Improving the image quality of spaceborne multiple-aperture SAR under minimization of sidelobe clutter and noise

As we know, a multiple-aperture radar carried by a group of formation-flying satellites can be used to improve the coverage and azimuth resolution of spaceborne synthetic aperture radar (SAR) simultaneously. However, in the case of the reconstruction of azimuth signal from a periodic nonuniform sampling sequence, the sidelobe clutter and noise may be amplified by tens of decibels in some cases, which makes the final SAR image blurry. In this letter, we present the analytical result of the amplification factor of sidelobe clutter and noise for the evaluation of the reconstruction performance. According to the result, we propose a method to reduce the sidelobe clutter and noise, which considerably improves the quality of SAR image. Computer simulation results confirm the effectiveness of the method.     

Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping

ABSTRACT

This study investigates misregistration issues between Landsat-8/ Operational Land Imager and Sentinel-2A/ Multi-Spectral Instrument at 30?m resolution, and between multi-temporal Sentinel-2A images at 10?m resolution using a phase-correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30?m resolution between Landsat-8 and Sentinel-2A images, and 1.2 pixels and 2.8 pixels at 10?m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07?±?0.02 pixels at 30?m resolution, and 0.09?±?0.05 and 0.15?±?0.06 pixels at 10?m resolution for the same and adjacent Sentinel-2A orbits, respectively, for multiple tiles and multiple conditions. A simpler 1st order polynomial function (affine transformation) yielded RMSE of 0.08?±?0.02 pixels at 30?m resolution and 0.12?±?0.06 (same Sentinel-2A orbits) and 0.20?±?0.09 (adjacent orbits) pixels at 10?m resolution.     

联合稀疏约束的双通道星载SAR图像重建

摘 要：针对提高星载SAR图像质量的问题,研究双通道星载SAR图像重建模型。由目标散射中心理论和后向散射特性分析,得出SAR图像具有稀疏特性,并且能够用确定性稀疏先验约束表达。将稀疏特性先验从单幅机载SAR图像处理问题中推广引入到两幅星载SAR图像重建问题中,提出基于散射中心稀疏和强散射梯度的双通道正则化重建模型,并采用椭圆抛物面模型估计重建中的降质矩阵,用双下降求解方法求解重建模型。并用Cosmo-SkyMed数据进行了实验验证。实验表明,该重建模型能够提高SAR图像的距离向和方位向分辨率,改善图像质量,提高解译能力。     

相位误差、位置误差和灰度误差三者关系及SAR原始图像像元不确定性

由于SAR遥感的独特优势，使它得到了广泛的应用。因此，对SAR图像的不确定性研究也越来越必要和迫切了。针对数据分析方法的不足，该文着重从机理的角度探讨SAR原始像元的不确定性。相位误差、位置误差和灰度误差三者关系、各种误差分类和综合以及这些误差对SAR原始像元不确定性作用等问题往往是困扰从机理角度对SAR图像不确定性研究的主要障碍，该文重点讨论这些问题。     

Validation of Indian National DEM from Cartosat-1 Data

CartoDEM is an Indian National DEM generated from Cartosat-1 stereo data. Cartosat-1, launched in May, 2005, is an along track (aft ?5°, Fore +26°) stereo with 2.5 m GSD, give base-height ratio of 0.63 with 27 km swath. The operational procedure of DEM generation comprises stereo strip triangulation of 500?×?27 km segment with 10 m posting along with 2.5 m resolution ortho image and free—access posting of 30 m has been made available (bhuvan.nrsc.gov.in). A multi approach evaluation of CartoDEM comprising (a) absolute accuracy with respect to ground control points for two sites namely Jagatsinghpur -flat and Dharamshala- hilly; second site i.e. Alwar-plain and hilly with high resolution aerial DEM, (b) relative difference between SRTM and ASTERDEM (c) absolute accuracy with ICESat GLAS for two sites namely Jagatsinghpur-plain and Netravathi river, Western Ghats-hilly (d) relative comparison of drainage delineation with respect to ASTERDEM is reported here. The absolute height accuracy in flat terrain was 4.7 m with horizontal accuracy of 7.3 m, while in hilly terrain it was 7 m height with a horizontal accuracy of 14 m. While comparison with ICESat GLAS data absolute height difference of plain and hilly was 5.2 m and 7.9 m respectively. When compared to SRTM over Indian landmass, 90 % of pixels reported were within ±8 m difference. The drainage delineation shows better accuracy and clear demarcation of catchment ridgeline and more reliable flow-path prediction in comparison with ASTER. The results qualify Indian DEM for using it operationally which is equivalent and better than the other publicly available DEMs like SRTM and ASTERDEM.     

