/MECo.

Ship detection of polarimetric synthetic aperture radar (PolSAR) plays an important role in marine monitoring and ocean protection. Over the past years, local spatial information around pixels has been successfully applied to this task. However, few works have been done on PolSAR ship detection using the nonlocal spatial information (NSI). Within this context, we here propose one NSI-guided ship detection method PMR. Briefly speaking, the feature power difference (PD) is first constructed by computing the total power difference between the center pixelcand its most similar nonlocal pixeliwithin a 7×7 window. Then, the polarimetric feature reflection symmetry (RS) is introduced into PD to construct the method PMR (i.e., PD Multiply RS) for further enhancing the target-to-clutter ratio (TCR) and improving the ship detection accuracy. Experiments carried out on three real PolSAR datasets show that, in comparison with some other methods, especially the recently proposed local neighborhood information-based ship detector PWFN, PMR is more apt for ship detection. On average, its figure of merit (FoM) and TCR values respectively surpass PWFN0.24 and 12.83 dB.

Synthetic aperture radar (SAR) image classification task when the training and test sets have different distributions can be initially solved using existing domain adaptation (DA) methods. However, considering that none of their classification accuracy is high, this letter proposes an active learning DA classification method to further solve this task. First, an adversarial learning-based DA pipeline is put forth, using labeled source and unlabeled target domains to conduct adversarial learning in order to narrow the domain gap. A prototype regularization process is then built, which further enhances the target domain data clusters’ ability to discriminate between them. In order to fully improve SAR image classification accuracy, we then propose a dynamic hard sample selection process to choose hard samples to supplement into the subsequent stage of training samples. This process involves moving the gradient direction of the query function closer to the gradient direction of the class margin objective function. Extensive experiments on SAR image datasets with different distributions from different imaging platforms and optical remote sensing datasets have verified the effectiveness and superiority of the proposed method.

For synthetic aperture radar (SAR) image classification problem, we need to take into account unlabeled datasets containing unknown classes crossing different satellites. In this letter, a spherical space domain adaptation (DA) network under open set condition is proposed to solve this problem. First, we transform the prior Euclidean feature space into the spherical space to construct a classification network such that features of the same class of SAR images are clustered together and features of different or unknown classes are separated on the hypersphere. Second, a correction module is designed to increase the accuracy of the pseudo-label obtained by the classifier. Then, based on the adversarial learning strategy, we introduce the gradient alignment module to achieve better alignment of the source and target domains. Finally, tests on two SAR benchmark datasets from distinct satellites show that the proposed network outperforms state-of-the-art (SOTA) approaches in terms of classification accuracy.

Ship detection using polarimetric SAR (PolSAR) images has recently been an active topic in the Earth observation field. There, how to detect small ships is an open and challenging issue. Within this context, we put forward a two-stage ship detection model, by which a novel ship detection method is proposed as well. Briefly, in the first stage, a suppression manipulation is adopted to suppress sea clutter, where the feature SVVSOis built on the intensity information with the orientation angle compensation (OAC). In the second stage, an enhancement manipulation is further executed to highlight ships from the suppressed sea clutter, where the features PID (polarimetric intensity difference) and NsD (nonsurface degree) are first constructed with SVVSOand a series of theoretical derivations. Then, via fusing PID and NsD together, the two-stage-based method FPAN is proposed to detect ships. To demonstrate its performance, we apply FPAN to four different L-Band PolSAR datasets. Experimental results reveal that, compared to other state-of-the-art methods, especially the DBSPCPmethod, FPAN is more effective in detecting small ships. On average, its figure-of-merit (FoM) and target-to-clutter ratio (TCR) values are, respectively 9.40% and 25.18% greater than those of DBSPCP, while the time consumption is just 58.67% of the latter.

