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To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, the current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of the templates is low due to the lack of speckle noise. Second, the generated templates have a low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposed a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solved the two aforementioned problems from the following aspects. First, the influence of the speckle noise was considered in the network to avoid the problem of reduced authenticity in the generated images. Second, a channel attention mechanism module was used to improve the network's learning ability of the shadow features, which improved the similarity between the generated template and the shadow area in the input template. Finally, the single generative adversarial network (SinGAN) scheme, which is a generative adversarial network capable of image sample augmentation for a single SAR image, and the proposed scheme were compared regarding the equivalent number of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrated that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme had targets and shadow features similar to those of the original image and could incorporate speckle noise characteristics, resulting in a higher authenticity, which helps to achieve fast and effective SAR deception jamming. [ABSTRACT FROM AUTHOR]

Highly transparent material classification can play an important role in the field of computer vision to classify glass or plastics for recycling and for home service robots to recognize transparent material. In these areas, there is a need to classify materials that are more than 73% transparent, but current transparent material classification methods cannot classify materials with full transparency levels. This paper proposes a highly transparent material classification method based on the refractive index, reflectivity, and transmissivity features from an imaging model of a time-of-flight (ToF) camera as the classification feature. First, we use the ToF camera to collect the depth and light intensity of the transparent material, as well as the scene information. The acquisition depth is distorted owing to the material characteristics of transparent materials. Second, we estimate the refractive index, reflectance, and transmittance from the depth distortion and IR (infrared rays) image. Finally, we choose a classifier that conforms to the nonlinear characteristics of the data to achieve transparent material classification. The method’s classification accuracy reached 94.1% in an experiment, indicating that our method considers the unique phenomenon of highly transparent materials reflecting against the background, incorporates this phenomenon into the ToF distance model, it can extract material features that express the characteristics of highly transparent materials, making it applicable to the classification of transparent materials at all levels of transparency. [ABSTRACT FROM AUTHOR]

The classification of materials is a research hotspot. These methods generally focus on the classification of flat materials and do not consider the influence of polishing and convex surfaces. We develop a classification algorithm of polishing and convex surface objects, and derive the photon accumulation point spread function (PAPSF) of material from the imaging model of a binocular pulsed time-of-flight (ToF) camera as the classification feature, which consists of depth distortion, the indirect reflection photon cumulant and the indirect reflection photon cumulant. We design a one-versus-all support vector machine (SVM) classifier to classify materials of polishing and convex surfaces objects. We conduct classification experiments on four plastics and four metal materials with a similar appearance. Our method in flat and raw material classification has the same classification accuracy as the latest method based on a continuous-wave- modulation ToF camera, but also our method achieved accuracies of 91.0% in flat and polishing material classification, 93.0% in different convex surface and fixed polishing material classification, 91.5% in fixed convex surface and different polishing material classification and 90.2% in polishing and convex surface material classification. [ABSTRACT FROM AUTHOR]

The spatial correlation of ice-sounding data can be used to trace internal isochronic layers and synchronize the age–depth relationship between different ice core sites, which is difficult using existing imaging methods. In this study, we propose a new algorithm to address the opportunity that applying spatial correlation to ice-sheet imaging offers. The algorithm is a spatial-correlation-based ice-sounding imaging method using fast back projection (FBP) that successfully improves the spatial correlation of imaging results with high efficiency. We give the specific steps to implement the algorithm and apply it to simulate both point targets and ice-sounding radar data to demonstrate its validity in imaging ice sheets. Furthermore, compared with two previous methods, the proposed algorithm improves the spatial correlation without degrading the ability of signal-to-noise ratio (SNR) improvement and processing efficiency. [ABSTRACT FROM AUTHOR]

We propose an innovative method of material classification based on the imaging model of pulsed time-of-flight (ToF) camera integrated with the unique signature that describes physical properties of each material named reflection point spread function (RPSF). First, the optimization method reduces the effect of material surface interreflection, which would affect RPSF and lead to decreased accuracy in classification, by alternating direction method of multipliers (ADMM). A method named feature vector normalization is proposed to extract material RPSF features. Second, according to the nonlinearity of the feature vectors, the structure of hidden layer neurons of radial basis function (RBF) neural network is optimized based on singular value decomposition (SVD) to improve generalization. Finally, the similar appearance of plastics and metals are classified on turntable-based measurement system by own design. The average classification accuracy reaches 93.3%, and the highest classification accuracy reaches 94.6%. [ABSTRACT FROM AUTHOR]

