InSAR-based scene matching navigation is a cutting-edge autonomous navigation scheme for GNSS-denied environments. However, due to the inherent speckle noise of SAR systems and texture differences between real-time and reference interferograms, the extracted matching feature points inevitably contain observation noise and outliers. Meanwhile, current mainstream platform localization inversion primarily relies on simplified airborne SAR imaging geometric models. Lacking error suppression mechanisms, these models cause the matching observation errors to directly propagate and amplify into platform positioning errors. To address these issues, this paper proposes a robust localization estimation model named Inv-RD. By incorporating time-dependent payload orbital equations, the inherently underdetermined Range-Doppler (RD) inversion problem is reframed into an overdetermined nonlinear optimization problem based on redundant observations. Subsequently, the Levenberg-Marquardt (L-M) algorithm is employed for global optimization, which significantly mitigates the impact of matching noise on localization accuracy. Experiments using measured flight data demonstrate that, even in the presence of matching errors, the proposed Inv-RD model significantly outperforms traditional geometric models in terms of positioning precision.

Generalized category discovery (GCD) aims to group unlabeled samples from known and unknown classes when only part of the labeled data in the known classes is given. It allows the model to adapt to dynamic environments by discovering novel categories. However, when we applied the GCD approach to the decentralized open world, we still encountered the following challenges: (1) none of labeled data easily obtained in the open world, (2) heterogeneous label spaces across different environments, (3)representation degradation caused by fine-tuning models with limited data in specific environments. To address the above challenges, we introduce a new and practical task, namely Cloud-edge GCD (CE-GCD). Different from semi-supervised GCD, CE-GCD assumes that we only have a base model trained on common public categories, and aims to perform personalized unsupervised novel category discovery in multiple environments with heterogeneous label spaces. Data from different environments or clients cannot be shared, only model parameters can be transferred. To tackle this problem, we propose a novel GCD framework based on energy-guided known class discrimination and multi-level contrastive learning. In each client, we first use the classifier of the base model to distinguish between known and unknown classes, and then perform unsupervised learning on the unknown classes. Each client transfers category information through prototypes to assist learning. Extensive experiments on multiple datasets demonstrate the effectiveness of our approach.

Content-based Music Retrieval (CBMR) is a fundamental task in music information retrieval, encompassing sub-tasks including Audio Identification, Audio Matching, and Version Identification. Traditional methods typically analyze audio signals or spectrograms to extract features related to rhythm, melody, harmony, and timbre. However, with the rapid development of Music Transcription and digital music technologies, MIDI representation has emerged as a powerful alternative fo r music analysis. In this paper, we propose MIDI-Zero, a novel self-supervisedlearning framework for CBMR that operates entirely on MIDI representations. Unlike existing approaches, MIDI-Zero requires no external training data; all training data is automatically generated based on predefined task rules, eliminating the need for labeled datasets or external music collections. MIDI-Zero is designed to handle both symbolic music data and audio-based tasks by leveraging Music Transcription models. Its strong robustness ensures effectiveness even with low-quality transcriptions. Extensive experiments demonstrate that MIDI-Zero achieves competitive performance across various CBMR sub-tasks, particularly excelling in Audio Matching. Our approach simplifies the feature extraction process, bridges the gap between audio and symbolic music representations, and offers a versatile and scalable solution for music retrieval.

Most existing adversarial attack methods for remote sensing images merely add adversarial perturbations or patches, resulting in visually unnatural modifications. Clouds are common atmospheric effects in remote sensing images. Generating clouds on these images can produce adversarial examples better aligning with human perception. In this paper, we propose an adversarial attack framework that leverages natural cloud patterns as perturbations. Common Perlin noise-based cloud generation is a random, non-optimizable process, which cannot be directly used to attack the target models. We design a Perlin Gradient Generator Network (PGGN), which takes a compact gradient parameter vector (gradient vectors, coefficients, and scaling factors) as input and generates multiscale Perlin noise gradient grids. Through hierarchical computations, these grids produce scale-specific cloud masks, which are adaptively fused via learnable mixing coefficients and scaling factors. Crucially, the entire cloud generation process is formulated as a black-box optimization problem, where the cloud parameter vector is iteratively refined using the Differential Evolution (DE) algorithm. This approach enables query-efficient black-box attacks by directly aligning cloud shapes with adversarial objectives, while preserving natural cloud textures. Comprehensive experiments demonstrate the strong attack capabilities of this method, along with its high query efficiency. Furthermore, we conduct an in-depth analysis of the transferability of the generated adversarial examples and their robustness in adversarial defense scenarios.

Classification of hypersaline water bodies, e.g., salt fields and salt lakes, presents unique challenges due to the similar spectral and scattering characteristics of various saline water bodies. To address this issue, we propose an innovative classification method tailored for such environments, integrating Sentinel-1 multitemporal polarimetric synthetic aperture radar (PolSAR) data with Landsat multispectral imagery. The method introduces a novel PolSAR-based feature, termed scattering mechanism entropy, to quantify variations in salt crystal precipitation processes. Additionally, the blue band from multispectral imagery is leveraged to represent ion concentrations in hypersaline water bodies. By deriving the statistical relationship between scattering mechanism entropy and blue band data for each pixel, we amplify the separability of salt features and mitigate the influence of spectral similarity. These derived features are then concatenated into a Chernoff distance-based classifier for improved classification performance. To validate the robustness and effectiveness of this method, we apply it to the classification of salt fields and salt lakes on four major salt lake sites in China: Qarhan salt lake (2018–2020), Yiliping salt lake (2021–2023), Taijnar salt lake (2019–2023), and Gasikule salt lake (2019–2023). The proposed approach achieves classification accuracies of 90.91%, 92.73%, 97.40%, and 97.65%, respectively, significantly outperforming existing water body classification methods.

Deploying deep learning-based remote sensing image interpretation models in orbit can alleviate data downlink pressure and enhance processing efficiency. To adapt to the arithmetic conditions of on-orbit platforms, deep learning models must undergo compilation before deployment, where the search and measurement of operator scheduling are critical aspects. The existing compilation methods related to the Tensor Virtual Machine (TVM) compiler suffer from inefficiencies caused by suboptimal starting point selection and extensive search spaces during the search process. Additionally, the clustering algorithm employed during the measurement of candidate scheduling relies on fixed dimensions and lacks flexible application management, resulting in slow convergence. This paper proposes corresponding optimization methods built upon the TVM framework to address these two challenges. In the search aspect, techniques such as starting point planning, salient reduction, and model sharing considerably reduce search time. In the measurement aspect, the clustering process is optimized through adaptive scheduling management and dimension adaptation, enhancing measurement efficiency. Experimental results indicate that, compared to current state-of-the-art deep learning compilers, the proposed method increases compilation speed by an average of 2.6 times while maintaining consistent inference speed.

Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories. In this paper, we propose a novel generative and fine-tuning framework, LTGC, to handle long-tail recognition via leveraging generated content. Firstly, inspired by the rich implicit knowledge in large-scale models (e.g., large language models, LLMs), LTGC leverages the power of these models to parse and reason over the original tail data to produce diverse tail-class content. We then propose several novel designs for LTGC to ensure the quality of the generated data and to efficiently fine-tune the model using both the generated and original data. The visualization demonstrates the effectiveness of the generation module in LTGC, which produces accurate and diverse tail data. Additionally, the experimental results demonstrate that our LTGC outperforms existing state-of-the-art methods on popular long-tailed benchmarks.

