Very High Resolution Synthetic Aperture Radar Research Articles

CG-Net: Conditional GIS-Aware Network for Individual Building Segmentation in VHR SAR Images

Object retrieval and reconstruction from very high resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging owing to the complexity of SAR data. This paper addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. To achieve this, we introduce building footprints from GIS data as complementary information and propose a novel conditional GIS-aware network (CG-Net). The proposed model learns multi-level visual features and employs building footprints to normalize the features for predicting building masks in the SAR image. We validate our method using a high resolution spotlight TerraSAR-X image collected over Berlin. Experimental results show that the proposed CG-Net effectively brings improvements with variant backbones. We further compare two representations of building footprints, namely complete building footprints and sensor-visible footprint segments, for our task, and conclude that the use of the former leads to better segmentation results. Moreover, we investigate the impact of inaccurate GIS data on our CG-Net, and this study shows that CG-Net is robust against positioning errors in GIS data. In addition, we propose an approach of ground truth generation of buildings from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image datasets. The segmentation results can be applied to reconstruct 3D building models at level-of-detail (LoD) 1, which is demonstrated in our experiments.

A Benchmark High-Resolution GaoFen-3 SAR Dataset for Building Semantic Segmentation

Deep learning is increasingly popular in remote sensing communities and already successful in land cover classification and semantic segmentation. However, most studies are limited to the utilization of optical datasets. Despite few attempts applied to synthetic aperture radar (SAR) using deep learning, the huge potential, especially for the very high resolution (VHR) SAR, are still underexploited. Taking building segmentation as an example, the VHR SAR datasets are still missing to the best of our knowledge. A comparable baseline for SAR building segmentation does not exist, and which segmentation method is more suitable for SAR image is poorly understood. This article first provides a benchmark high-resolution (1 m) GaoFen-3 SAR datasets, which cover nine cities from seven countries, review the state-of-the-art semantic segmentation methods applied to SAR, and then summarize the potential operations to improve the performance. With these comprehensive assessments, we hope to provide the recommendation and roadmap for future SAR semantic segmentation.

An Approach to Unsupervised Detection of Fully and Partially Destroyed Buildings in Multitemporal VHR SAR Images

In the presence of abrupt change events, multitemporal synthetic aperture radar (SAR) data represent a precious supporting tool for quantifying changes, in particular in urban areas. A large amount of SAR data also exists at very high resolution (VHR). Over urban areas, the introduction of the VHR imagery moves the analysis down to the single building scale. However, VHR imagery is also characterized by a large heterogeneity and a more complex representation of the building. In this work, we propose a geometrical model for describing partially destroyed buildings and derive the corresponding multitemporal backscattering signature by applying the ray-tracing method. The model is integrated into an unsupervised automatic approach for the detection of both fully and partially destroyed buildings. The strategy considers a hierarchical structure of the changes. Experimental results conducted on two multitemporal VHR SAR datasets show a large robustness of the approach and good accuracy in the detection of the classes for damaged buildings with different severity levels.

Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

Building extraction from very high-resolution synthetic aperture radar images based on statistical and structural information fusion

ABSTRACTTo investigate the limits of building detection from very high-resolution (VHR) synthetic aperture radar (SAR) images, a new method, based on statistical and structural information fusion, is proposed in this paper. The proposed method contains two stages: First, using order statistics constant false alarm rate (OS-CFAR) and power ratio (PR) detectors, a set of detections are made. These detections have different statistical properties, compared to the other objects, and these properties are selected for discriminating buildings from clutters. Second, the morphological analysis is used for increasing the precision of the detection. In this stage, segments, which have the most similarities to buildings in terms of shape and size, are extracted via various structural elements (SEs). The final result is obtained by fusing the two sets of detections. The experimental results on the four real VHR SAR images show that the proposed method has a high detection rate (DR) and low false alarm rate (FAR).

Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks

This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high-resolution (VHR) synthetic aperture radar (SAR) images. In this context, this paper has two major contributions. First, it presents a novel and generic workflow that initially classifies the spaceborne SAR tomography (TomoSAR) point clouds-generated by processing VHR SAR image stacks using advanced interferometric techniques known as TomoSAR-into buildings and nonbuildings with the aid of auxiliary information (i.e., either using openly available 2-D building footprints or adopting an optical image classification scheme) and later back project the extracted building points onto the SAR imaging coordinates to produce automatic large-scale benchmark labeled (buildings/nonbuildings) SAR data sets. Second, these labeled data sets (i.e., building masks) have been utilized to construct and train the state-of-the-art deep fully convolution neural networks with an additional conditional random field represented as a recurrent neural network to detect building regions in a single VHR SAR image. Such a cascaded formation has been successfully employed in computer vision and remote sensing fields for optical image classification but, to our knowledge, has not been applied to SAR images. The results of the building detection are illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering approximately 39 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -almost the whole city of Berlin- with the mean pixel accuracies of around 93.84%.

A framework for SAR-optical stereogrammetry over urban areas

Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverage, and spectral imaging ability, fusion techniques are required to integrate the different properties of each sensor and produce useful information. For example, synthetic aperture radar (SAR) data can be fused with optical imagery to produce 3D information using stereogrammetric methods. The main focus of this study is to investigate the possibility of applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical image pairs. For this purpose, the applicability of semi-global matching is investigated in this unconventional multi-sensor setting. To support the image matching by reducing the search space and accelerating the identification of correct, reliable matches, the possibility of establishing an epipolarity constraint for VHR SAR-optical image pairs is investigated as well. In addition, it is shown that the absolute geolocation accuracy of VHR optical imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be improved by a multi-sensor block adjustment formulation based on rational polynomial coefficients. Finally, the feasibility of generating point clouds with a median accuracy of about 2 m is demonstrated and confirms the potential of 3D reconstruction from SAR-optical image pairs over urban areas.

A Bottom-Up/Top-Down Hybrid Algorithm for Model-Based Building Detection in Single Very High Resolution SAR Image

Building detection from high-resolution synthetic aperture radar (SAR) image is an essential issue for many SAR applications in urban areas. In this letter, we propose a novel bottom-up/top-down hybrid algorithm for model-based building detection from single very high resolution (VHR) SAR image. First, the building model is generated and described by a set of extraction criteria, which restrict the spatial layout of a building and its primitive features. Specifically, the rectangles of different intensity levels are extracted from the SAR image as primitive features. Then the bottom-up stage proposes building candidates composed by extracted rectangles, and the top-down step predicts building candidates composed by weak features omitted in the primitive extraction. After that, all candidates are verified through false alarm detection. Under this framework, the detection performances can be greatly improved especially in dense built-up areas. The effectiveness of the proposed method is verified by experimental results obtained from real VHR SAR images.

Autofocus Correction of Residual RCM for VHR SAR Sensors With Light-Small Aircraft

Due to the light weight and small size, synthetic aperture radar (SAR) sensors with light-small aircraft are very sensitive to atmospheric turbulences, which cause serious trajectory deviations. Limited by the cost or payloads, light-small aircraft sometimes cannot carry high-accuracy inertial navigation systems (INS)/global positioning systems (GPS). For the very high resolution (VHR) case, residual range cell migration (RCM) often exceeds several range resolution cells during the course of the synthetic aperture, thus degrading the image quality substantially. In this paper, a robust scheme for the correction of residual RCM is presented. The scheme consists of three principle steps. First, the range-compressed data in the signal domain are divided into subapertures in azimuth. For each subaperture, a sliding window is used to select the subblock data with the highest signal-to-clutter ratio. Second, for each subaperture, residual RCM is estimated by using the selected subblock data based on a nonparametric entropy minimization technique. Third, the estimated residual RCM of all subapertures are filtered and coherently combined to perform the correction for the full-aperture data. Processing the results of VHR SAR raw data shows that the proposed scheme is effective for highly precise imaging with light-small aircraft equipped with only low-accuracy INS/GPS.

Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

Mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Most synthetic aperture radar (SAR) data-based landslide detection approaches reported in the literature use change detection techniques, requiring very high resolution (VHR) SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth’s landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia.

