A unified framework for crop classification in southern China using fully polarimetric,dual polarimetric,and compact polarimetric SAR data

With the development of synthetic aperture radar (SAR) techniques, various imaging modes that involve single polarimetry, dual polarimetry, full polarimetry (FP), and compact polarimetry (CP) have been proposed and applied to SAR systems. This article attempts to introduce a unified framework for crop classification in southern China using FP, coherent HH/VV, and CP data. By analysing the polarimetric response from different land-cover types (including rice, banana trees, sugarcane, eucalyptus, water, and built-up areas in the experimental site) and by exploring the similarities between data in these three modes, a knowledge-based characteristic space is created and a unified classification framework is presented. Time-series data acquired by TerraSAR-X over the Leizhou Peninsula, southern China, are used in our experiments. The overall classification accuracies for data in the FP and coherent HH/VV modes are approximately 95%, and for data in the CP mode, the accuracy is 91%, which suggest that the proposed classification scheme is effective. Compared with the Wishart Maximum Likelihood (ML) classifier, the proposed method provides approximately 5.64%, 7.30%, and 6.48% higher classification accuracies in the FP, HH/VV, and circular transmit and dual circular receive modes, respectively.     

Change detection of isolated housing using a new hybrid approach based on object classification with optical and TerraSAR-X data

Optical and microwave high spatial resolution images are now available for a wide range of applications. In this work, they have been applied for the semi-automatic change detection of isolated housing in agricultural areas. This article presents a new hybrid methodology based on segmentation of high-resolution images and image differencing. This new approach mixes the main techniques used in change detection methods and it also adds a final segmentation process in order to classify the change detection product. First, isolated building classification is carried out using only optical data. Then, synthetic aperture radar (SAR) information is added to the classification process, obtaining excellent results with lower complexity cost. Since the first classification step is improved, the total change detection scheme is also enhanced when the radar data are used for classification. Finally, a comparison between the different methods is presented and some conclusions are extracted from the study.     

L-distribution for multilook polarimetric SAR data and its application in ship detection

Statistical modeling based on the product model is an effective method to fit the sea clutter of polarimetric synthetic aperture radar (PolSAR) images in nonhom...     

Discrimination of maize crop with hybrid polarimetric RISAT1 data

Microwave remote sensing provides an attractive approach to determine the spatial variability of crop characteristics. Synthetic aperture radar (SAR) image data provide unique possibility of acquiring data in all weather conditions. Several studies have used fully polarimetric data for extracting crop information, but it is limited by swath width. This study aimed to delineate maize crop using single date hybrid dual polarimetric Radar Imaging Satellite (RISAT)-1, Fine Resolution Stripmap mode (FRS)-1 data. Raney decomposition technique was used for explaining different scattering mechanisms of maize crop. Supervised classification on the decomposition image discriminated maize crop from other land-cover features. Results were compared with Resourcesat-2, Linear Imaging Self Scanner (LISS)-III optical sensor derived information. Spatial agreement of 91% was achieved between outputs generated from Resourcesat-2, LISS-III sensor and RISAT-1 data.     

Discrimination potential of X-band polarimetric SAR data

In anticipation of X-band polarimetric Synthetic Aperture Radar (SAR) data from future sensors, we investigated the potential of X-band fully polarimetric data for discriminating between the principal classes present in a study site near Avignon, France. Decomposition and analysis techniques have been applied to a dataset acquired by the ONERA airborne RAMSES (Radar Aéroporté Multi-Spectral d'Etude des Signatures) SAR. Results show that X-band provides some discrimination capability. The polarimetric parameters, entropy and α-angle, show clearly that these signature classes are grouped into five clusters corresponding to physical scattering characteristics. The introduction of the anisotropy parameter does not increase our ability to distinguish between different classes whose clusters are in the same entropy/α-angle zone. The correlation observed between the radar signal and the surface roughness over bare soils is very low.     

Distributed target detection with polarimetric MIMO radar in compound-Gaussian clutter

This paper mainly deals with distributed targets detection with a polarimetric MIMO radar against compound-Gaussian clutter dominated scenario with unknown covariance matrix. First, the general polarimetric detecting problem of the distributed targets is developed to the MIMO radar, and then, the fully adaptive Generalized Likelihood Ratio Test (GLRT) is devised according to the well known two-step design procedure. Three covariance matrix estimation strategies using the secondary data are introduced to make derived receiver fully adaptive. A thorough performance assessment is given, and via several numerical examples, the results highlight that the spatial and polarization diversities can be exploited to improve the detection performance of the distributed targets in compound-Gaussian background, and it outperforms the phased-array counterpart, the adaptive loss is completely acceptable in practical applications. Meanwhile, the fixed-point estimation strategy is more suitable to implement the adaptive detection algorithm.     