In the above article[1], the average TCR values inTable IIwere incorrectly presented. The corrected table is given here:

To more effectively detect small ships, in this article, a novel region-based polarimetric covariance difference matrix [RP] is put forward, which mainly consists of two stages. Briefly speaking, in the first stage, a new pixel representation way is proposed to depict the spatial characteristics of pixel, through which the difference information related to pixel’s local region is calculated as well. In the second stage, the global region difference information of pixel is computed. Finally, we construct [RP] via fusing these two different kinds of information together with a balance factor$c$. Meanwhile, considering that the backscattering energy of ships is useful for ship detection, a new intensity-driven polarimetric notch filter (ID-PNFRP) is also derived from [RP]. Three different datasets are adopted to evaluate the effectiveness of [RP] and ID-PNFRP. Experimental results show that: 1) compared with the polarimetric covariance matrix [$C$] and the polarimetric covariance difference matrix [$P$], [RP] is more suitable for ship detection and 2) compared with the original geometrical perturbation-polarimetric notch filter (GP-PNF) and the total power detector SPAN, the proposed method ID-PNFRPcan better detect small ships with greater figure of merit (FoM) and target-to-clutter ratio (TCR) values.

In the face of Synthetic Aperture Radar (SAR) image object detection with different distributions of training and test data, traditional supervised learning methods cannot achieve good detection performance. Domain adaptation (DA) method has been shown to have the ability to solve this problem, but existing DA object detection algorithms all use adversarial DA theory for the detection task, which is ineffective in solving object regression localization in the detection task. In this article, to better solve the above problem, an automatic SAR image ship detection method based on feature decomposition crossing different satellites is proposed. The feature extraction layer of backbone network is divided into low level and high level, where domain-invariant feature extractors are designed for the local features extracted from the low level and the global features extracted from the high level, respectively. We argue that the local and global features extracted from source domain and target domain contain domain-specific features (DSF) for adversarial DA and domain-invariant features (DIF) that contribute to object regression localization. Then, we decompose the local features and global features into DSF and DIF via vector decomposition method. For DSF counterpart, we introduce adversarial DA attention for feature alignment. DIF from the local features are fused into the backbone network for high-level global feature extraction. Finally, by using region proposal network and adversarial domain classifier, we can get the accurate bounding box and object class of SAR image objects. Extensive experiments prove that the proposed method outperforms state-of-the-art methods in terms of detection performance.

Traditional deep learning Synthetic Aperture Radar (SAR) image object detection methods fail to provide effective detection results when faced with SAR image datasets with different joint probability distributions obtained from multiple imaging satellites. In this article, an automatic SAR image object detection method based on domain adaptation is proposed to adapt to unlabeled target domain datasets acquired by different satellites. On the basis of introducing an adversarial domain adaptation learning strategy, we propose Adversarial Learning Attention (ALA) and Compensation Loss Module (CLM) on the baseline network. In ALA, considering the great difference in the scattering intensity of SAR images, the entropy vector can be used to distinguish the high-entropy and low-entropy regions among them and assign the corresponding weights, based on which the adversarial domain adaptation learning attention is proposed to achieve instance-level feature alignment and pixel-level feature alignment in source domain and target domain, respectively. In CLM, the domain alignment of pixel-level feature and instance-level feature of SAR image objects is first implemented, and then to make the feature alignment of both domains more accurate, better aggregation of proposals of different classes of prototype objects in the same domain is required, and further compensation loss is proposed to further restrict the prototype alignment of SAR object features in both domains. We conduct experiments on two SAR datasets obtained from different satellites whose results show the superiority of the proposed method over the state-of-the-art (SOTA) methods and the effectiveness of the proposed module in improving the detection accuracy.

Dark environment becomes a challenge for computer vision algorithms owing to insufficient photons and undesirable noise. To enhance object detection in a dark environment, we propose a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation. In a self-supervision manner, the MAET learns the intrinsic visual structure by encoding and decoding the realistic illumination-degrading transformation considering the physical noise model and image signal processing (ISP). Based on this representation, we achieve the object detection task by decoding the bounding box coordinates and classes. To avoid the over-entanglement of two tasks, our MAET disentangles the object and degrading features by imposing an orthogonal tangent regularity. This forms a parametric manifold along which multitask predictions can be geometrically formulated by maximizing the orthogonality between the tangents along the outputs of respective tasks. Our framework can be implemented based on the mainstream object detection architecture and directly trained end-to-end using normal target detection datasets, such as VOC and COCO. We have achieved the state-of-the-art performance using synthetic and real-world datasets. Codes will be released at https://github.com/cuiziteng/MAET.