This paper presents the ice thickness, fine resolution internal reflecting horizons (IRHs), and distinct bottom topography measurements of Chinese Kunlun Station and Grove Mountains, Antarctica, derived from sounding these glaciers with a high-resolution radar. To enable the development of next-generation ice-sheet models, we need information on IRHs, bottom topography, and basal conditions. To this end, we performed measurements with the progressively improved ice-sounding radar system, currently known as the high-resolution ice-sounding radar developed by the Key Laboratory of Electromagnetic Radiation and Sensing Technology of Institute of Electronics, Chinese Academy of Sciences, Beijing, China. We processed the collected data using focused synthetic aperture radar (SAR) algorithm named the modified range migration algorithm using curvelets and the modified nonlinear chirp scaling algorithm to improve radar sensitivity and reduce along-track surface clutter. Representative results from selected transects indicate that we successfully sounded 3-km-thick ice with a fine resolution of 0.75 m. In this paper, we provide a brief description of the radar system, discuss the focused SAR processing algorithms, and provide sample results to demonstrate the successful sounding of the ice sheet in Antarctica. [ABSTRACT FROM AUTHOR]

We develop and then demonstrate a modified planar subarray processing algorithm based on the inverse scaled Fourier transform applied to very high frequency ice-sounding data that produces swath measurements of ice sheet surface topography, ice thickness, and radar reflectivity of both internal reflecting horizons and bedrock of the ice sheet. It is a real-time ice-sounding imaging method. First, theory analysis has been carried on the proposed algorithm. Then, we give the particular realizing steps to implement this algorithm. Finally, we apply this algorithm to the simulation point targets and real data collected during the 29th Chinese Antarctic Research Expedition to prove its validity of imaging of ice sheets. Furthermore, compared with two previous algorithms in two major aspects—the power of azimuth clutter reduction and calculating time—the proposed algorithm could considerably reduce the imaging time to meet the requirement of real-time imaging of ice-sounding data without degrading the ability of azimuth clutter reduction via C+MPI language on a parallel computer system. [ABSTRACT FROM PUBLISHER]

Bed topography and roughness play important roles in numerous ice-sheet analyses. Although the coverage of ice-penetrating radar measurements has vastly increased over recent decades, significant data gaps remain in certain areas of subglacial topography and need interpolation. However, the bed topography generated by interpolation such as kriging and mass conservation is generally smooth at small scales, lacking topographic features important for sub-kilometer roughness. DeepBedMap, a deep learning method combined with multiple surface observation inputs, can generate high-resolution (250 m) bed topography with realistic bed roughness but produces some unrealistic artifacts and higher bed elevation values in certain regions, which could bias ice-sheet models. To address these issues, we present MB_DeepBedMap, a multi-branch deep learning method to generate more realistic bed topography. The model improves upon DeepBedMap by separating inputs into two groups using a multi-branch network structure according to their characteristics, rather than fusing all inputs at an early stage, to reduce artifacts in the generated topography caused by earlier fusion of inputs. A direct upsampling branch preserves large-scale subglacial landforms while generating high-resolution bed topography. We use MB_DeepBedMap to generate a high-resolution (250 m) bed elevation grid product of Antarctica, MB_DeepBedMap_DEM, which can be used in high-resolution ice-sheet modeling studies. Moreover, we test the performance of MB_DeepBedMap model in Thwaites Glacier, Gamburtsev Subglacial Mountains, and several other regions, by comparing the qualitative topographic features and quantitative errors of MB_DeepBedMap, BEDMAP2, BedMachine Antarctica, and DeepBedMap. The results show that MB_DeepBedMap can provide more realistic small-scale topographic features and roughness compared to BEDMAP2, BedMachine Antarctica, and DeepBedMap. [ABSTRACT FROM AUTHOR]

Video anomaly detection is a challenging task because of the uncertainty of abnormal events. The current method based on predictive frames has obtained better detection results compared with the previous reconstruction or hand-crafted methods. In current prediction methods, the characteristics considered previously are only of a single scale, and the time constraint information is not fully used. In our work, we proposed a new framework structure to achieve better abnormality detection rate. To address the objects of different scales in each video frame, we considered extracting the characteristics of different receptive fields to encode more spatial information. At the same time, we added temporal constraints to the network instead of using time-consuming optical flow information, and we completed the memory of temporal features through a ConvGRU module. Furthermore, while distinguishing abnormal events, we also considered temporal information and spatial information so that our framework could fully combine spatio-temporal information to correctly distinguish abnormal events from normal events. We obtained excellent results on three datasets, thus demonstrating the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters. [ABSTRACT FROM AUTHOR]