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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Inshore Ship Detection in Sar Images Based on Deep Neural Networks
摘要： Inshore ship detection in SAR image faces difficulties on correctly identifying near-shore ships and onshore objects. This article proposes a multi-scale full convolutional network (MS-FCN) based sea-land segmentation method and applies a rotatable bounding box based object detection method (DR-Box) to solve the inshore ship detection problem. The sea region and land region are separated by MS-FCN then DR-Box is applied on sea region. The proposed method combines global information and local information of SAR image to achieve high accuracy. The networks are trained with Chinese Gaofen-3 satellite images. Experiments on the testing image show most inshore ships are successfully located by the proposed method.
关键词： object detection networks,full convolutional networks,deep learning,inshore ship detection,Synthetic aperture radar
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Multi-View Bistatic Synthetic Aperture Radar Target Recognition Based on Multi-Input Deep Convolutional Neural Network
摘要： Bistatic synthetic aperture radar (SAR) can provide additional observables and scattering information of the target from multiple views. In this paper, a new bistatic SAR automatic target recognition (ATR) method based on multi-input deep convolutional neural network is proposed. The geometry of the multi-view bistatic SAR ATR is modeled, and an electromagnetic simulation approach is utilized as an alternative to generate enough bistatic SAR images for network training. Then a deep convolutional neural network with multiple inputs is designed, and the features of the multi-view bistatic SAR images will be effectively learned by the proposed network. Therefore, the proposed method can achieve a superior recognition performance. Experimental results have shown the superiority of the proposed method based on the electromagnetic simulation bistatic SAR data.
关键词： multi-view,deep convolutional neural network,automatic target recognition,Bistatic synthetic aperture radar
更新于2025-09-23 15:21:21
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Wide-Angle, Ultra-Wideband ISAR Imaging of Vehicles and Drones
摘要： In-situ, wide-angle, and ultra-wideband inverse synthetic aperture radar (ISAR) imaging of vehicles and drones is demonstrated using a portable ultra-wideband radar. In order to form well-focused ISAR images, motion compensation is performed before applying the k-space imaging algorithm. While the same basic motion compensation methodology is applied to both types of targets, a more complex motion model is needed to better capture the ?ight path of the drone. The resulting ISAR images clearly show the geometrical outline of the targets and highlight locations of prominent backscattering. The ISAR images are also assessed against images generated through instrumented targets or laboratory measurements, and the image quality is shown to be comparable.
关键词： ultra-wideband,radar imaging,vehicles,radar measurements,drones,inverse synthetic aperture radar
更新于2025-09-23 15:21:01
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Lower Power, Better Uniformity, and Stability CBRAM Enabled by Graphene Nanohole Interface Engineering
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： image reconstruction,image representations,Adaptive filters,image edge analysis,image enhancement,synthetic aperture radar (SAR),image analysis,digital filters
更新于2025-09-23 15:21:01
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Bayesian High Resolution Range Profile Reconstruction of High-Speed Moving Target From Under-Sampled Data
摘要： Obtained by wide band radar system, high resolution range profile (HRRP) is the projection of scatterers of target to the radar line-of-sight (LOS). HRRP reconstruction is unavoidable for inverse synthetic aperture radar (ISAR) imaging, and of particular usage for target recognition, especially in cases that the ISAR image of target is not able to be achieved. For the high-speed moving target, however, its HRRP is stretched by the high order phase error. To obtain well-focused HRRP, the phase error induced by target velocity should be compensated, utilizing either measured or estimated target velocity. Noting in case of under-sampled data, the traditional velocity estimation and HRRP reconstruction algorithms become invalid, a novel HRRP reconstruction of high-speed target for under-sampled data is proposed. The Laplacian scale mixture (LSM) is used as the sparse prior of HRRP, and the variational Bayesian inference is utilized to derive its posterior, so as to reconstruct it with high resolution from the under-sampled data. Additionally, during the reconstruction of HRRP, the target velocity is estimated via joint constraint of entropy minimization and sparseness of HRRP to compensate the high order phase error brought by the target velocity to concentrate HRRP. Experimental results based on both simulated and measured data validate the effectiveness of the proposed Bayesian HRRP reconstruction algorithm.
关键词： inverse synthetic aperture radar (ISAR) imaging,entropy minimization,Newton method,variational Bayesian inference,High resolution range profile (HRRP),under-sampled data,velocity estimation
更新于2025-09-23 15:21:01
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[Advances in Intelligent Systems and Computing] Recent Findings in Intelligent Computing Techniques Volume 709 (Proceedings of the 5th ICACNI 2017, Volume 3) || Detection and Analysis of Oil Spill in Ocean for Reduced Complexity in Extraction Using Image Processing
摘要： Oil spills occurring in oceans are difficult to detect and require sophisticated measures to obtain and analyze the images. In this chapter, both color image using high-resolution cameras and Synthetic Aperture Radar (SAR) images are analyzed and certain useful results are obtained to reduce the complexity in extracting the oil spills. The recognition and examination of the oil spill images are done using image processing technique. Furthermore, if the oil spill is scattered as patches, the algorithm classifies the patches into smaller patches and larger ones by using k-means clustering. Hence, the patches depending on the size or intensity can be extracted on a simpler basis.