Accurate and efficient ship detection in remote sensing images still remains a challenging task due to the large variations of scales, orientations and distributions. In this paper, we propose an anchor-free ship detector that directly regresses ship localization parameters, offering a simpler pipeline over the previous methods. The detection network is then trained in a multi-task fashion which contains not only the ship center-point maps and oriented bounding boxes but the ship masks. Instead of fixing the weights among the multiple task losses, we adopt a curriculum learning strategy which gradually adapts the loss weights during the training process so that the network can learn the discriminative ship features at the early stage and obtain more localization information while training continues. Experimental results on real dataset demonstrate the effectiveness and efficiency of our proposed method.

In Synthetic Aperture Radar (SAR) image classification tasks, the performance depends on both the classifier and the dataset itself. However, in comparison with plenty of SAR classification methods, there is little work aimed at analyzing the distinguishability of the dataset. In the classification dataset, some classes are semantically different but their distinguishability is low, the classes are hard to be classified especially in some more practical cases that there are unknown classes without supervision exist. Referring to open set recognition (OSR), in this paper, we proposed the SAR Distinguishability Analysor (SAR-DA) to evaluate the distinguishability of the OpenSARUrban dataset. By modeling each class as a multivariate Gaussian distribution in latent space, SAR-DA can not only classify the classes having been seen in training phase, but also can recognize unknown samples if a test sample is out of each known distribution. Each class in OpenSARUr-ban is set unknown in turn, then we apply the SAR-DA on the split dataset in OSR and supervised setting. The distinguishability can be reflected by the unknown recognition recall rate. The experimental results show that the unknown recognition recall rate in OSR setting significantly decreased compared with those in supervised setting, indicating that even though the classes in OpenSARUrban are semantically different from each other, the latent distributions of some classes are quite similar and hard to be classified, thus these classes are of low distinguishability.

In this paper, we present a self-supervised learning method of Auto-Encoding Multi-Transformations (AEMT) for airplane classification. In this method, the image features are learned in an unsupervised way by simultaneously estimating multiple image transformations from the features of original and transformed images instead of reconstructing the input images. Besides, we propose two structure variants of the AEMT method: composite and parallel modes of which the former transforms the images in a composite fashion while the latter does it in parallel. The experimental results demonstrate that the proposed method outperforms the state-of-the-art self-supervised learning methods for the airplane classification task.

Spotlight synthetic aperture radar (SAR) achieves very high-resolution (VHR) images by steering the azimuth beam during the formation of the synthetic aperture. In practice, the steering is implemented through discrete azimuth beam switching. Then, phase shifts can occur between the adjacent beams due to the error of the antenna pattern. In addition, the troposphere introduces a beam-angle-dependent delay to the echo. Those undesired phase shifts and delays cause phase errors in the received echo and result in image quality deterioration. In this letter, an algorithm, called the Newton entropy minimization (N-EM), is proposed to estimate and compensate the phase errors caused by the discrete azimuth beam steering for the spotlight SAR data. Combining with the subaperture imaging approach of the spotlight SAR, the algorithm estimates the phase offsets between each couple of the adjacent beams based on the minimum entropy criterion. The analytic expression, which is a nonlinear equation, is developed for the optimal estimation. Then, the Newton's method is employed to solve the equation. The real spaceborne SAR (both the staring and sliding spotlight modes) data processing results demonstrate the efficiency and accuracy of the proposed algorithm.

Satellite and Aerial Image Time Series contain tremendous amounts of information of ground targets in space and time and have been commonly used in temporal pattern analysis tasks of ground targets. It has a wide range of applications including environment monitoring, urban planning, hazard assessment, etc. PV (photovoltaic) field construction monitoring is a new topic with increasing attentions. In this paper, we proposed a simple yet effective network based on LSTM (long short term memory) to detect PV field construction event. We build image time-series dataset from Sentinel-2 data of an Egypt PV field under construction to monitor the project progress. Compared with clustering model and CNNs (convolutional neural networks), our network achieves the better accuracy. Although low resolution and mislabeled pixels limit the accuracy cap, the simple network is still robust and easy to transfer for other applications.

