Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods ship have been developed; however, the of small ships presents unique challenges. SAR image characteristics, such as sidelobe effect blurred outline induced by special imaging mechanism, well size, are primary factors that lower accuracy. This paper provides a sidelobe-aware network imagery. First, considering outline, dual-pooling, i.e.,...

In existing superpixel-wise segmentation algorithms, superpixel generation most often is an isolated preprocessing step. The performance determined to a certain extent by the accuracy of superpixels. However, it still challenge develop stable method. this article, we attempt incorporate and merging steps into end-to-end trainable deep network. First, employ recently proposed differentiable method over-segment single-polarization synthetic aperture radar (SAR) image. It outputs statistical...

Audio-visual emotion recognition is the research of identifying human emotional states by combining audio modality and visual simultaneously, which plays an important role in intelligent human-machine interactions. With help deep learning, previous works have made great progress for audio-visual recognition. However, these learning methods often require a large amount data training. In reality, acquisition difficult expensive, especially multimodal with different modalities. As result,...

In existing superpixel-wise saliency detection algorithms, superpixel generation often is an isolated preprocessing step. The performance of maps determined by the accuracy superpixels to a certain extent. However, it still challenge develop stable method. this article, we attempt incorporate and calculation steps into end-to-end trainable deep network. First, employ recently proposed differentiable method over-segment synthetic aperture radar (SAR) images, which outputs possibility that...

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 detection. These mainly focus two issues: one false alarm in complex inshore environments, other effective extraction utilization features. The topic discussed this paper culprits that has caused aforementioned problems, but long overlooked. Specifically, it pertains issue...

Data collection from deployed sensor networks can be with static sink, ground-based mobile or Unmanned Aerial Vehicle (UAV) based aerial data collector. Considering the large-scale and peculiarity of environments, on controllable UAV has more advantages. In this paper, we have designed a basic framework for collection, which includes following five components: deployment networks, nodes positioning, anchor points searching, fast path planning UAV, network. We identified key challenges in...

The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts data. In order segment these big remotely sensed data in an acceptable time frame, more and segmentation algorithms based on deep learning attempt take superpixels as processing units. However, the over-segmented become non-Euclidean structure that traditional Convolutional Neural Networks (CNN) cannot directly process. Here, we propose a novel Attention Graph Convolution Network (AGCN)...

Since the number of superpixels is lower than that pixels, can substantially speed up subsequent processing steps and have been widely used in synthetic aperture radar (SAR) image segmentation. However, most existing superpixel-wise segmentation algorithms, superpixel prediction an isolated preprocessing step independent task. The performance results determined by accuracy superpixels. Once are generated, their shape cannot be changed following stage, even if same contain pixels different...

Accurate detection of rivers plays a significant role in water conservancy construction and ecological protection, where airborne synthetic aperture radar (SAR) data have already become one the main sources. However, extracting river information from efficiently accurately still remains an open problem. The existing methods for detecting are typically based on rivers' edges, which easily mixed with those artificial buildings or farmland. In addition, pixel-based image processing approaches...

Synthetic aperture radar automatic target recognition (SAR ATR) uses computer processing capabilities to infer the classes of targets without human intervention. For SAR ATR, deep learning gradually emerges as a powerful tool and achieves promising performance. However, it faces serious challenges how deal with incremental scenarios. The existing learning-based ATR methods usually predefine total number classes. In realistic applications, new tasks/classes will be added continuously. If all...

Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based and is the basis of interpretation. However, most existing methods usually neglect appearance spatial consistency during feature extraction also require large number training data. In addition, processing cannot meet real time requirement. We hereby present weakly supervised algorithm to perform task for high-resolution SAR images. For effective segmentation, input first over-segmented...

Synthetic Aperture Radar (SAR) automatic target recognition (ATR) technology is one of the key technologies to achieve intelligent interpretation for SAR images. With rapid development deep learning, neural networks have been successively used in ATR and show priority comparison with conventional methods. Recently, more attention paid robustness learning based The reason that maliciously modified imperceptible adversarial images can deceive methods which are on networks. In this paper, we...

Synthetic aperture radar ship detection has recently received significant attention from scholars. However, accurately distinguishing between ships is challenging due to the overlap inshore labels. In addition, some labeled boxes contain interference information, such as land areas, which can cause false alarms and confusion in feature learning. To address these challenges, this article creates an edge semantic decoupling (ESD) module, adds segmentation branches, introduces information of...

Synthetic aperture radar (SAR) radiometric crosscalibration achieves the calibration of uncalibrated satellites by employing calibrated to illuminate same ground targets. The stability these targets is critical for effective cross-calibration. Such stable targets, named pseudo-invariant sites (PICS), have been extensively researched optical sensors, but there has comparatively less focus on SAR sensors. Furthermore, studies that synthesize multiple comprehensive dynamic range remain...

Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. However, polarization SAR ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors sea clutter modeling and parameter estimation problems detection, which is difficult to adapt the complex background. In addition, neural network-based mostly rely on single polarimetric-channel...

Myopia has become a global public health issue, and existing research primarily focuses on predicting the future spherical equivalent (SE) of children adolescents based ocular optical data. However, electronic medical record (EMR) data often exhibit characteristics such as multi-dimensionality, irregular time series, missing values, non-uniform intervals, which present significant challenges for prediction. To address these issues, this paper proposes prediction model named IST-xLSTM,...

Precise localization of cephalometric landmarks is crucial in the fields orthodontics and craniofacial surgery. Traditional manual analysis computer-aided have significant drawbacks, including large errors, low accuracy, being time-consuming. To achieve efficient accurate landmarks, this study proposes a detection algorithm, CenterNet-PSA, which integrates Polarized Self-Attention Mechanism. The algorithm first uses pre-trained DLA-34 as feature extraction network to extract features, then...

Adulteration detection or authentication is considered a type of one-class classification (OCC) in chemometrics. An effective OCC model requires representative samples. However, it challenging to collect samples from all over the world. Moreover, also very hard evaluate representativeness collected In this study, we blazed new trail propose an method identify adulterated edible oils without building prediction beforehand. developed by real-time modeling, and population analysis was designed...

Audio-visual emotion recognition aims to distinguish human emotional states by integrating the audio and visual data acquired in expression of emotions. It is crucial for facilitating affect-related human-machine interaction system enabling machines intelligently respond One challenge this problem how efficiently extract feature representations from modalities. Although progresses have been made previous works, most them ignore common information between during learning process, which may...