Phase filtering based on deep learning is currently one of the hot research directions in the processing of interferometric SAR data. In this paper, a filtering model is designed using a deep learning framework and the non-local characteristics of interferometric phase maps. The method divides the interferometric phase map into image patches, with each undergoing feature extraction via structurally identical, weight-sharing encoders and decoders to leverage the map’s non-local similarities. Experimental outcomes show this approach outperforms conventional spatial-domain, transform-domain, and deep learning-based filtering methods.

Research about SAR target recognition has received a lot of attention in recent years. The relevant research results encountered some obstacles in their practical application. This is partly due to the long-tailed distribution of data in real-life scenarios. Specifically, a majority of data samples are concentrated in a few categories. The skewed distribution can cause learning bias toward the majority class. Although there have been some studies on long-tailed recognition for natural images, we found that these methods still need improvement when applied to SAR scenes. On the one hand, these studies use balanced datasets for performance testing, which is incompatible with imbalanced SAR target recognition. On the other hand, the predictions of these methods are unreliable for identifying high-value tail class SAR targets that are sensitive to faults. To address these issues, We propose a SAR long-tailed target recognition method based on evidence learning. The evidence learning head can output uncertainty as a measure of predictive confidence. On this basis, we demonstrate the use of uncertainty to further optimize the long-tailed SAR recognition performance and achieve optimal performance when the predicted distribution is unknown.

Compact Polarimetric (CP) SAR has a unique advantage in marine target observation, which can obtain rich polarization information and maintain a large observation width. However, due to the Doppler effect, moving ship targets are prone to ghost during the imaging process. Since CP data lose some scattering information, it is more difficult to distinguish the target from interference. This paper proposes a multi-feature collaboration and enhancement method for removing ghosts from the CP SAR ship target. The ship and ghost interference features are enhanced by multi-feature collaborations, then the contrast between ship and ghost interference is enhanced using an extremum separation feature descriptor, and finally, the ghost interference is removed using a signal-to-clutter ratio based Interference Feature Filter (IFF). Given limited CP data for ships with ghost phenomenon, the experiments simulate Circular Transmit and Linear Receive (CTLR) mode data using GF-3 fully polarimetric data, and the results show that this method can remove the ghost around the ship in the CP image better.

A ground-based simulation system is necessary to verify the feasibility of real spaceborne Synthetic Aperture Radar (SAR) systems. Since echo simulation and image generation are computationally complex, parallel acceleration for SAR systems has been an active research area. However, most of the acceleration algorithms utilize compute unified device architecture (CUDA) as the programming platform, that works only on NVIDIA’s GPUs. This paper proposes a parallel optimization algorithm for spaceborne SAR echo simulation and imaging in Open Computing Language (OpenCL). In the echo simulation module, we optimize the Fast Fourier Transform (FFT), the calculation of range echoes, and the synchronization operation. In the imaging module, the data is decomposed to reduce the computational burden, and matrix transpose and phase factor multiplication are optimized using OpenCL. The architecture and steps needed to extract parallelism in the implementation of the algorithm for accelerated SAR echo simulation and imaging are described in detail. Benefiting from the better generality of OpenCL, this algorithm can be used to program General-Purpose Graphics Processing Units (GPGPU), i.e., CPUs, GPUs, and other types of processors. The performance is promoted by orders of magnitude compared with CPU-based implementation.

Compact Polarimetric (CP) SAR has a larger imaging bandwidth compared to full-polarimetric SAR and more polarization information than dual-polarimetric SAR, which has significant potential in maritime ship detection. The non-uniform scale of ship targets and complex backgrounds present detection challenges. This paper proposes an improvement to YOLOv8 by employing deformable convolutions in the backbone network and adding attention mechanisms in the network neck. Deformable convolutions excel at extracting multi-scale features of ships with strong expressive capability, while attention mechanisms suppress the learning of background features. The paper utilizes a CP SAR dataset constructed by using high-information-content SPAN images and augments the dataset. In comparison with the experimental results of the standard YOLOv8 model, our method demonstrates an improvement of 3.2% in recall, 3.6% in precision, and 1.5% in mAP. The results indicate the effectiveness of our approach in the task of ship detection using CP SAR.

Audio Identification aims to precisely retrieve exact matches from a vast music repository through a query audio snippet. The need for specificity and granularity has traditionally led to representing music audio using numerous short fixed-duration overlapped segment/shingle features in fingerprinting approaches. However, fingerprinting imposes constraints on scalability and efficiency, as hundreds or even thousands of embeddings are generated to represent a music audio. In this paper, we present an innovative self-supervised approach called Angular Margin Guided Embedding (AMG-Embedding). AMG-Embedding is built on a traditional fingerprinting encoder and aims to represent variable-duration non-overlapped segments as embeddings through a two-stage embedding and class-level learning process. AMG-Embedding significantly reduces the number of generated embeddings while achieving high-specific fragment-level audio identification simultaneously. Experimental results demonstrate that AMG-Embedding achieves retrieval accuracy comparable to the based fingerprinting approach while consuming less than 1/10th of its storage and retrieval time. The efficiency gains of our approach position it as a promising solution for scalable and efficient audio identification systems.

Recently, many effective methods have emerged to address the robustness problem of Deep Neural Networks (DNNs) trained with noisy labels. However, existing work on learning with noisy labels (LNL) mainly focuses on balanced datasets, while real-world scenarios usually also exhibit a long-tailed distribution (LTD). In this paper, we propose an online category-aware approach to mitigate the impact of noisy labels and LTD on the robustness of DNNs. First, the category frequency of clean samples used to rebalance the feature space cannot be obtained directly in the presence of noisy samples. We design a novel category-aware Online Joint Distribution to dynamically estimate the category frequency of clean samples. Second, previous LNL methods were category-agnostic. These methods would easily be confused with noisy samples and tail categories’ samples under LTD. Based on this observation, we propose a Harmonizing Factor strategy to exploit more information from the category-aware online joint distribution. This strategy provides more accurate estimates of clean samples between noisy samples and samples with tail categories. Finally, we propose Dynamic Cost-sensitive Learning, which utilizes the loss and category frequency of the estimated clean samples to address both LNL and LTD. Compared to extensive state-of-the-art methods, our strategy consistently improves the generalization performance of DNNs on several synthetic datasets and two real-world datasets.

While deep learning-based methods have made significant strides in remote sensing applications, the scarcity and inadequate quality of remote sensing images tend to curtail the improvement of follow-up research such as remote sensing object detection. However, the human visual system is able to quickly grasp the features of an unseen object given only a few examples, which is considered to be related to a strong shape bias. Inspired by how human toddlers learn shapes and the process of recognizing objects by shape, this paper proposes a novel method known as Shape-Prior based Generated Content (SPGC) data augmentation to overcome these challenges. Specifically, our method includes two main steps: shape data generation and stylization. Initially, the method begins with generating shape data regardless of training data availability. Next, we enhance the robustness of the generated shape data through stylization, forming a robust shape dataset. Stylization is further bifurcated into two scenarios: when training data is unseen, self-stylization is employed where the shape data simultaneously serves as content and style data, resulting in significant performance improvements. When training data is accessible, data-specific stylization is applied, with the shape data as content and training data as style, leading to more substantial enhancements than self-stylization. Experimental results on mainstream remote sensing object detection datasets including NWPU VHR-10, DIOR, and FAIR1M demonstrate that our method significantly improves performance and underscores its effectiveness.