相位误差、位置误差和灰度误差三者关系及SAR原始图像像元不确定性

由于SAR遥感的独特优势，使它得到了广泛的应用。因此，对SAR图像的不确定性研究也越来越必要和迫切了。针对数据分析方法的不足，该文着重从机理的角度探讨SAR原始像元的不确定性。相位误差、位置误差和灰度误差三者关系、各种误差分类和综合以及这些误差对SAR原始像元不确定性作用等问题往往是困扰从机理角度对SAR图像不确定性研究的主要障碍，该文重点讨论这些问题。     

天宫一号高光谱遥感场景分类数据集及应用

天宫一号高光谱成像仪具有空间分辨率高、光谱分辨率高、图谱合一等特性，在中国航天高光谱领域具有里程碑的意义。针对一般遥感场景分类数据集尺度单一、光谱分辨率较低等问题，本文提出基于天宫一号的多谱段、高空间分辨率、多时相高光谱遥感场景分类数据集(TG1HRSSC)。利用天宫一号高光谱成像仪获取的高质量数据，经过辐射校正、几何校正、空间裁剪、波段筛选、数据质量分析与控制等，制作了一批通用的航天高光谱遥感场景分类数据集，通过载人航天空间应用数据推广服务平台（http://www.msadc.cn[2019-09-10]）进行分发和共享。该数据集包括天宫一号高光谱成像仪获取的城镇、农田、林地、养殖塘、荒漠、湖泊、河流、港口、机场等9个典型地物场景的204个高光谱影像数据，其中5 m分辨率全色谱段1个波段、10 m分辨率可见近红外谱段54个有效波段以及20 m分辨率短波红外谱段52个有效波段。研究利用AlexNet、VGG-VD-16、GoogLeNet等深度学习算法网络对构建的数据集进行场景分类的试验，结果表明该数据集的场景分类应用实现较好效果。由于该数据集具备高分辨、高光谱等特征优势，未来在语义理解、多目标检测等方面有着广泛的应用价值。     

不依赖人工定标器的全极化SAR定标研究进展

极化定标是极化合成孔径雷达应用的前提。传统极化定标方法以地面布设的人工定标器为参考,通过极化畸变模型对系统误差进行求解与标定。然而,人工定标器价格昂贵、数量稀少,每次定标任务都需根据传感器过境方向、雷达视角等信息进行设备调整;此外,现代雷达系统工作波位多、入射角调节范围大,不同视角获取影像的定标参数也不相同,这对地面定标设备的布设精度、调整的时效性提出了更高要求。为了及时、快速地完成极化定标,如何以自然界中的某些特殊地物作为人工定标器的替代品来完成定标具有重大的科学价值。本文综述了近年来国内、外提出的不依赖人工定标器的SAR极化定标研究进展（即自主定标）。首先阐述了极化定标的基本流程与极化质量评价体系;然后对近年来高精度自主定标相关研究进行了梳理,根据技术特点将其分为基于自然地物约束的自主极化定标、基于似角反射器的自主极化定标两类,对不同算法适用性进行了分析;最后对未来的研究方向进行了展望。     

A Method to Determine Appropriate Spatial Resolution for Hard Image Classification

This study analyzed the relationship between the spatial resolution and the hard classification effect based on pixel-based image classification, and then discussed how to determine appropriate spatial resolution. Thematic maps of winter wheat derived from 250 m MODIS image, 19.5 m China-Brazil Earth Resources Satellite (CBERS) image, and 2.44 m QuickBird image were used to examine the classification effect as a case study. It indicated that the “Pareto Boundaries” and the “within-class variability” could be used to determine the coarsest and the highest resolution for hard classification, respectively. The methods proposed in this study should be useful to guide how to select appropriate spatial resolution for land cover mapping.     