Multilayer CFAR Detection of Ship Targets in Very High Resolution SAR Images

This letter proposes a new ship target detection method for very high resolution (VHR) synthetic aperture radar (SAR) images based on multilayer constant false alarm rate (CFAR). First, combined with log-normal distribution, a multilayer CFAR method is designed to overcome the holes and the fracture in the traditional detected results. This method can retain more details of ships and takes much less time than the traditional CFAR method for VHR SAR images. Second, based on a priori knowledge of ships, we use the sliding window to remove the false alarm targets. Finally, In order to measure the size and shape of a ship, we extract the outline of a ship and fill it by a level set method. Experimental results, carried out on real SAR images, demonstrate that the proposed approach outperforms the previous one in terms of the detection ratio of pixels instead of the number of ships.

Power Spectra of Very High Resolution SAR Amplitude Images of Urban Areas

With the launch of COSMO/SkyMed and TerraSARX missions, very high resolution (VHR) Synthetic Aperture Radar (SAR) images of urban areas have become routinely available. In particular, in the spotlight acquisition mode, COSMO/SkyMed SARs are able to obtain a resolution even better than one meter. Accordingly, in principle a huge amount of information on objects present in the urban scenario can be extracted from such images; however, due to the involved interaction between incident electromagnetic wave and imaged scene, direct interpretation of VHR SAR images is not straightforward, and different methods to analyze them are being developed by the scientific community. In this paper, we explore the potentiality of using the power spectral density (PSD) of VHR SAR amplitude images of urban areas for information extraction. First, we propose a theoretical model of the PSD of such images, and then we test the validity of the developed model on COSMO/SkyMed spotlight SAR images of urban areas. Finally, some suggestions are provided on how the parameters of the obtained PSD can be exploited to extract useful information from VHR SAR amplitude images of urban areas.

Ratio-Detector-Based Feature Extraction for Very High Resolution SAR Image Patch Indexing

With the advent of very high resolution (VHR) synthetic aperture radar (SAR) images, local content description is becoming a critical issue for indexing. Conventional SAR image analysis techniques, like segmentation and pixel-level classification, are likely to fail as high-level semantic description should be considered for better discrimination. Therefore, we propose to use image-patch-based analysis method for SAR image interpretation. Inspired by ratio edge detector, in this letter, a new feature extraction method represented by the mean ratios in different directions is proposed for VHR SAR image content characterization. Based on the mean ratio, two simple yet powerful and robust features are proposed for SAR image patch indexing. One is the bag-of-word model using not only the basic statistics, i.e., local mean and variance, but also the mean ratios in different directions. The second one is an adaptation of the Weber local descriptor to SAR images by substituting the gradient with the ratio of mean differences in vertical and horizontal directions. To evaluate the proposed features, image patch indexing based on active learning using a SAR image database consisting of high-resolution TerraSAR-X patches is performed. Comparison with the state-of-the-art features, particularly texture features, has shown improved performance for SAR image categorization.

A Hierarchical Approach to Change Detection in Very High Resolution SAR Images for Surveillance Applications

The availability of very high resolution (VHR) synthetic aperture radar (SAR) images, which can be acquired by satellites over the same geographical area with short repetition interval, makes the development of effective unsupervised change detection (CD) techniques very important. This paper proposes a hierarchical approach to CD in VHR SAR images for addressing surveillance applications, where VHR data are acquired with high temporal resolution (e.g., one image every few days). The proposed approach is based on two concepts: exploitation of a multiscale technique for a preliminary detection of areas containing changes in backscattering at different scales (hot spots) and explicit modeling of the semantic meaning of changes by using both the intrinsic SAR image properties (e.g., acquisition geometry and scattering mechanisms) and the available prior information. In order to illustrate the effectiveness of the proposed approach, a problem of freight traffic surveillance is addressed considering two data sets. Each of them is made up of a pair of multitemporal VHR SAR images acquired by the COSMO-SkyMed (COnstellation of small Satellites for the Mediterranean basin Observation) constellation in spotlight mode. Each data set defines a complex CD problem due to both the presence of a variety of changes on the ground and the complexity of object backscattering. Experimental results point out the effectiveness of the proposed approach.