SAR ship detection is an important technology supporting water traffic monitoring and marine safety maintenance. In recent years, many methods based on deep neural networks have been used to improve the performance of SAR ship detection. These methods mainly focus on two issues: one is the false alarm of ship detection in complex inshore environments, and the other is the effective extraction and utilization of SAR ship features. The topic discussed in this paper is one of the culprits that has caused the aforementioned two problems, but has long been overlooked. Specifically, it pertains to the issue of intra-class imbalance in SAR ship detection. There are imbalances in the size distribution, azimuth distribution, and background distribution under the real data collection environment. However, since SAR ship detection is a single-class detection task, the aforementioned imbalances lack reliable descriptors during training. This paper proposes using evidence learning to obtain the epistemic uncertainty as a descriptor of biased learning on samples. Contrastive learning is used to further utilize the uncertainty label of samples to correct biased learning under intra-class imbalance. The proposed method is proven to be effective on multiple network models. AP50 reaches 94.8% on the HRSID dataset, 98.4% on SSDD dataset and 80.9% on the LS-SSDD dataset, both achieving SOTA performance.

Multi-temporal PolSAR data captures the temporal variations in polarization parameters, enabling more accurate land cover classification. Most existing multi-temporal PolSAR features rely on comparing only two-time points. These approaches can be limited in capturing cumulative changes over a longer period. To better represent the cumulative changes of land cover in the entire time-series, this paper proposes a multi-temporal PolSAR feature, namely time correlation entropy. We first extract the dominant scattering mechanism of targets from the polarization covariance matrices using matrix decomposition. Then the time correlation matrix is constructed by comparing all dominant scattering mechanism pairs in the time series. From the Shannon entropy, the entropy of the time correlation matrix, i.e., time correlation entropy, is derived to indicate the degree of changes in the land cover during the observation period. Finally, the maximum entropy principle is further applied to prove that this entropy conforms to a normal distribution. Following this corollary, a classification method based on the interval estimation of distribution parameters is proposed. We evaluate the proposed feature and classification on the salt lake classification application in Qarhan Salt Lake and Gasikule Salt Lake using Sentinel-1 images. Compared to common PolSAR features and classification methods, our method gains the best results. Besides, its results also have better regional consistency and noise resistance.

Traditional absolute radiometric calibration methods for synthetic aperture radar (SAR) face challenges in terms of flexibility, maintenance, and calibration frequency. In contrast, radiometric cross calibration can achieve rapid and timely calibration by utilizing the calibrated SAR satellites to illuminate the same ground targets. However, there are still two factors limiting the accuracy of cross calibration. First, two satellites used for cross calibration often have different incidence angles, whereas the existing methods for correcting incidence angle differences have poor performance in scene adaptation and overcorrection. Second, the stability of ground targets plays a critical role in effective cross calibration, but in practice, not all targets possess the same stability. To address the first issue, this article proposes a novel scene-driven incidence angle difference correction method. It leverages the historical information about the target scenes to determine the evaluation threshold for data blocks. Moreover, it incorporates the adaptive exponential cosine model to correct the scattering variations caused by the difference in incidence angle. To address the second issue, an uncertainty analysis method is employed to calculate the uncertainty of each data block. Then, these uncertainties are utilized to calculate weight coefficients, and the calibration constant is determined using a weighted least squares (WLS) model. Cross-calibration experimental results on the Sentinel-1A/B demonstrate an average reduction of 21.6% in the relative calibration error and 18.6% in the root-mean-square error (RMSE) compared with the traditional method, validating the effectiveness of the proposed method.

Recently, multi-expert methods have led to significant improvements in long-tail recognition (LTR). We summarize two aspects that need further enhancement to contribute to LTR boosting: (1) More diverse experts: (2) Lower model variance. However, the previous methods didn’t handle them well. To this end, we propose More Diverse experts with Consistency Self-distillation (MDCS) to bridge the gap left by earlier methods. Our MDCS approach consists of two core components: Diversity Loss (DL) and Consistency Self-distillation (CS). In detail, DL promotes diversity among experts by controlling their focus on different categories. To reduce the model variance, we employ KL divergence to distill the richer knowledge of weakly augmented instances for the experts’ self-distillation. In particular, we design Confident Instance Sampling (CIS) to select the correctly classified instances for CS to avoid biased/noisy knowledge. In the analysis and ablation study, we demonstrate that our method compared with previous work can effectively increase the diversity of experts, significantly reduce the variance of the model, and improve recognition accuracy. Moreover, the roles of our DL and CS are mutually reinforcing and coupled: the diversity of experts benefits from the CS, and the CS cannot achieve remarkable results without the DL. Experiments show our MDCS outperforms the state-of-the-art by 1% ~ 2% on five popular long-tailed benchmarks, including CIFAR10-LT, CIFAR100-LT, ImageNet-LT, Places-LT, and iNaturalist 2018. The code is available at https://github.com/fistyee/MDCS

Common remote sensing images usually contain targets such as airports, stations, stadiums, mountains, lakes and so on. The localization of these targets is of great research importance. However, a large-scale annotation of these images is difficult to be obtained, because these labels can only be annotated manually, and the annotation is undoubtedly very time-consuming and laborious. In the paper, we propose a framework to generate localization annotations via semi-supervised training. Firstly, optical remote sensing images, with or without target objects, are automatically collected, and a binary classification model is trained on these images. Then the feature map, as a preliminary candidate region for targets, can be obtained from the model. Finally, a saliency detection method based on transformer is employed to further refine the candidate regions and get more precise localization annotations. The proposed framework is a semi-supervised approach to automatically generate large-scale precise location annotation datasets, which can further help many related researches.

Cloud detection plays an essential part in optical remote sensing data processing. Cloud detection technology has made significant progress, with the rapid development of deep learning technology in image processing. Then, Since Transformer has excelled in the image semantic segmentation task, researchers have begun experimenting with introducing Transformer into cloud detection tasks to tackle the difficulties in acquiring information on the ground due to the cloud cover. Compared to other detection problems, cloud detection is difficult to obtain large amounts of annotated data for training models because of poor interpretability and fewer applications. In order to solve these problems, in this paper, we propose a cloud detection method based on scene synthesis and Transformer. To address the difficulty of thin cloud scenarios with small amounts of data, we segmented the cloud target by multi-channel merged region growing algorithm after Gabor filtering, and then randomly embeded the cloud scene into reasonable regions to synthesize the cloud detection dataset. In addition, we designed a Trans-CloudNet with combination loss to improve the class imbalance problem in the training data by increasing the inter-class weight coefficients while comparing the image similarity. Experiments demonstrate that the method we proposed has excellent performance on thin cloud detection.