加速迭代硬阈值ISAR稀疏成像

加速迭代硬阈值(AIHT)作为一种压缩感知(CS)算法,具有计算复杂度低、内存占用小的特点,在较小数目的观测下,经过有限次迭代便能获得较好的重建效果。因此,本文将该算法应用到逆合成孔径雷达(ISAR)稀疏成像,对该方法的具体特征和适用性进行了详细分析。仿真结果表明,在相同信噪比和回波脉冲数条件下,基于AIHT的ISAR成像方法,相比于稀疏贝叶斯算法(SBL),在重建时间上缩短了至少2个量级;相比于迭代加权最小二乘法(IRLS)和平滑l0范数算法,在重建质量上提高了约3倍。经实测数据处理结果验证,在1/4原始脉冲数条件下,该方法仍具有良好的聚焦性能和压低旁瓣效果。     

Comparative evaluation of horizontal accuracy of elevations of selected ground control points from ASTER and SRTM DEM with respect to CARTOSAT-1 DEM: a case study of Shahjahanpur district,Uttar Pradesh,India

Digital elevation model (DEM) data of Shuttle Radar Topography Mission (SRTM) are distributed at a horizontal resolution of 90 m (30 m only for US) for the world, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM data provide 30 m horizontal resolution, while CARTOSAT-1 (IRS-P5) gives 2.6 m horizontal resolution for global coverage. SRTM and ASTER data are available freely but 2.6 m CARTOSAT-1 data are costly. Hence, through this study, we found out a horizontal accuracy for selected ground control points (GCPs) from SRTM and ASTER with respect to CARTOSAT-1 DEM to implement this result (observed from horizontal accuracy) for those areas where the 2.6-m horizontal resolution data are not available. In addition to this, the present study helps in providing a benchmark against which the future DEM products (with horizontal resolution less than CARTOSAT-1) with respect to CARTOSAT-1 DEM can be evaluated. The original SRTM image contained voids that were represented digitally as ?140; such voids were initially filled using the measured values of elevation for obtaining accurate DEM. Horizontal accuracy analysis between SRTM- and ASTER-derived DEMs with respect to CARTOSAT-1 (IRS-P5) DEM allowed a qualitative assessment of the horizontal component of the error, and the appropriable statistical measures were used to estimate their horizontal accuracies. The horizontal accuracy for ASTER and SRTM DEM with respect to CARTOSAT-1 were evaluated using the root mean square error (RMSE) and relative root mean square error (R-RMSE). The results from this study revealed that the average RMSE of 20 selected GCPs was 2.17 for SRTM and 2.817 for ASTER, which are also validated using R-RMSE test which proves that SRTM data have good horizontal accuracy than ASTER with respect to CARTOSAT-1 because the average R-RMSE of 20 GCPs was 3.7 × 10^?4 and 5.3 × 10^?4 for SRTM and ASTER, respectively.     

基于小波理论的IKONOS卫星全色影像和多光谱影像的融合

1999年9月24日发射成功的世界上第一颗商用1m分辨率的卫星IKONOS，具有1m分辨率的全色影像和4m分辨率的多光谱影像，通过对IKONOS1m分辨率的全色影像和4m分辨率的多光谱影像的融合可以获得1m分辨率的多光谱影像，为应用提供理高质量的数据源，基于小波多分辨率分析的MRAGM方法，它适合处理任意整数分辨率之比的融合情况，具有在提高影像空间分辨率的同时又保持色调和饱和度不变的优越性，而实现它的关键是构建具有紧支撑特性的M进制低通尺度函滤波器，本文利用基于四进制小波滤波器的MRAGM算法，成功地把全色影像和多光谱影像进行了融合，结果显示该方法的效果令人满意。     