Oil is an essential asset for every country, and plays a key role in world trade system. The detection of oil tanks is a very important task for both military and commerce. Recently, researchers have shown an increasing interest in oil tanks detection in remote sensing imagery. However, the previous works almost used the methods of circle detection, but the real oil tanks in remote sensing imagery are more close to ellipses. In this paper, we propose an oil tanks detector base on an U-shape Fully Convolutional Network(FCN) in optical remote sensing images. The structure of our network consists of three parts: feature extraction part, feature merge part and the output layer. The output layer consists of two output branches, one branch is a score map branch, which generates confidence score to indicate the region of oil tanks at pixel wise, and the other ends up with several channels which regress the ellipse geometric parameters (center, horizontal axis and vertical axis). In addition, we also design a novel loss function adapted to our network. The experimental results conducted on our dataset collected from Google Earth show that this method achieves promising performance on oil tanks detection in terms of both efficiency and accuracy in high-resolution optical remote sensing images.

Deep learning has found many applications in high-resolution remote sensing image interpretation. In this study, an image analysis system is presented, consisting of image segmentation and mineral volume change estimation. In this system, a revised U-Net structure, called Res-U-Net, is proposed by combining U-Net and residual structure for image segmentation. Experiments are performed on the collected high-resolution remote sensing images, which were annotated with iron ore positive, iron ore negative, and background, and the results demonstrate the superiority of the proposed Res-U-Net over other image segmentation methods. Our proposed Res-U-Net outperforms the traditional U-Net by achieving pixel-wise accuracy of 92% and mean intersection over union (mIOU) 86%, as well as faster frame rate of 35 FPS on test dataset.

Automatic building extraction from aerial or satellite images is a dense pixel prediction task for many applications. It demands a large number of clean label data to train a deep neural network for building extraction. But it is labor expensive to collect such pixel-wise annotated data manually. Fortunately, the building footprint data of geographic information system (GIS) maps provide a cheap way of generating building label data, but these labels are imperfect due to misalignment between the GIS maps and images. In this letter, we consider the task of learning a deep neural network to label images pixel-wise from such noisy label data for building extraction. To this end, we propose a general label noise-adaptive (NA) neural network framework consisting of a base network followed by an additional probability transition modular (PTM) which is introduced to capture the relationship between the true label and the noisy label. The parameters of the PTM can be estimated as part of the training process of the whole network by the off-the-shelf backpropagation algorithm. We conduct experiments on real-world data set to demonstrate that our proposed PTM can better handle noisy labels and improve the performance of convolutional neural networks (CNNs) trained on the noisy label data generated by GIS maps for building extraction. The experimental results indicate that being armed with our proposed PTM for fully CNN, it provides a promising solution to reduce manual annotation effort for the labor-expensive object extraction tasks from remote sensing images.

Generally, high-resolution imaging for highly squinted synthetic aperture radar (SAR) data is a difficult problem due to large range migration. Thus, when trying to solve this nontrivial problem, an azimuth-variant Doppler will arise, thereby leading to phase errors containing the azimuth spatial-variant (ASV) component. In this article, we analyze the characteristics of highly squinted SAR data and propose a new full-aperture ASV phase error autofocus algorithm. In this new algorithm, the accurate and suitable phase error signal model for highly squinted SAR data is derived. Moreover, the closed-form solution of the relationship between a distorted image and a focused image is also explicitly revealed. Furthermore, in this newly proposed algorithm, an accurate estimation of nonlinear ASV phase error is established based on the maximum contrast of the SAR imagery. In addition, an iterative gradient-based solver is introduced. The advantage of this new method provides a simple yet effective approach while being able to eliminate the ASV phase errors. More importantly, the accuracy of this new method using the full-aperture data is independent of SAR imaging algorithms. As a result, the proposed new method can be easily embedded in many existing imaging algorithms to produce focused imagery. Finally, two real highly squinted SAR data sets are provided to validate the advantages of our algorithm.

With the development of multi-pass synthetic aperture radar (SAR) imaging, feature enhancement with multiple SAR images has become an important research topic due to its distinct advantage of redundant scene information. The existing SAR image feature enhancement algorithms mainly focus on single SAR image processing by means of ℓq(02,1-norm minimization approach is introduced to achieve the purpose of feature enhancement. In addition, two dimensional fast iterative shrinkage thresholding algorithm (2D-FISTA) is utilized to increase the computational efficiency. Experimental results are illustrated to validate that the proposed method can improve SAR image performance significantly in terms of denoising and sidelobes suppression while maintaining higher computational efficiency.