The use of deep learning technology in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has attracted significant attention. However, it has been noted that deep learning is vulnerable to adversarial attacks, and of course SAR ATR is no exception. Notably, several studies in the past two years have generated SAR adversarial examples, which can successfully deceive a specific well-trained SAR ATR deep network. Considering that research on optical adversarial attacks has progressed toward adversarial examples in the physical world, this paper aims to examine the feasibility of SAR adversarial examples in the physical world. Inspired by Google Sticker, we propose a SAR ATR adversarial-patch-based deception method, namely SAR sticker. The images crafted by our SAR sticker exhibit marked resemblance to their corresponding original images, yet they possess great potential in launching potent attacks on state-of-the-art SAR ATR models, achieving a 76% fooling rate.

In recent years, it has been a research hotspot to apply big data-driven deep learning methods to Synthetic Aperture Radar (SAR) target recognition with limited data. However, the problem caused by the long-tailed characteristics of SAR data has long been ignored. Specifically, a majority of data samples are concentrated in a few categories, leading to a skewed distribution of data. This skewed distribution can cause learning bias towards the majority class, which can subsequently degrade the recognition performance of the minority class. This issue is further exacerbated in limited sample conditions for SAR target recognition. After conducting research on target recognition for long-tailed natural images, this study has found that the existing methods used in this field cannot be easily applied to SAR target recognition. The primary reason is that SAR image data exhibit simultaneous and complex inter-class and intra-class long-tailed distributions. In response to this issue, we proposes the use of a multi-branch expert network and dual-environment sampling to address the long-tail problems in both inter-class and intra-class scenarios. The proposed method outperforms popular long-tailed target recognition methods on the long-tailed versions of the MSTAR and FUSAR datasets.

Multi-temporal polarimertic SAR is considered to be very effective in crop classification and cultivated land detection, which has received much attention from researchers. Currently, for most multi-temporal polarimetric SAR data classification methods, the simultaneous temporal-polarimetric-spatial feature extraction capability has not been exploited sufficiently. Also, the diversity of different time and different polarimetric features has not been taken into account sufficiently. In this paper, we propose a classification model that combines a dual-stream network as a temporal-polarimetric-spatial feature extraction module with Vision Transformer(ViT) called Temporal-Polarimetric-Spatial Transformer(TSPT) to address the above problems. Secondly, a 3 dimension(3D) convolutional attention module that enables the network to weight the temporal dimension, polarimetric feature dimension and spatial dimension is developed, according to their importance. Experimental results on both UAVSAR and RADARSAT-2 datasets show that the proposed method outperforms ResNet.

In existing superpixel-wise saliency detection algorithms, superpixel generation often is an isolated preprocessing step. The performance of saliency maps is determined by the accuracy of superpixels to a certain extent. However, it is still a challenge to develop a stable superpixel generation method. In this article, we attempt to incorporate the superpixel generation and saliency calculation steps into an end-to-end trainable deep network. First, we employ a recently proposed differentiable superpixel generation method to over-segment the synthetic aperture radar (SAR) images, which outputs the possibility that the pixels assigned to neighbor superpixels (soft superpixel). In saliency calculation part, as one of our main contributions, we propose a differentiable and computationally simple saliency model, i.e., lacunarity cue. It is inspired by the fact that generally the backscattering intensity of regions of interest (ROIs) in SAR images irregularly fluctuates, while the areas with consistent pixels are often ignored as the clusters. We improve the pixelwise box differential dimension algorithm to measure the irregularity of scattering points in a superpixel. The superpixel generation and saliency calculation can be implemented under a unified deep network. Hence, the shapes of the superpixels can be iteratively adjusted according to the saliency maps until the ROIs are correctly detected. Experiments on real SAR images with different sizes and scenes show that the saliency maps can effectively highlight the target areas, thus outperforming the state-of-the-art saliency detection models.

Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This paper provides a sidelobe-aware small ship detection network for synthetic aperture radar imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection Dataset (LS-SSDD), SAR Ship Detection Dataset (SSDD), and AIR-SARShip dataset validated the efficacy and state-of-the-art performance.

During Synthetic Aperture Radar (SAR) imaging, the presence of ghost is frequently observed on SAR images of maritime scenes due to the finite pulse repetition frequency and non-ideal antenna pattern. In Polarimetric Synthetic Aperture Radar (PolSAR) images of ships, the ship movement makes the dispersion of span which is called ghost interference. This problem leads to high false alarm rates and measurement errors. To solve this problem, we propose a method applied to full-polarimetric SAR data. Firstly, we use multi-feature combination to enhance the scattering mechanism of the targets. Secondly, based on this method, using the Rank-1 and generalized similarity parameter (GSP) to improve the contrast between the sea and the ships. Finally, interference feature filter (IFF) is used to get the image with interference removed. We use the GaoFen-3 (GF-3) full-polarimetric SAR data for experiments. The results show that this method effectively removes the ghost interference, and then we will perform a target detection to prove that it can reduce the false alarm rate.

This paper proposes a generalized feature extraction framework for one-dimensional(1D) remote sensing data. This approach streamlines the processing for extracting features by eliminating the need for some preprocessing, such as data normalization, data filtering, and spectrogram generation, which explicitly encode domain-specific knowledge of the tasks. The main component of the new framework, called Shifted-Grad Transformer(SGT), includes the Shift module, Grad module, Smooth module, Raw embedding module, Transformer encoder module, and additional essential module. Extensive experiments on data sets such as Hyperspectral image data, Magnetic signal data, and other 1D data have demonstrated that the SGT performs significantly better than existing methods and provides a new solution to the 1D data processing problem. Our Training code and data are available at https://github.com/wfnian/SGT.

As flood occurs unpredictably, there is not enough time to label the data in practice. The use of clustering inside flood detection deep networks can reduce their demand for labeled data. However, existing clustering algorithms aim at assigning a unique cluster for each pixel. This leads to the fact that clustering process is non-differentiable to the inputs, hindering their incorporation into deep networks. In this study, we introduce a new assignment strategy for single-polarization SAR images to make the clustering differentiable, named “soft association.” Here, each pixel is assigned to various clusters with different probabilities. The greater the probability value, the more likely the pixel will be finally assigned to the cluster. Based on this, an end-to-end trainable semi-supervised clustering network for SAR flood detection is established. Compared with the existing state-of-the-art semi-supervised methods, it can achieve similar performance with fewer labeled samples.

Polarimetric features extracted from the polarimetric synthetic aperture radar data contain a wealth of target scattering information, but usually lead to the problems, such as network learning burden and high computational consumption. A multichannel fusion convolutional neural network based on scattering mechanisms was presented in this letter. First, the polarimetric features were divided into three categories according to their corresponding scattering mechanisms, and put into three network channels, respectively. Second, a new feature output was constructed based on the fusion of three-channel output features. Third, the four output features were cascaded through two fully connected layers and the Softmax classifier to get the classification result. Moreover, a new loss function was defined, combining cross entropy and average cross entropy to prevent network overfitting. Experimental results on airborne synthetic aperture radar (AIRSAR) and GF-3 data set verified the effectiveness of the proposed method in the aspect of classification accuracy and small sample.

