- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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 - Two-dimensional Spectrum for Diving Stage SAR Processing with High-order Equivalent Range Model
摘要： For airborne synthetic aperture radar (SAR) processing in its diving stage, the diving velocity brings additional range variance to the range history in the synthetic aperture time. The traditional hyperbolic range model is not accurate enough to approximate the actual range equation and the precise two-dimensional spectrum cannot be achieved. In order to address this problem, a highly accurate spectrum deduction based on high-order equivalent range model is proposed in this paper. By introducing the high-order terms, more degrees-of-freedom are obtained for the equivalent range model and the actual range history can be accurately fitted. Based on the achieved range model, the two-dimensional spectrum for diving stage SAR can be accessed. Simulation experiments are carried out to validate the effectiveness of proposed spectrum.
关键词： two-dimensional spectrum,diving stage,range model,Synthetic aperture radar (SAR)
更新于2025-09-23 15:22:29
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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 - Towards Unsupervised Flood Mapping Generation Using Automatic Thresholding and Classification Aproaches
摘要： This paper presents an unsupervised flood monitoring approach developed by DARES TECHNOLOGY using Synthetic Aperture Radar (SAR) space-borne images in order to improve disaster management and coordinate response activities in front of flood crisis events. The methodology developed combines SAR pre-processing, histogram thresholding in blocks with bi-modal behavior and unsupervised segmentation approaches. The use of interferometric parameters, such as the coherence is also employed to refine results. The challenge of this detection approach is working towards an unsupervised approach, i.e., training data is no required and, therefore, information or ground-truth about the class statistics within the area of interest are not necessary. The methodology proposed will be validated over the floods occurred in Mumbai city on August 29, 2017 produced by heavy rain events. 4 Sentinel-1 SAR images will be employed for this purpose.
关键词： Flood Monitoring,Sentinel-1,SAR
更新于2025-09-23 15:22:29
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Ratio-Based Multitemporal SAR Images Denoising: RABASAR
摘要： In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a 'superimage' by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the 'superimage'; and 3) computation of the denoised image by remultiplying the denoised ratio by the 'superimage.' Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the 'superimage' that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.
关键词： ratio image,speckle reduction,Multitemporal synthetic aperture radar (SAR) series,superimage
更新于2025-09-23 15:22:29
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PolSAR Image Semantic Segmentation Based on Deep Transfer Learning--Realizing Smooth Classification With Small Training Sets
摘要： Suffering from speckle noise and complex scattering phenomena, classification results of SAR images are usually noisy and shattered, which makes them difficult to use in practical applications. Deep-learning-based semantic segmentation realizes segmentation and categorization at the same time, and thus can obtain smooth and fine-grained classification maps. However, this kind of methods require large data sets with pixel-wise categorical annotations, which are time consuming and tedious to retrieve. Compared with photographs and optical remote sensing images, manually annotating SAR data is even harder, which results in a delay of using relevant techniques in this field. In this letter, a new data set is proposed to support semantic segmentation for high-resolution PolSAR images. Limited by the aforementioned problems, the data set is only a small one with 50 image patches. Therefore, two transfer learning strategies are proposed, which adopt the fully convolutional network (FCN) and U-net architecture, respectively, and use distinct pretraining data sets to adapt to different situations. The experiments demonstrate the good performance of both methods and a promising applicability of using small training sets. Moreover, although trained with small patches, both networks can perfectly apply on large images. The new data set and methods are hopeful to support various PolSAR applications as baselines.
关键词： polarimetry,SAR,image classification,Deep learning,image segmentation
更新于2025-09-23 15:22:29
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[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - Reconstruction Full-Pol SAR Data from Single-Pol SAR Image Using Deep Neural Network
摘要： Compared with single channel polarimetric (single-pol) SAR image, full polarimetric (full-pol) data convey richer information, but with compromises on higher system complexity and lower resolution or swath. In order to balance these factors, a deep neural networks based method is proposed to recover full-pol data from single-pol data in this paper. It consists of two parts: a feature extractor network is applied first to extract hierarchical multi-scale spatial features, followed by a feature translator network to predict polarimetric features with which full-pol SAR data can be recovered. Both qualitative and quantitative results show that the recovered full-pol SAR data agrees well with the real full-pol data. No prior information is assumed for scatterer media, and the framework can be easily expanded to recovery full-pol data from non-full-pol data. Traditional PolSAR applications such as model-based decomposition and unsupervised classification can now be applied directly to recovered full-pol SAR image to interpret the physical scattering mechanism.