恶意软件的时序对偶数据流图挖掘及其检测方法

基于数据流图的恶意软件检测方法通常仅关注API(application programming interface)调用过程中的数据流信息,而忽略API调用顺序信息。为解决此问题,所提方法在传统数据流图的基础上融入API调用的时序信息,提出恶意软件时序对偶数据流图的概念,并给出模型挖掘方法,最后提出一种基于优化的图卷积网络对时序对偶数据流图进行分类、进而用于恶意软件检测与分类的方法。实验结果表明,所提方法的恶意软件识别准确率较传统基于数据流图的恶意软件识别方法有更好的检测效果。     

极化SAR影像边缘检测综述

极化合成孔径雷达（SAR）图像包含目标丰富的散射信息,在边缘检测中具有重大的潜力。对极化SAR影像边缘检测问题进行了系统的研究,从单极化SAR出发,分析了极化SAR边缘检测问题,对已有的方法进行了分类总结,重点介绍了极化SAR边缘检测的最新进展,指出了当前存在的问题,对极化SAR边缘检测的发展趋势进行了展望。     

基于物理散射模型的全极化SAR图像增强滤波算法

针对传统的极化SAR滤波方法图像中城镇区域和植被区域地物在滤波中易被混淆, 导致滤波后图像中地物边缘保持效果下降的问题, 提出了一种增强的保持极化散射特性的滤波算法。利用一种增强的四分量极化分解方法获取更加精确的地物散射机制, 并将散射机制信息引入滤波方法中, 使滤波算法中像素的散射机制更精确。增强的四分量极化分解方法引入了极化SAR数据的定向角补偿技术、一种新的体散射模型以及两种散射功率限制条件, 来改进Freeman-Durden分解的结果。理论分析和实验结果表明, 改进后的方法获取了比传统的极化SAR图像滤波算法更加理想的计算结果。     

Towards a European convention on data protection

The widespread transborder flow of computerised personal data has created an urgent necessity for international arrangements protecting the right to privacy. The Council of Europe is now preparing a draft Convention on Data Protection, the basic principles of which will be the free flow of information among the Contracting States and the protection of the privacy of the subjects of such information. Its provisions would, among other things, designate the applicable law in cases of transfrontier data flow and would specify that all remaining conflicts between laws be resolved in favour of the data subject. Because such an agreement requires a great deal of mutual assistance among the Contracting States, the Council of Europe, as a group of like-minded States, is perhaps the best level at which to begin international co-operation in data protection.     

简缩极化SAR数据信息提取与应用

全极化(FP)成像模式丰富了合成孔径雷达(SAR)数据的信息量,在地物分类、环境监测、目标探测等领域取得了广泛应用,但是全极化系统受设计和维护复杂度、功率消耗、覆盖范围和数据下传等因素影响,制约了全极化SAR的应用.简缩极化(CP)SAR系统不仅降低了全极化SAR系统的复杂度,还能在一定程度上保持全极化信息,在森林参数反演、地物分类、目标检测等领域已取得了初步的成果.本文简要介绍了简缩极化SAR系统的基本原理,阐述了简缩极化SAR的全极化信息重建及分解的主要方法,并总结其近十年的主要研究成果,最后给出了其发展趋势.     

Snow depth in Dhundi: an estimate based on weighted bias corrected differential phase observations of dual polarimetric bi-temporal Sentinel-1 data

ABSTRACT

The studies on snow depth comprise a crucial area of research in the Indian Himalayas, where the seasonal snow cover primarily drives the rivers and significant water resources. In this paper, the initial estimates of the line of sight displacement obtained using differential interferometric phase in VV and VH polarizations using Sentinel-1 bi-temporal dual polarimetric SAR data corresponding to snow covered and snow free land cover, are improved by applying bias corrections for the snow phase and for residual errors in displacement derived from the corrected snow phase. The bias for the snow phase is computed from the observed phase in VV and VH polarizations for the snow free area and the bias for the residual errors is computed by observing the stationary pixels identified in the snow free area using a digital elevation model. The snow depth is computed as a weighted sum of the corrected displacements in the VV and VH polarization, with the weights derived using the local incidence angle. The snow depth results based on the proposed approach was evaluated with respect to field measurements and a coefficient of determination of 0.628 was observed with an improvement of ~0.4 as compared to the displacement observed in the VV and VH channel using the conventional method.     