The land cover classification has played an important role in remote sensing applications. However, most classification methods were designed based on the pixel features or local spatial features of the remote sensing image, which limits the classification accuracy and generalization. In order to further utilize the spatial information, this letter proposes a dual-branch neural network (NN) inspired by the conditional random field (CRF) model, namely CRF-Net, which takes into account the global spatial features of the image, i.e., geographic latitude-longitude information. First, a dual-branch NN is designed to extract the pixel features and coordinate features. Then, the two kinds of features are fused to realize the remote sensing imagery classification. In the experiments, randomly selected samples and spatial-disjoint samples are employed to verify the effectiveness of the proposed method for hyperspectral image (HSI) and polarimetric synthetic aperture radar (PolSAR) image classification. The experimental results show that the proposed method is superior to the traditional supervised classification methods under the spatial-disjoint sampling strategy, and can achieve the same level of accuracy under the random sampling condition.

Time-series polarimetric synthetic aperture radar (PolSAR) has been proven to be an effective technique for crop classification and agricultural activity monitoring. However, the characterization and utilization of time-series PolSAR data by existing methods are still inadequate. They are unable to extract and utilize time-varying features, which can describe the dynamic changes of crop polarimetric information. In this paper, we propose a tensor form to comprehensively describe the information of time-series PolSAR data, including spatial context information, polarimetric scattering information, and temporal context information. And we define a novel similarity value for the tensors (TSV), which can simultaneously consider distance and shape similarity of tensors. Then, we construct a tensor-based graph representation to capture the global similarity information of time-series PolSAR data. Finally, we propose a tensor-based graph convolutional network (Tensor-GCN) to extract deep features of graph node tensors for crop classification. Experimental results and analysis on two time-series PolSAR data firmly demonstrate the superiority of the proposed Tensor-GCN to other state-of-the-art methods.

Convolutional neural networks (CNNs) have demonstrated impressive ability to achieve promising results in PolSAR image classification. However, the traditional CNN performs convolution on local square regions with fixed sizes. The selection of these local square regions (patches) cannot fully take advantage of the boundary information of land covers and cannot search optimal neighborhoods in the whole image. To overcome these shortcomings, we propose a superpixel-based graph convolutional network (SP-GCN) for PolSAR image classification. SP-GCN utilizes superpixels as graph nodes, which makes full use of boundary information of superpixels and significantly reduces the computational cost of GCN, making it possible to apply GCN to large-scale PolSAR image classification. To reduce the impact of superpixel scale on classification results, we further propose a multiscale superpixel-based graph convolutional network (MSSP-GCN) based on the SP-GCN. Experimental results on three PolSAR datasets firmly demonstrate the superiority of the proposed SP-GCN and MSSP-GCN to other state-of-the-art methods.

In existing superpixel-wise segmentation algorithms, superpixel generation most often is an isolated preprocessing step. The segmentation performance is determined to a certain extent by the accuracy of superpixels. However, it is still a challenge to develop a stable superpixel generation method. In this article, we attempt to incorporate the superpixel generation and merging steps into an end-to-end trainable deep network. First, we employ a recently proposed differentiable superpixel generation method to over-segment the single-polarization synthetic aperture radar (SAR) image. It outputs the statistical likelihood that each pixel belongs to different superpixels. In superpixel merging part, as one of our main contributions, we propose a superpixel-wise statistical dissimilarity measure method for converting the soft superpixels set into a self-connected weighted graph. More importantly, inspired by the concept of the number of walks in graph theory, we define the$k$-order connectivity of each vertex. This definition can intelligently indicate the potential soft cluster centers and class assignments in graph. This merging method is differentiable, computationally simple, and free of empirical parameters. The superpixel generation and merging phases can be implemented under a unified deep network. The benefit is that our method can iteratively adjust the shapes of the superpixels according to the boundaries and segmentation results during training, until the satisfactory segmentation results are captured. Experimental results on real SAR images demonstrate that the segmentation precision of our proposed method is superior to other state-of-the-art methods in terms of precision and computational efficiency.

Since the number of superpixels is lower than that of pixels, superpixels can substantially speed up subsequent processing steps and have been widely used in synthetic aperture radar (SAR) image segmentation. However, in most of the existing superpixel-wise segmentation algorithms, superpixel prediction is an isolated preprocessing step and is independent of the segmentation task. The performance of the segmentation results is determined by the accuracy of superpixels. Once superpixels are generated, their shape cannot be changed in the following segmentation stage, even if the same superpixels contain pixels of different landcovers. To address this, we propose an end-to-end trainable superpixel-wise segmentation method for single-polarization SAR images. First, we design a differentiable boundary-ware clustering method for estimating task-specific superpixels. Instead of the hard association between pixels and superpixels in the existing superpixel algorithms, this method introduces the soft association map to make the clustering differentiable. Hence, it can be implemented using a simple deep fully convolutional network. In the segmentation part, we propose a novel soft graph convolution network (Soft-GCN), which takes the association map as input and performs superpixel-wise segmentation. The advantage of our method is that superpixel generation and graph convolution parts can be trained under a unified framework, until two parts obtain the optimum parameters. In the training process, it can adaptively adjust the shape of the superpixels according to the segmentation results, ensuring the superpixels correctly adhere the boundaries. Experimental results with simulated and real SAR images demonstrate that our method outperforms other state-of-the-art segmentation algorithms, while also being faster.

In multitemporal SAR and Polarimetric SAR (Pol-SAR) coherence is a capital parameter to exploit common information between temporal acquisitions. Yet, its use is limited to high coherences. This article proposes the analysis of low coherence scenarios by introducing a reinterpretation of coherence. It is demonstrated that coherence results from the product of two terms accounting for coherent and radiometric changes, respectively. For low coherences, the first term presents low values, preventing its exploitation for information retrieval. The information provided by the second term can be used in these circumstances to exploit common information. This second term is proposed, as an alternative to coherence, for information retrieval for low coherences. Besides, it is shown that polarimetry allows the temporal optimization of its values. To prove the benefits of this approach, multitemporal SAR and PolSAR data classification is considered as a tool, showing that improvements of the classification overall accuracy may range between 20% and 50%, compared to classification based on coherence.

Currently, deep neural networks have been widely used in the field of SAR target recognition. Many researchers found that deep neural networks have an ability of denoising. In many cases, there is no need to denoise in pre-process. But the denoising ability of deep neural networks can take place of conventional denoising algorithm or not is doubtful. In this article, we explore the effect of image denoising algorithms to SAR target recognition methods based on deep neural networks. Firstly, seven traditional denoising algorithms are selected to process two SAR datasets. And these data are utilized to train two kinds of deep neural networks. After comparing and analyzing the training processes and results, we find that 1) The effect of denoising algorithms is influenced by architectures of neural networks and quality of datasets. It is difficult to find a SAR image denoising algorithm, which can improve the accuracy of any recognition network. Sometimes they even drag down the performance of recognition networks. 2) The deep networks with more layers will have better denoising ability, so the effect of denoising algorithms will decrease. For ResNet, there is no need to add the denoising processing.

Small vessels in synthetic aperture radar (SAR) images usually have weak scattering intensity and occupy only a few numbers of image pixels, resulting in a high miss detection rate during the detection process. Regarding the problem, two solutions were presented in this paper. Firstly, dual-polarimetric SAR data were used and dual-polarimetric features were adaptively fused. Comparing to single-polarization and conventional non-adaptive fusion method, it optimally enhanced the characteristics of small vessels. Secondly, the conventional feature pyramid network (FPN) was enhanced by reducing the downsampling factor, adding spatial attention, and channel attention. The added spatial attention enhanced the significant features of small vessels on the large-scale feature map; the added channel attention filtered out the spliced features maps that were benefiting small vessel detection and reduced feature redundancy. Experimental results on the small vessel data set of Sentinel-1 verified that it not only reduced the miss detection rate but also improved calculation efficiency.