Signature analysis and 3-D reconstruction of rectangular building in very high resolution SAR images

PDF HTML阅读 XML下载 导出引用 引用提醒 高分辨率SAR图像中矩形建筑物特性分析与三维重建 DOI: 作者: 作者单位: 中国科学院空间信息处理与应用系统技术重点实验室,中国科学院电子学研究所,中国科学院空间信息处理与应用系统技术重点实验室,中国科学院电子学研究所,中国科学院空间信息处理与应用系统技术重点实验室,中国科学院电子学研究所,中国科学院空间信息处理与应用系统技术重点实验室,中国科学院电子学研究所,中国科学院空间信息处理与应用系统技术重点实验室,中国科学院电子学研究所 作者简介: 通讯作者: 中图分类号: 基金项目: Signature analysis and 3-D reconstruction of rectangular building in very high resolution SAR images Author: Affiliation: Institute of Electronic, Chinese Academy of Sciences,The Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences,The Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences,The Key Laboratory of Technology in Geo-spatial Information Processing and Application System,The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:近年来不断发展成熟的合成孔径雷达技术将获取的图像分辨率提高到分米级.在高分辨率条件下, 建筑物在SAR图像上表现出的空间信息更加丰富，结构特征更加明显.首先提出了分解模型对高分辨 SAR图像中矩形建筑物的特性进行详细分析.在此模型中，散射效应根据不同的贡献来源被细分，以便于解析建筑物图像特征在不同的SAR成像条件下的几何结构和空间分布规律.然后基于建筑物图像表征的结构先验，提出了一种新的单幅高分辨率SAR图像建筑物检测和3-D重建算法，其中包括模型匹配的图像特征的提取，以及先验引导的重建过程.最后，选用了实际高分辨率SAR图像进行建筑物检测和三维重建实验并对重建结果进行了讨论. Abstract:The new advanced very high resolution (VHR) synthetic aperture radar (SAR) sensors are capable of achieving sub-metric spatial resolution, which offers the opportunity for a fine level of analysis of man-made structures in such data. In this paper, we first proposed a decomposed model to analyze the signature of rectangular buildings in VHR SAR images. In the model, the scattering effects were subdivided according to different contributors. Accordingly, detailed geometrical structures and spatial distributions of image features of building objects with respect to varied SAR illumination conditions were exploited. Then, based on the generalized effects, a new detection and reconstruction algorithm was proposed for 3-D reconstruction from single VHR SAR images. Specifically, a matching procedure was applied to extract image features, and prior knowledge was employed in the reconstruction steps to jointly perform dimensions retrieval and building type identification. Finally, the proposed approach was applied to VHR SAR imagery and the reconstruction results were discussed. 参考文献 相似文献 引证文献

TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns

TerraSAR-X add-on for digital elevation measurement (TanDEM-X) is a German Earth observation mission collecting a total of two global coverages of very high resolution (VHR) synthetic aperture radar (SAR) X-band data with a spatial resolution of around three meters in the years 2011 and 2012. With these, the TanDEM-X mission (TDM) will provide a unique data set which is complementary to existing global coverages based on medium (MR) or high resolution (HR) optical imagery. The capabilities of the TDM in terms of supporting the analysis and monitoring of global human settlement patterns are explored and demonstrated. The basic methodology for a fully-operational detection and delineation of built-up areas from VHR SAR data is presented along with a description of the resulting geo-information product-the urban footprint (UF) mask-and the operational processing environment for the UF production. Moreover, potential follow-on analyses based on the intermediate products generated in the context of the UF analysis are introduced and discussed. The results of the study indicate the high potential of the TDM with respect to an analysis of urbanization patterns, peri-urbanization, spatio-temporal dynamics of settlement development as well as population estimation, vulnerability assessment and modeling of global change.

On the Relationship Between Double Bounce and the Orientation of Buildings in VHR SAR Images

In this letter, we study empirically the relation between the double-bounce effect of buildings in very high resolution (VHR) synthetic aperture radar (SAR) and the orientation angle for two different ground materials (i.e., asphalt and grass) by analyzing two different TerraSAR-X VHR spaceborne SAR images. Furthermore, we compare our empirical results with the simulations obtained using theoretical electromagnetic models. In order to deal with slightly rough surfaces, we also present a novel model for double-bounce scattering based on the small-perturbation method. We show that the double-bounce effect results in different power signatures, depending on the type of the building and the surrounding ground properties. Finally, we discuss the reliability of theoretical models for predicting the double-bounce power for the analyzed data sets. The models can predict the general behavior of the double bounce but lack in calculating the accurate double-bounce radar cross section reliably.