Enhanced edge detection for polarimetric SAR images using a directional span-driven adaptive window

ABSTRACT

Automatic edge detection for polarimetric synthetic aperture radar (PolSAR) images plays a fundamental role in various PolSAR applications. The classic methods apply the fixed-shape windows to detect the edges, whereas their performance is limited in heterogeneous areas. This article presents an enhanced edge detection method for PolSAR data based on the directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and is constructed by adaptively selecting samples that follow the same statistical distribution. Therefore, it can overcome the limitation of classic fixed-shape windows. To obtain refined and reliable edge detection results in heterogeneous urban areas, we adopt the spherically invariant random vector (SIRV) product model since the complex Wishart distribution is often not met. In addition, a span ratio is combined with the SIRV distance to highlight the dissimilarity measure and to improve the robustness of the proposed method. The simulated PolSAR data and three real data sets from experimental synthetic aperture radar, electromagnetics institute synthetic aperture radar, and Radarsat-2 systems are used to validate the performance of the enhanced edge detector. Both quantitative evaluation and visual presentation of the results demonstrate the effectiveness of the proposed method and its superiority over the classic edge detectors.     

Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data

During recent years, synthetic aperture radar (SAR) data have been increasingly used for flood mapping. New radar satellites especially, such as TerraSAR-X, Radarsat-2 and COSMO-SkyMed, provide high-resolution data with high potential for fast and reliable detection of inundated areas. This article compares three simple approaches to derive water areas from SAR data in relation to the German–Vietnamese project, Water-related Information System for the Sustainable Development of the Mekong Delta (WISDOM). Two methods are pixel based and use histogram-based grey-level thresholds, as well as a homogeneity criterion for classification. The third approach is object based and applies characteristic attributes of water objects such as grey value, texture and relations to neighbouring objects. Further discussed are the influence of a variation of the thresholds and the challenges to validate water masks derived from active remote-sensing data. We implemented one of the introduced approaches for surface water derivation in a water mask processor for automatic water mask calculation from radar satellite imagery (WaMaPro). This fully automatic processing chain was developed to process TerraSAR-X and Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) imagery in order to meet the demands for automatic flood monitoring.     

Improving SAR data processing with polarimetric reference functions in the range Doppler algorithm

Synthetic aperture radar (SAR) is a form of radar that can be used to create images of objects and landscapes. The main important application of the polarimetric SAR can be found in surface and target decomposition process of its image processing. In this article, we propose a method of polarimetric SAR data processing using two new polarimetric reference functions of canonical targets with the intention to apply in coherent decompositions. Our experiment uses polarimetric backscatter characteristics of the dihedral and trihedral reflectors as the targets under a ground-based SAR geometry to create the polarimetric reference functions for azimuth compression in the SAR data processing. We process the data using Pauli decomposition to investigate the effect of our functions on the RGB (red, green, and blue) properties of the processed images. The results show that Pauli decomposition using our functions produces images with different distribution and intensity of RGB colours in the image pixels with some signs of improvement over the traditional range Doppler algorithm. This demonstrates that our polarimetric reference function can be used in the decomposition steps of the traditional SAR data processing and can potentially be used to reveal some useful quantitative physical information of target points of interest and improve image and surface classification.     

Towards a system for automatic facial feature detection

A model-based methodology is proposed to detect facial features from a front-view ID-type picture. The system is composed of three modules: context (i.e. face location), eye, and mouth. The context module is a low resolution module which defines a face template in terms of intensity valley regions. The valley regions are detected using morphological filtering and 8-connected blob coloring. The objective is to generate a list of hypothesized face locations ranked by face likelihood. The detailed analysis is left for the high resolution eye and mouth modules. The aim for both is to confirm as well as refine the locations and shapes of their respective features of interest. The detection is done via a two-step modelling approach based on the Hough transform and the deformable template technique. The results show that facial features can be located very quickly with Adequate or better fit in over 80% of the images with the proposed system.     

An algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data

TerraSAR-X (TS-X) is a new, fully polarized X-band synthetic aperture radar (SAR) satellite, which is a successor of the Spaceborne Imaging Radar X-band Synthetic Aperture Radar (SIR-X-SAR) and the SRTM. TS-X has provided high-quality image products over land and oceans for scientific and commercial users since its launch in June 2007. In this article, a new geophysical model function (GMF) is presented to retrieve sea surface wind speeds at a height of 10 m (U ₁₀) based on TS-X data obtained with VV polarization in the ScanSAR, StripMap and Spotlight modes. The X-band GMF was validated by comparing the retrieved wind speeds from the TS-X data with in situ observations, the high-resolution limited area model (HIRLAM) and QuikSCAT scatterometer measurements. The bias and root mean square (RMS) values were 0.03 and 2.33 m s^?1, respectively, when compared with the co-located wind measurements derived from QuikSCAT. To apply the newly developed GMF to the TS-X data obtained in HH polarization, we analysed the C-band SAR polarization models and extended them to the X-band SAR data. The sea surface wind speeds were retrieved using the X-band GMF from pairs of TS-X images obtained in dual-polarization mode (i.e. VV and HH). The retrieved results were also validated by comparing with QuikSCAT measurements and the results of the German Weather Service (DWD) atmospheric model. The obtained RMS was 2.50 m s^?1 when compared with the co-located wind measurements derived from the QuikSCAT, and the absolute error was 2.24 m s^?1 when compared with DWD results.