关键词： Image processing,Synthetic aperture radar (SAR) images,Machine learning,K-means clustering
更新于2025-09-23 15:21:01
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Robust Radial Velocity Estimation Based on Joint-Pixel Normalized Sample Covariance Matrix and Shift Vector for Moving Targets
摘要： The clutter suppression and target radial velocity estimation are essential in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. In reality, the heterogeneous clutter, the image coregistration error, and channel mismatch will remarkably decline the estimation performance of the target radial velocity. To address these issues, a robust radial velocity estimation algorithm is proposed in this letter. Based on the joint-pixel signal model, the joint-pixel normalized sample covariance matrix (JPNSCM) is employed to mitigate the effect of heterogeneous clutter, and the shift vector determined by JPNSCM is used to obtain the actual target steering vector. Then, the adaptive matched ?ltering algorithm is adopted to estimate the target radial velocity. Compared with traditional estimation algorithms, the proposed method obtains better performance in both simulations and real SAR data experiments.
关键词： Ground moving target indication (GMTI),synthetic aperture radar (SAR),parameter estimation
更新于2025-09-23 15:21:01
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A Modified Local Binary Pattern Descriptor for SAR Image Matching
摘要： Image matching is an important step which is taken in most applications of synthetic aperture radar (SAR) images. In this letter, a method is proposed for SAR image matching which introduces a modified local binary pattern (LBP) as a descriptor. Multitextural feature LBP (MTF-LBP) uses the gray-level cooccurrence matrix to increase image texture information. MTF-LBP creates bit plane for each point candidate for matching. Then, using hamming distance, true matches are determined. Experiments are conducted on four spaceborne SAR image pairs including Radarsat-2, TerraSAR-X, ALOS-PALSAR, and Sentinel-1. The proposed method is compared with five common LBP approaches. The results indicate that the proposed method has a better performance in terms of the number of true matches.
关键词： Image texture analysis,LBP (MTF-LBP),synthetic aperture radar (SAR),multitextural feature,matching
更新于2025-09-23 15:21:01
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Saliency detection of targets in polarimetric SAR images based on globally weighted perturbation filters
摘要： In this paper, a saliency detection for Polarimetric Synthetic Aperture Radar (PolSAR) images is proposed based on weighted perturbation filters. Auxiliary data is demanded to identify polarimetric vector of targets, for a canonical perturbation filter. Only if the target signature was available and accurate, it would be satisfiable to apply the filter in practice. Besides, not every target can usually be detected by an individual filter, because of variant polarimetric characteristics of targets with respect to different aspects or shapes. To overcome these drawbacks, several perturbation filters are combined in the proposed method. By initializing with different parameters, these filters decompose PolSAR data into their index maps. Then, aiming to find out filters of interest, i.e., ones related to target pixels, we assume that targets to detect are sparse in PolSAR image. Thus, saliency weights are assigned to the filters, based on Jaccard distances of their index maps. Therein, the spatial sparseness between objects and their surrounding derives high weights for corresponding filters. And then, after globally fusion of refined filtering responses with the weights, saliency map is generated for every local pattern in PolSAR image. Finally, the target regions are extracted from this map, by thresholding and morphological operation. Experiments performed on real and simulated PolSAR data verify the performance of this method, in comparison with several common PolSAR detectors. Also, the proposed method finds out most targets in ground truth, without auxiliary polarimetric information provided.
关键词： Geometrical perturbation filter,Sparse spatial correlation,Polarimetric synthetic aperture radar,Saliency detection
更新于2025-09-23 15:21:01
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Multi-time scale coordinated scheduling for the combined system of wind power, photovoltaic, thermal generator, hydro pumped storage and batteries
摘要： The phenomenon of soil salinization in semi-arid regions is getting amplified and accentuated by both anthropogenic practices and climate change. Land salinization mapping and monitoring using conventional strategies are insufficient and difficult. Our work aims to study the potential of synthetic aperture radar (SAR) for mapping and monitoring of the spatio-temporal dynamics of soil salinity using interferometry. Our contribution in this paper consists of a statistical relationship that we establish between field salinity measurement and InSAR coherence based on an empirical analysis. For experimental validation, two sites were selected: 1) the region of Mahdia (central Tunisia) and 2) the plain of Tadla (central Morocco). Both sites underwent three ground campaigns simultaneously with three Radarsat-2 SAR image acquisitions. The results show that it is possible to estimate the temporal change in soil electrical conductivity (EC) from SAR images through the InSAR technique. It has been shown that the radar signal is more sensitive to soil salinity in HH polarization using a small incidence angle. However, for the HV polarization, a large angle of incidence is more suitable. This is, under considering the minimal influence of roughness and moisture surfaces, for a given InSAR coherence.
关键词： interferometric synthetic aperture radar (InSAR) coherence,polarimetric synthetic aperture radar (SAR),soil salinity,Electrical conductivity (EC)
更新于2025-09-23 15:19:57