Airborne multiple-input multiple-output array synthetic aperture radar (MIMO array SAR) can be used to directly obtain the three-dimensional (3-D) imagery of the illuminated region with a single track. Different sparse reconstruction algorithms within the framework of compressive sensing (CS) have been conceived to reconstruct the cross-track signal because of its inherent spatial sparsity. However, the computational complexity and the number of two-dimensional (2-D) SAR images needed for sparse reconstruction of the existing algorithms are usually high. To overcome this problem, the same range line cells based 2-D real-valued CS is proposed in this paper. In this new algorithm, the original signal model is transformed from the complex domain to the real domain by means of unitary transformation. Finally, airborne MIMO array SAR real experimental results are illustrated to validate the prominent advantages of the proposed method.

Full-polarimetric synthetic aperture radar (SAR) images have the ability to provide physical patterns of the earth observation, no more than geometric information. In order to learn physical patterns from non-full-polarimetric SAR images, a complex-valued CNN is leveraged to learn a model containing physical parameters. The parameters are learned from the original complex scattering matrix of full-polarimetric SAR images and they can be adopted to extract physical patterns from non-full-polarimetric SAR images. Cloude and Pottier's H-α division, as the annotation principle, is computed by way of coherence matrix. We perform experiments on (German Aerospace Center) DLR's full-polarimetric, airborne F-SAR data, demonstrating that extracting physical patterns from non-full-polarimetric images is feasible. The comparative results illustrate that: 1) The best physical categoric patterns can be extracted from HV and VH polarimetric images in general, while performance from HH and VV polarimetric images are limited; 2) Cross-polarimetric SAR images have greater ability for surface and volume scattering, while co-polarimetric ones are better for multiple scattering extraction.

Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) with array antennas distributed along the cross-track direction can obtain 3-D scene information of the surveillance region. However, the cross-track resolution is unacceptable due to the length limitation of the MIMO antenna array. The superresolution algorithms within the framework of compressive sensing (CS) have been introduced to recover the cross-track signal because of its inherent spatial sparsity. The existing sparse recovery algorithms for 3-D SAR are attempted to find the sparse solution in the complex domain directly, which requires a very high computational complexity. To overcome this problem, a new fast 3-D imaging algorithm based on real-valued sparse representation is proposed in this paper. In this new algorithm, unitary transformation can be employed to transform the sparse signal recovery model of uniform/nonuniform MIMO array SAR from the complex domain to the real domain. Thus, a real-valued reweighted 12,1-norm minimization model is established. In addition, a modification of the fast iterative shrinkage-thresholding algorithm (FISTA) is used to reconstruct the 3-D image for further improving the computational efficiency. Moreover, the theoretical analysis of computational complexity of the proposed algorithm is derived when compared with an existing complex domain algorithm. Finally, numerical simulations and MIMO array SAR real experimental results are illustrated to validate that the proposed algorithm can reduce the computational complexity significantly in terms of CPU time while still maintaining the inherent advantages of superresolution and robustness against the noise.

Single- and dual-polarimetric synthetic aperture radar (SAR) images provide very limited capabilities to interpret physical radar signatures. For generality and simplicity, we call single-polarimetric, dual-polarimetric, and fully polarimetric SAR (PolSAR) images flexible PolSAR images. In order to sufficiently extract physical scattering signatures from this kind of data and explore the potentials of different polarization modes on this task, this paper proposes a contrastive-regulated convolutional neural network (CNN) in the complex domain, attempting to learn a physically interpretable deep learning model directly from the original backscattered data. To achieve a better deep model containing physically interpretable parameters, the objective cost is compared to and selected from several commonly used loss functions in the complex form. The required ground-truth labels are generated automatically according to Cloude and Pottier's H-alpha division plane, which significantly reduces intensive labor cost and transfers this method to an unsupervised learning mechanism. The boundaries between different scattering signatures, however, sometimes show an erroneous separation. With the aim of aggregating intra-class instances and alienating inter-class instances, meanwhile, a complex-valued contrastive regularization term is computed mathematically and is added to the objective cost by a tradeoff factor. Moreover, data augmentation is applied to relieve the side effects caused by data imbalance. Finally, we performed experiments on German Aerospace Center's (DLR)'s L-band, high-resolution (HR), and airborne F-SAR data. Our results demonstrate the possibility of extracting physical scattering signatures from flexible PolSAR images. Physically interpretable potentials of SAR images with different polarization modes are analyzed, and we conclude with physical signature identification.