关键词： synthetic aperture radar (SAR),deep neural network (DNN),polarimetric reconstruction
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - An Object-Based Method Based on a Novel Statistical Distance for SAR Image Change Detection
摘要： This paper introduces an object-based method based on a new statistical distance for SAR image change detection. Firstly, multi-temporal segmentation is carried out to segment two temporal SAR images simultaneously. It considers the homogeneity in two temporal images, and could generate homogeneous objects in spectral, spatial and temporal. In addition, through setting different segmentation parameters, the multi-temporal images can be segmented in a set of scales. This process exploits the advantages of OBIA that could effectively reduce spurious changes, and considers the scale of change detection task. Secondly, a multiplicative noise model called Nakagami–Rayleigh distribution is employed to describe SAR data, and then applied to Bayesian formulation. Thus, a new statistical distance that is insensitive to speckles is derived to measure the distances between pairs of parcels. Then, cluster ensemble algorithm is utilized to improve accuracy of individual result in each scale to obtain the final change detection map. Finally, multi-temporal Radarsat-2 images are employed to verify the effectiveness of the proposed method compared with other four methods.
关键词： synthetic aperture radar (SAR),multi-scale analysis,object-based image analysis,change detection
更新于2025-09-23 15:22:29
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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 - Performance Analysis of Time-Frequency Technique for the Detection of Small Ships in SAR Imagery at Large Grazing Angle and Moderate Metocean Conditions
摘要： This paper addresses the performance of time-frequency based techniques for the detection of small ships (length less than 30m) under side-looking synthetic aperture radar (SAR) limiting conditions. The aim of this work is to assess this technique for improving TerraSAR-X (TS-X) near real time (NRT) ship detection service. The goals are achieved by processing both the co-polarized single look complex channels (HH and VV) of TS-X data where ships have been identified by their self-reporting messaging system. The results show that the target-to-clutter ratio does not improve significantly for the detection of small ships under the conditions investigated.
关键词： performance analysis,time-frequency methods,ship detection,SAR
更新于2025-09-23 15:22:29
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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 - A Fast Sparse Representation Method for SAR Target Configuration Recognition
摘要： Focusing on the problem of the real-time implementation in sparse representation (SR) based recognition algorithm, a fast sparse representation (FSR) algorithm is presented in this paper to improve the efficiency of synthetic aperture radar (SAR) target configuration recognition. Taking the inertia variance characteristic of SAR target images over a small range of azimuth angles into consideration, training samples of each configuration are averaged. Instead of using all the training samples to establish the dictionary in SR, the average samples are utilized to construct the dictionary in FSR. A small dictionary accelerates the speed of the proposed algorithm.
关键词： sparse representation (SR),Synthetic aperture radar (SAR) images,target configuration recognition
更新于2025-09-23 15:22:29
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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 - Fully Convolutional Network with Polarimetric Manifold for SAR Imagery Classification
摘要： Image classification performance depends on the understanding of image features and classifier selection. Owing to the special imaging mechanism, achieving precise classification for remote sensing imagery is still quite challenging. In this paper, a fully convolutional network with polarimetric manifold, is proposed for Synthetic Aperture Radar (SAR) image classification. First, the polarimetric features are extracted to describe the target information; then the feature points in high-dimension are mapped to low-dimension through the manifold structure. In this way, the effect of single manifold is equal to that of multi-layer convolution. The experimental results on SAR image data indicate that the presented manifold network can effectively separate the polarimetric features and improve the classification accuracy.
关键词： manifold structure,Synthetic Aperture Radar (SAR),image classification,convolution network
更新于2025-09-23 15:22:29
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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 - Towards the Retrieval of 2-D Vessel Velocities with Single-Platform Spaceborne SAR: Experimental Results with the TerraSAR-X 2-Looks Tops Mode
摘要： In this contribution, we propose the use of 2-looks Synthetic Aperture Radar data to retrieve azimuth velocities of moving targets with a single platform. The established technique to retrieve velocities of moving targets is Along-track Interferometry (ATI), which provides a measurement of the velocity in the radar line of sight direction by employing two or more phase centers separated in the along-track direction. The use of 2-looks data allows to observe targets at two different instants of time, with a Doppler separation, enabling the retrieval of the target velocity in the azimuth direction. We introduce the 2-looks Terrain Observation by Progressive Scans (TOPS) mode, presenting the performance that can be achieved with the TerraSAR-X system. Moreover, we present experimental results with real data acquired with TerraSAR-X over coastal areas to retrieve velocities of vessels. A validation of the results with Automatic Identification System (AIS) data (ground truth) provides accuracies below 1 m/s.
关键词： Synthetic Aperture Radar (SAR),Vessel tracking,2-looks TOPS,TerraSAR-X
更新于2025-09-23 15:22:29