Abstract Deep convolutional neural networks (CNNs) have greatly improved the Face Recognition (FR) performance in recent years. Almost all CNNs in FR are trained on the carefully labeled datasets containing plenty of identities. However, such high‐quality datasets are very expensive to collect, which restricts many researchers to achieve state‐of‐the‐art performance. In this paper, a framework, called SeqFace, for learning discriminative face features is proposed. Besides a traditional identity training dataset, the designed SeqFace can train CNNs by using an additional dataset which includes a large number of face sequences collected from videos. Moreover, the label smoothing regularization (LSR) and a new proposed discriminative sequence agent (DSA) loss are employed to enhance the discrimination power of deep face features via making full use of the sequence data. Only with a single ResNet model, the method achieves very competitive performance on several face recognition benchmarks, including LFW, YTF, CFP, AgeDB, and MegaFace. The code and model are publicly available at the website https://github.com/huangyangyu/SeqFace .

Polarimetric synthetic aperture radar (PolSAR) image classification is an important part of SAR data interpretation and provides more intuitive and detailed SAR polarization information. To bridge the PolSAR data and applications, it is necessary to design a comprehensive PolSAR classification framework to achieve satisfactory results. The deep neural network (DNN) appears to be a solution for the classification issue, in which it outperforms the classical supervised classifiers under the condition of sufficient training data. However, the volume of training data will greatly limit the effectiveness of practical applications. In this article, we try to solve the dependence issue on training data in three different ways: recurrent learning, data augmentation, and postprocessing. First, the long short-term memory (LSTM) network is introduced to achieve pixel sequence learning by taking into account the spatial and polarimetric features. Second, the random neighbor pixel-block (RNPB) method is proposed to increase the number of training samples for sequence learning. Third, the conditional random field (CRF) model is employed to further improve the classification accuracy. In the experiments, three sets of PolSAR data are used to evaluate the small sample performance of the proposed classification method. With only 0.5% labeled pixels for training, the proposed RNPB-LSTM-CRF method can approach 99% overall classification accuracy for all the data sets. Compared with the existing methods, the proposed method can achieve state-of-the-art results for PolSAR image classification under the condition of 1% training samples.

In this article, we propose a fast superpixel region merging algorithm for synthetic aperture radar (SAR) image segmentation. With our previously proposed adaptive superpixel generation approach (ALFCE), an initial over-segmentation superpixel map for SAR imagery can be obtained. A sketch edge map is used here to eliminate the mixed superpixels to refine the over-segmentation. Then, we focus on rapid superpixel merging for efficient and accurate SAR image segmentation by using the statistical region merging (SRM) framework. This article proposes a new merging order with the consideration of statistical dissimilarity measure and common boundary length penalty, as well as the homogeneity constraint for each superpixel pair. For the merging predicate, we define an adaptive merging threshold according to the image complexity, making the proposed superpixel merging no need to set any merging parameters in advance. Disjoint set is utilized in this article to map the superpixel pairs to pixel pairs for the sake of fast region merging, which has a low computation cost even with the increasing of superpixels. Experimental results on synthetic and real SAR images demonstrate that the segmentation precision of our proposed method can reach more than 85% and also superior to other state-of-the-art methods in terms of computational efficiency.

Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily overfit on these noisy labels due to its high capability. In this paper, we present a very simple but effective training paradigm called P-DIFF, which can train DNN classifiers but obviously alleviate the adverse impact of noisy labels. Our proposed probability difference distribution implicitly reflects the probability of a training sample to be clean, then this probability is employed to re-weight the corresponding sample during the training process. P-DIFF can also achieve good performance even without prior-knowledge on the noise rate of training samples. Experiments on benchmark datasets also demonstrate that P-DIFF is superior to the state-of-the-art sample selection methods.

Many deep learning algorithms have been successful applied for synthetic aperture radar automatic target recognition (SAR-ATR), but high recognition accuracy usually relies on large amount of labeled training data. In addition, SAR is active imaging sensor and target characteristics are quite different with varying look angles, which also reduces recognition accuracy. Multi-task learning can improve the performance of main task by learning and sharing useful information from auxiliary tasks. Based on multi-task learning, this paper fully exploits the potential of available SAR data for target classification. Two auxiliary tasks, separating target from shadow and estimating target aspect angle, are designed to obtain auxiliary information and improve the classification accuracy. The MSTAR data set proves the effectiveness of the method, and the results show that the method has good recognition accuracy.

Simultaneous multitask processing is a common requirement in synthetic aperture radar (SAR) automatic target recognition (ATR), e.g., not only the category of the target but also the aspect angle of the target need to be identified at the same time. Moreover, the target recognition network is always expected to have the capability of incremental learning, i.e., acquire the processing capabilities for new tasks while maintaining the processing capabilities for old tasks. In this paper, an incremental multitask learning method based on structured pruning is proposed. The structured pruning, originally proposed for network compression, is used to learn with dominant neuron and release parameter space of convolutional neural network for new tasks. Through iterative pruning and training of new tasks, multitask target recognition is realized in a single convolutional neural network and could simultaneously output recognition results of multiple tasks. The experiments on the MSTAR dataset show that our method can simultaneously recognize the category and aspect angle of target, while does not decrease the corresponding accuracy compared to single-task processing.

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is an essential part of SAR data applications. As one of the image classification methods that can efficiently capture structural information and semantic context, the convolutional neural network (CNN) seems to be a solution for the classification issue in that it outperforms the classical supervised classifiers under the condition of sufficient training data, and it has been used in PolSAR classification widely. Simultaneously, the distance metric learning (DML) is proposed to improve the classification algorithms in performance and even in feature extraction. In this paper, DML with adaptive density discrimination regarded as a loss function, namely Magnet Loss, is applied to the classification of PolSAR images, and k-means++ is realized the clustering process for each category of training samples. Then, different classifiers are executed to replace the softmax function to achieve more accurate classification. Finally, a series of experiments are implemented to prove the effectiveness of the proposed method. Simultaneously, the samples of the coarse label are given and used to analyze the fine-grained classification algorithm by clustering.

Automatic target recognition (ATR) has made great progress with the development of deep learning. However, the target feature in synthetic aperture radar (SAR) image is not consistent with human vision, and the SAR training samples are always limited. These hard issues pose new challenges to the SAR ATR based on convolutional neural network (CNN). In this letter, we propose an improved CNN model to solve the limited sample issue via the feature augmentation and ensemble learning strategies. Normally, the high-level features that are more comprehensive and discriminative than the middle-level and low-level features are always employed for category discrimination. In order to make up the insufficient training features in the limited sample case, the cascaded features from optimally selected convolutional layers are concatenated to provide more comprehensive representation for the recognition. To take full advantage of these cascaded features, the ensemble learning-based classifier, namely, the AdaBoost rotation forest (RoF), is introduced to replace the original softmax layer to realize a more accurate limited sample recognition. Through the AdaBoost RoF method, not only are these features further enhanced by the rotation matrix but also a strong classifier is constructed by several weak classifiers with different adjusted weights. The experimental results on MSTAR data set show that the cascaded features and ensemble weak classifiers can fully exploit effective information in limited samples. Compared with the existing CNN method, the proposed method can improve the recognition accuracy by about 20% under the condition of ten training samples per class.