The marine atmospheric boundary layer (MABL) roll plays an important role in the turbulent exchange of momentum, sensible heat, and moisture throughout the boundary layer of tropical cyclones. Hence, rolls are believed to be closely related to storm development and intensification. In this study, based on the RADARSAT-2 dataset including various tropical cyclone (TC) intensities, the roll characteristics are retrieved from synthetic aperture radar (SAR) images via fast Fourier transform (FFT). We investigate the roll wavelengths at variance of TC intensities and found that the roll wavelengths are related to the distance with respect to TC center and TC intensities. This study is promising to bring roll-induced effects into hurricane forecasting model.

A new study based on the complex and amplitude cross-pol correlation is made. It exploits Sentinel-1 Single Look Complex (SLC) SAR and reference HSCAT wind data. The behavior of these two polarimetric features vs wind speed is analyzed. Low-to-moderate and high wind speed regimes are considered. The study is focused on three subsets characterized by different incident angle ranges. The study shows that in all cases the two polarimetric features are sensitive to wind speed and better behave with respect to$S$vv.

Ship detection from high-resolution optical remote sensing images has been a prevalent domain in recent years. Unlike objects in natural images, ships of interest can be anywhere in optical remote sensing images with multi-scale and multi-oriented which makes it more different to be detected. In this paper, we propose a novel method based on the fully convolutional network to detect ships. Our method has three important components: 1) we design a network merging different levels of feature map to fuse multi-scale information. Determining the existence of large ship require features from deep layers in the network, while predicting rotated bounding box enclosing small ships need shallow layers information; 2) The network can be trained end-to-end to generate score maps which indicates the confidence score for the ship region of interest in pixel-wise level through all locations and scaled of an image; 3) We design a rotated bounding box regression model to localize the ships. The experimental results on our dataset collected from Google Earth has demonstrated our proposed method achieves promising performance on ship detection in terms of both efficiency and accuracy in high-resolution optical remote sensing images.

Ship detection from remote sensing images can provide important information for maritime reconnaissance and surveillance and is also a challenging task. Although previous detection methods including some advanced ones based on deep convolutional neural network expertize in detecting horizontal or nearly horizontal targets, they cannot give satisfying detection results for arbitrary-oriented ship detection. In this letter, we introduce a novel ship detection system that can detect arbitrary-oriented ships. In this method, a rotated region proposal networks (R2PN) is proposed to generate multiorientated proposals with ship orientation angle information. In R2PN, the orientation angles of bounding boxes are also regressed to make the inclined ship region proposals generated more accurately. For ship discrimination, a rotated region of interest pooling layer is adopted in the following classification subnetwork to extract discriminative features from such inclined candidate regions. The proposed whole ship detection system can be trained end to end. Experimental results conducted on our rotated ship data set and HRSD2016 benchmark demonstrate that our proposed method outperforms state-of-the-art approaches for the arbitrary-oriented ship detection task.

In this paper, we present a method of ship size extraction for Sentinel-1 synthetic aperture radar (SAR) images, which is composed of the image processing stage and the regression stage. In order to achieve extraction with high accuracy, considering the data characteristics of Sentinel-1 images, we propose to use the dual-polarization fusion and the nonlinear regression with the gradient boosting. The experiments and analyses on a relatively large data set show that: 1) compared with the existing and related studies, the proposed method achieves an improved performance. The extraction errors are pushed under one pixel, and they are 4.66% (8.80 m) and 7.01% (2.17 m) for length and width, respectively; 2) the dual-polarization information fusion does improve the size extraction accuracy; and 3) the nonlinear regression does exploit the relationship between the influential factors and the size parameters and provide a better performance than the linear regression. The experimental results verify that the proposed design is suitable for ship size extraction in Sentinel-1 SAR images.

In this paper, we propose a superpixel generation method for synthetic aperture radar (SAR) images by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The pixels is firstly grouped to generate initial superpixels by using probabilistic patch-based (PPB) dissimilarity. Then, small clusters are combined into their neighbor superpixels to get final results through a distance measurement defined by statistical models. Experiments on simulated data sets exhibit high boundary adherence of the generated superpixels and demonstrate the availability and efficiency of the proposed method.

Performance of ship detection is influenced by synthetic aperture radar (SAR) imaging characteristics and environmental conditions. In this paper, aiming at evaluating vessel detectability for Sentinel-1 SAR data, a model based on a large-scale Sentinel-1A vessel chips database is established. The model sensitivity is analyzed by simulation data. In the experiment, by inputting the parameters of imaging characteristics (incidence angle, polarization, spatial resolution) and environmental conditions (wind speed, wind direction, sea state) of a specific Sentinel-1 image, the minimum detectable vessel length can be estimated. Further validations demonstrate the availability of the estimated minimum detectable vessel length.

This paper presents a new algorithm for recognizing patterns of densely arrayed targets in high resolution (HR) SAR images, serving the increasing demand for target detection and recognition in HR/VHR SAR images. The novelty of our work is to formulate the problem of pattern extarction as a jigsaw puzzle with similar target patches. Compared to existing work, our algorithm has multiple advantages, including: 1) extracting densely arrayed targets region from full scale SAR images automaticly; 2) predicting accurate displacement vector between neighboring similar patches; 3) synthetising an uniform target pattern with similar patches. These advantages are demonstratced by results of pattern extraction from a study case in the Davis-Monthan air force base.

Synthetic Aperture Radar (SAR) image land cover classification is an important task in SAR image interpretation. Supervised learning, such as Convolutional Neural Network (CNN), demands instances which are accurately labeled. However, a large amount of accurately labeled SAR images are difficult to produce. In this paper, a Probability Transition CNN (PTCNN) is proposed for patch-level SAR image land cover classification with noisy labels. Firstly, deep features are extracted by a CNN model, followed by a probabilistic transition model, where true labels are treated as hidden variables and the posterior probabilities of true labels are transferred into their noisy versions. The whole network is trained with Caffe in a uniform fashion and a land cover database is used to produce noisy labels, which are randomly chosen with various proportions. Experimental results demonstrate that the proposed PTCNN model is robust to noise and gives a promising classification performance. Therefore, the PTCNN model may lower the standards for the quality of image labels, and shows its availability in practical applications.

Fine-scale classification in form of object extraction or segmentation for high resolution SAR images is a challenging task due to the existing local noises, object deformation and part missing. A novel SAR classification method based on CRFs which combines low-level features, label context and object structure priors is presented in this paper. Local label pattern is proposed in this paper to model the object structures by measuring the local label configuration on the grid layer of SAR images. We build a new CRFs model with label context and object structure priors for image classification. Besides, we adopt Mean Field approximation for efficient inference of our CRFs model. This work intends to implement an efficient classification framework by integrating high-level label context and object priors and apply it to fine-scale object extraction of SAR images. The framework demonstrates good performance in both accuracy and efficiency for object extraction or segmentation of simulated images and high resolution SAR images.

In this paper, we propose a fast PolSAR image superpixel segmentation method. This method takes a simple coarse-to-fine optimization technique to minimize a Markov-Random-Field (MRF) like energy function which integrates the Pol- SAR image statistic, spatial position and boundary smoothing. It updates boundary of superpixels staring with a large block level and iterates down to the final pixel level. We demonstrate the performance of our approach both on the synthetic and real full polarimetric images , showing that our proposed approach can achieve significantly faster convergence than SLIC method, and make a good compromise between accuracy and computation speed.

Convolutional Neural Network (CNN) has attracted much attention for feature learning and image classification, mostly related to close range photography. As a benchmark work, we trained a relatively large CNN to classify SAR image patches into five different categories, where the image patches tiled and annotated from a typical TerraSAR-X spotlight scene of Wuhan, China. The neural network designed in this paper consists of seven layers, including one input layer, two convolutional layers where each followed by a max-pooling layer, as well as two fully-connected layers with a final five-class softmax. Using the toolkit caffe, we achieved the training and testing accuracy of 85.7% and 85.6% respectively, which is considerably better than the traditional feature extraction and classification based SVM method and shows great potential of CNN used for SAR image interpretation. In order to accelerate the training process, a very efficient GPU implementation was employed.