Benefit from large-scale training datasets, deep Convolutional Neural Networks(CNNs) have achieved impressive results in face recognition(FR). However, tremendous scale of datasets inevitably lead to noisy data, which obviously reduce the performance of the trained CNN models. Kicking out wrong labels from large-scale FR datasets is still very expensive, although some cleaning approaches are proposed. According to the analysis of the whole process of training CNN models supervised by angular margin based loss(AM-Loss) functions, we find that the distribution of training samples implicitly reflects their probability of being clean. Thus, we propose a novel training paradigm that employs the idea of weighting samples based on the above probability. Without any prior knowledge of noise, we can train high performance CNN models with largescale FR datasets. Experiments demonstrate the effectiveness of our training paradigm. The codes are available at https://github.com/huangyangyu/NoiseFace.

Generative adversarial network (GAN) is an artificial neural network based on unsupervised learning method. Due to its powerful model representation capabilities, GAN has been introduced to synthesize synthetic aperture radar (SAR) image data, for the real sample is difficult to acquire. Large-scale, high-resolution SAR images play an important role in promoting SAR applications, such as automatic target recognition and image interpretation. However, on account of the difficult training problem of GAN network, especially for SAR images with speckle noise, it is difficult to obtain high-resolution SAR images by simply transfer the net from optical image. Recent studies in other image fields have shown that hierarchical structure is an effective and useful way to decompose a generation task into several smaller subtasks. How to obtain more high-resolution SAR images from limited original samples through GAN is the target of our research. Therefore, in this paper, we introduce a hierarchical GAN network model to generate SAR images, through the multi-stage network, gradually improve the quality of the generated image, and finally obtain high-resolution images. The type and aspect of generated images are determined by the input of condition vectors in the last two stages. In addition, we introduce the triple loss, in which the background loss is used to imitating background clutter noise of SAR image, the condition loss is to make the generated images' type and aspect become controllable, and the global loss for getting higher image generation quality. The generated images show high similarity with the real samples.

In this paper, we improve the Siamese Networks for SAR target few-shot learning. SAR target recognition is an important branch of SAR application. It can efficiently extract target category information from complex SAR images and help humans quickly understand SAR images. However, many successful machine learning methods require large amounts of annotated data. So, few-shot learning is always a topical challenge for machine learning. We apply Siamese Networks to SAR target recognition with limited data and improved it. Our model consists of CNN encoder, similarity discriminator and classifier. Relevantly, it has two inputs and three outputs. CNN encoder is constrained by similarity discriminator and classifier. Furthermore, the larger difference from the Siamese Network is that the target category is outputted by the classifier, not by the similarity discriminator. Our method not only makes use of the advantage of metric learning to improve the accuracy of SAR target recognition with limited data, but also significantly reduces the prediction time consumption for the model based on metric learning. In the ten categories military vehicle classification task, there are only five samples for each category and a total of 2425 testing samples. Our method outperforms A-ConvNet and Siamese Networks by 15.8% and 8.41%. The prediction time consumption of Siamese Networks is 114.832s, while that of our method is 1.172s.

Polarimetric synthetic aperture radar (PolSAR) imagery classification is an important part of SAR data interpretation. The number of available labeled samples limits the applications of supervised classifiers. In order to solve this issue, the representation based classification algorithms have been widely used. Usually, PolSAR image features are extracted by various methods, and their divergence is very significant. In the data representation based methods, the feature divergence is ignored in the distance metric, thus the different features have the same metric contributions. In this letter, we propose a robust weighting nearest regularized subspace (NRS) method, which introduces the robust statistics to construct the weights of distance metric according to the feature divergence. This method can increase the representation ability of the training samples by the weighted calculation of the biasing Tikhonov matrix. The experimental results show that the weighted distance metric can boost the original NRS classifier by 1.5%, and prove that the feature divergence should be taken into account in the data representation process.

Deep convolutional neural network (CNN) is an important branch of deep learning. Due to its strong ability of feature extraction, CNN models have been introduced to solve the problems of synthetic aperture radar automatic target recognition (SAR-ATR). However, labeled SAR images are difficult to acquire. Therefore, how to obtain a good recognition result from a small sample dataset is what we mainly focus on. In theory, a deeper network can bring a better training result. But it also brings more difficulties to the training process, especially with limited labeled training data. The residual learning which proposed in recent years can alleviate this problem effectively. In this paper, we use a deep residual network, and introduce the dropout layer into the building block to alleviate overfitting caused by limited SAR data. In order to improve the training effect, the new loss function center loss is adopted and combined with softmax loss as the supervision signal to train the deep CNN. The experimental results show that our method can achieve the classification accuracy of 99.67% with all training data, without data augmentation or pre-training. When data of the training dataset was reduced to 20%, we can still achieve a recognition result higher than 94%.

High-precision DEM can be generated by SAR interferometry processing with GaoFen-3 repeat-pass data. This paper presents the first results from GaoFen-3 SAR Interferometry obtained with two interferometric SAR data pairs at the selected experiment sites. The temporal separations were 29 days. At the site the coherence requirements were met, resulting in high quality interferogram. The experimental results show that GaoFen-3 repeat-pass data have some good characteristics, such as high imaging quality, stable spatial baseline, high temporal correlation and spatial correlation, which has a good repeat-pass SAR interferometry application prospect.

The polarimetric features of PolSAR images includes the inherent scattering mechanisms of terrain types, which is important for classification and other earth observation applications. By the use of target decomposition methods, many polarimetric scattering components can be obtained. Besides, the elements of Coherency/Covariance Matrix, as well as polarimetric descriptors such as SPAN, SERD/DERD etc., can also provide characteristic information. However, the computation cost will be very high if all of the polarimetric features are employed as the input of the classification process. In this paper, the effective polarimetric feature combination are studied based on the classification performance of SVM (Support Vector Machine) and NRS (Nearest-Regularized Subspace) machine learning approaches. A fast strategy on basis of correlation coefficient is used to select the features for classification experiments. For the airborne PolSAR data in Flevoland, 10 features have been selected from the total 107 polarimetric features with good classification accuracy up to 93.6%. The experiments on other data sets will be shown.

Synthetic Aperture Radar(SAR) has been widely used in airborne remote sensing and satellite ocean observation fields to reduce the affect of weather condition and sun illumination. As technology developed, swath and resolution requirements are increased in terrain, which arouse a huge increase in the number of simulated points and simulated pulses and lead to a huge increase in simulated time. With the development of graphics processing unit(GPU), it can parallel simulated points to reduce simulated time. As for increased simulated pulses, they can be paralleled on distributed computers. In the article, we focus on parallel on increased simulated pulses and put forward two frameworks based on message passing libraries (MPI) and cloud computing (Hadoop).