In order that global, dominant, and complete outlines of land covers are delineated, in this paper, we propose a regularized L0gradient minimization method which is specially developed for segmenting polarimetric synthetic aperture radar (PolSAR) images, and present a region-based stepwise design to implement it. The performance of the proposed method is tested and analyzed on two experimental data sets, with visual presentation as well as numerical evaluation. They both confirm that the proposed method achieves its principal goal and demonstrate its availability and advantage as a pre-processing step for PolSAR image interpretation chains.

It is believed that it is essential to take the spatial adaptivity into the segmentation method for polarimetric synthetic aperture radar (PolSAR) images. The size and shape of each segment and the strength of the relationship of neighboring pixels need to depend on the local spatial complexity of the scene. The wedgelet framework provides a promising analysis tool for spatial information. The major advantage of the wedgelet analysis is that it captures the geometrical structure of images at multiple scales, with the local spatial complexity taken into consideration. Hence, in this paper, we propose a wedgelet approximation and analysis framework specially designed for PolSAR data. Based on this framework, a spatially adaptive representation and segmentation method is constructed and presented. It mainly consists of three parts: first, the multiscale wedgelet decomposition is applied to the PolSAR image, and the local geometrical information is captured in an optimal way; then, the image is segmented in a spatially adaptive manner by the multiscale wedgelet representation in the form of the regularized optimization, which keeps a balance between the approximation and parsimony of the representation; the final part is the spatial-complexity-adaptive segmentation refinement based on the Wishart Markov random field model. The performance of the proposed method is presented and analyzed on two experimental data sets, with visual presentation and numerical evaluation. It is also compared with an existing and theoretically well-founded segmentation method. The experiments and results demonstrate the availability and advantage of the proposed method.

The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach outperforms most state-of-the-art spectral CS recovery approaches in fidelity, tolerance to noise and computation efficiency.

In this paper, we propose a method for lossy audio signal compression via structured sparse decomposition and compressed sensing (CS). In this method, a least absolute shrinkage and selection operator (LASSO) is employed to sparse and structured decompose the audio signals into tonal and transient layers, and then, both resulting layers are compressed by a CS method. By employing a new penalty term, which takes advantage of the structure information of transform coefficients, the LASSO is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two resulting layers, thus improving the performance of CS. Experimental results showed that the new method provided a better compression performance than conventional methods did.

In this paper, a multi-temporal (MT) superpixel generation method is proposed. MT superpixels are generated based on a novel edge extraction method designed for high resolution (HR) synthetic aperture radar (SAR) images and using MT spatial information in an optimization way. Numerical evaluation and comparison on simulated and real data sets demonstrate its availability for MT HR SAR image analysis.

As the need for radar applications has become more urgent, conventional radar performance does not meet the current demand for observation. In recent years, the MIMO radar is considered to have great potential. MIMO SAR can get more phase center for imaging, interference, GMTI, or any other application. Waveform design is the key point of MIMO SAR, and it's also the current biggest bottlenecks of MIMO SAR implementation. We prove that the DFCW/OFDM signal can hardly meet the required performance for MIMO SAR while the experiments focus on extended targets imaging.

The classic region-based filter for edge extraction for polarimetric synthetic aperture radar images is theoretically founded and efficient. However, in practical use, its performance is limited because the assumption of independence and identical distribution is often not met, particularly in heterogeneous areas. In this letter, we present a degenerate filter design integrated with the weighted maximum likelihood estimation to overcome this limitation. The performance of the proposed methodology is presented and analyzed on both simulated and real experimental data sets using visual presentation, as well as numerical evaluation and comparison with the classic method. They both demonstrate the availability and advantage of the proposed method.

In this letter, the local degree-of-freedom (LDOF) theorem for reduced-dimension space-time adaptive processing (STAP) methods is presented, and a rigorous proof is provided. LDOF is more valuable for practical STAP methods than conventional full degrees of freedom. The effectiveness of the LDOF theorem is verified, and the influences of some operations in practice on the LDOF are analyzed by simulations.