GaoFen-3 is China's first meter-level multi-polarization Synthetic Aperture Radar (SAR) satellite with scientific and commercial applications, which was developed by the China Academy of Space Technology (CAST) and had been launched in August, 2016. The SAR instrument and ground data processing system were developed by the Institute of Electronics, Chinese Academy of Sciences (IECAS). It employs a multi-polarization C-band SAR based on active phased array technology, which allows flexible beam operations in azimuth scanning, range scanning, right looking and left looking. Hence, GaoFen-3 has 12 imaging modes, covering the traditional Stripmap mode, ScanSAR mode, and the emerging Wave mode and Sliding Spotlight mode, and is a SAR satellite with the most abundant imaging modes in the world. In order to evaluate the imaging performance of these modes, the multiple mode SAR raw data simulation is highly demanded. In the paper, the simulation framework, the simulation algorithms and the evaluation strategies will be briefly introduced to expose how the raw data simulation guarantees the development of GaoFen-3 and its processing system.

The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method.

This paper presents a new simulator for spaceborne SAR on-board imaging process on mobile GPUs. The system can generate raw data and perform imaging process in real time. Due to introducing low power GPUs, it has low power consumption, light weight and high computing capability. This simulator has the unique structure, which can guarantee its real-time processing. The experimented results indicate that it is possible to apply the simulator to simulating spaceborne SAR imaging process, and this portable simulator can be used in other areas, which could broaden new horizons in low power space.

Synthetic Aperture Radar (SAR) has been widely used in airborne remote sensing and satellite ocean observation fields to reduce the affect of weather condition and sun illumination. As technology developed, swath and resolution requirements are increased in terrain, which result in a huge increase in echo data and simulated time[1]. With the development of graphics processing unit (GPU), it can reduce simulated time effectively. In order to simulate the coherent integration, atomic operation is always used in GPU, which has a bad influence to simulated time. To optimize simulated time, in this article, we put forward three GPU optimistic strategies for atomic-free SAR raw data simulation.

Although second‐order cone programming (SOCP) has been applied to optimise camera parameters in computer vision, it is occasionally been used to refine fisheye camera external parameters as well. This study presents a fisheye camera external parameter estimation based on SOCP in convex optimisation. The homography constraint between two spherical images are first exploited to derive an equation with respect to a given error threshold. Then, the fisheye camera external estimation is transformed into an SOCP optimisation problem through reformulating the parameter estimation equation. The SOCP method has been implemented in Matlab and the optimisation toolbox has been made publicly available. The fisheye camera external parameter optimisation method has been validated by some experiments with synthetic and real data. Comparison experiments between the proposed method and other methods in the literature are also carried out, and the results show that the SOCP method is better for the corrected images.

Due to independence of weather condition and sun illumination, Synthetic Aperture Radar (SAR) has been widely used in airborne remote sensing and satellite ocean observation fields. With the increase in swath and higher resolution requirements, SAR imaging algorithms require further study. However, as a research support, echo data has massive increase. So the high efficient echo simulation is required emergently. SAR echo simulation is optimized to accelerate, based on AVX/SSE of vector instruction set and OpenMP. Experiments demonstrate that in the case of using OpenMP and AVX / SSE combined, CPU simulation efficiency is improved by about 23 times, compared to the traditional one.

With the development of synthetic aperture radar (SAR) technology in recent years, we have to face the huge amount of data. So, the fast image processing technology seems to be really important in the domain of SAR. Based on the situation above, a new method to accelerate SAR image processing is proposed in this paper. The proposed method employs SIMD (Single Instruction Multiple Data) instructions and OpenMP (Open Multiprocessing) technology on multi-core SIMD CPU to realize parallel optimization on image processing algorithms. The CS (Chirp Scaling) algorithm is also chosen to process SAR data in our experiment. The experimental results demonstrate that the proposed SIMD based implementation is able to increase performance more than 20 times when compared to the single-core baseline used.

Novel collaborative representation (CR)-based nearest neighbor (NN) algorithms are proposed for hyperspectral image classification. The proposed methods are based on a CR computed by an ℓ2-norm minimization with a Tikhonov regularization matrix. More specific, a testing sample is represented as a linear combination of all the training samples, and the weights for representation are estimated by an ℓ2-norm minimization-derived closed-form solution. In the first strategy, the label of a testing sample is determined by majority voting of those with k largest representation weights. In the second strategy, local within-class CR is considered as an alternative, and the testing sample is assigned to the class producing the minimum representation residual. The experimental results show that the proposed algorithms achieve better performance than several previous algorithms, such as the original k-NN classifier and the local mean-based NN classifier.

This paper presents an improved algorithm of unsupervised change detection technique by taking the same low-order fractional Fourier transform (FRFT) on multitemporal images acquired on the same geographical area but at different time instances, then generates the difference image by the absolute log-ratio operator. In order to acquire the eigenvector space, we perform principal component analysis (PCA) on m × m nonoverlapping difference image blocks. The feature vectors are extracted using m × m data blocks projection onto eigenvector space. The change detection map is generated by clustering the feature vectors using k-means algorithm into two disjoint classes: changed and unchanged. The final results obtained by the improved algorithm exhibited lower error than its preexistence.

Ionosphere has different stratification at different height, so it's index of refraction varies with height. When radar's echo signals transmit through the ionosphere, the real propagation path is not the ideal straight line, in fact, it will inflect to a certain extent. For the spaceborne synthetic aperture radar (SAR) system, the imaging precision has been decreased both in azimuth section and range section due to imprecise slant range history. In this paper, we simulate the real distance between satellite and the target according to Snell's law, and analyze the effect of ionospheric refraction on spaceborne SAR imaging performance at L-band C-band and X-band.

In order to meet the need of continuous development of three dimensional (3D) Geographic Information System (GIS), we apply an algorithm that efficiently builds a compact sparse octree into GIS. The algorithm is based on the fast GPU hardware rasterization, which builds the octree structure from top level to bottom level at the same time. Therefore the graphics based parallel algorithm can accelerate the process of generate an octree structure for terrain geometric modeling. Otherwise, the algorithm also supports a fast dynamic update of the octree structure. The result shows that the algorithm can improve the efficiency of octree structure generation and large terrain data modeling.

This paper describes a scalable parallel method for interferometric synthetic aperture radar (InSAR) raw data simulation on graphic processing unit (GPU) with common unified device architecture (CUDA). The advantages of the new method rely on the three contributions: GPU hardware provides lots of stream processors for threads calculating, CUDA software environment runs thousands of threads working in parallel for assigned task, raw data simulation adopts the fine-grained task parallelism. Compared with OpenMP, MPI and grid computing, the method not only improves the computational efficiency greatly, but also save the resources such as hardware, electric power and room space. The results show that the method not only ensures accuracy, but also be able to obtain the speedup about 30 times.

In this paper we present a raw signal simulator based on GPU parallel computation for synthetic aperture radar. We describe a mathematical model of SAR simulation based on FFT in detail and implement it through GPU parallel computation. GPU has a better performance in complex calculation than CPU. It supports parallel computation and raises the speed of algorithms. At the last part of this paper, a simulation comparison is given. The result shows that the simulator base on GPU parallel computation improves the efficiency of algorithm. It is very useful when the algorithm consumes large amount of CPU time.

To enhance the computational efficiency and authenticity of Synthetic Aperture Radar (SAR) image simulation of man-made scenes, computer graphics (CG) method was introduced into imaging geometry simulation, graphical electromagnetic computing (GRECO) was used for the interested target scattering calculation. With these two methods, the targets' characteristics will be visualized in simulated image, such as shadow, foreshortening, layover and simple scattering property. The simulated SAR images can be used for target recognition, target detection, system verification and other researches.