基于最小二乘平差的全极化SAR配准偏移量估计方法

全极化合成孔径雷达影像(PolSAR)相对单极化SAR影像有更加丰富的地表信息。为了提高SAR影像偏移量跟踪技术估算偏移量的精度,提出一种基于最小二乘平差法的全极化SAR数据偏移量估计方法。首先利用全极化SAR不同极化通道数据估算偏移量得到多个观测值,然后通过最小二乘平差法对多余观测值循环剔除粗差得到最优偏移量。文中选取美国科罗拉多州湖城(Lake city)区域的两景JPL无人机UAVSAR全极化SAR影像进行实验,结果表明,文中新方法具有良好的粗差探测和去除功能,相对于已有研究结果,在方位向和距离向的偏移量估算精度都有明显提高,达到15%~25%。新方法提高了偏移量跟踪估算偏移量的精度,这对利用偏移量跟踪技术监测滑坡、地震及冰川等有重要的意义。     

Production of high-resolution remote sensing images for navigation information infrastructures

This paper introduces the image fusion approach of multi-resolution analysis-based intensity modulation (MRAIM) to produce the high-resolution multi-spectral images from high-resolution panchromatic image and low-resolution multi-spectral images for navigation information infrastructure. The mathematical model of image fusion is derived according to the principle of remote sensing image formation. It shows that the pixel values of a high-resolution multi-spectral images are determined by the pixel values of the approximation of a high-resolution panchromatic image at the resolution level of low-resolution multi-spectral images, and in the pixel valae computation the M-band wavelet theory and the d trous algorithm are then used. In order to evaluate the MRAIM approach, an experiment has been carried out on the basis of the IKONOS 1 m panchromatic image and 4 m multi-spectral images. The result demonstrates that MRAIM image fusion approach gives promising fusion results and it can be used to produce the high-resolution remote sensing images required for navigation information infrastructures.     

Production of high-resolution remote sensing images for navigation information infrastructures

This paper introduces the image fusion approach of multi-resolution analysis-based intensity modulation (MRAIM) to produce the high-resolution multi-spectral images from high-resolution panchromatic image and low-resolution multi-spectral images for navigation information infrastructure. The mathematical model of image fusion is derived according to the principle of remote sensing image formation. It shows that the pixel values of a high-resolution multi-spectral images are determined by the pixel values of the approximation of a high-resolution panchromatic image at the resolution level of low-resolution multi-spectral images, and in the pixel valae computation the M-band wavelet theory and the à trous algorithm are then used. In order to evaluate the MRAIM approach, an experiment has been carried out on the basis of the IKONOS 1 m panchromatic image and 4 m multi-spectral images. The result demonstrates that MRAIM image fusion approach gives promising fusion results and it can be used to produce the high-resolution remote sensing images required for navigation information infrastructures.     

小型消费级无人机地形数据精度验证

低空遥感是近几年快速发展、应用非常广泛的新兴技术。小型消费级无人机集成可见光传感器,具有快速、灵活、高性价比等优势,受到广泛关注。然而目前有关该类无人机综合测量精度的研究不足,影响其进一步的推广应用。为此,本文开展了针对大疆(Phantom 3 professional)小型消费级无人机地形测量数据精度验证工作,设定6种航高(50 m、60 m、70 m、80 m、90 m和100 m)获取研究区的立体像对,生成影像点云(point cloud)、数字表面模型(DSM)以及数字正射影像图(DOM)等结果。在测量精度验证中,首先,在标准实验场均匀布设地面控制点(GCP),利用差分GPS测出GCP的高精度3维坐标;然后,通过GCP对立体像对进行绝对定位;最后,利用误差统计方法分析上述结果的测量精度。验证表明,在50—100m航高时,无人机影像结果的分辨率为2.22—4.23 cm,水平方向平均误差为±0.51 cm,垂直方向平均误差为±4.39 cm,相对均方根误差(RMSE)水平方向为±2.79 cm,垂直方向为±9.98 cm。研究结果表明,小型消费级无人机在飞控系统下的测量精度可达厘米级,这不仅为野外地理和生态调查工作者提供一种低成本、快速、灵活与精确获取地形信息的新型测量手段,同时还对使用此类无人机做航测应用及飞行参数设置提供一定参考。     

Improved IHS Algorithm for Fusing High Resolution Satellite Images of Urban Areas

The intensity-hue-saturation method is used frequently in image fusion due to its efficiency and high spatial quality. The main shortage is its spectral distortion stemmed from replacement of intensity band with higher resolution image. In this study, a new method is introduced to improve the spectral quality of the Intensity-Hue-Saturation (IHS) algorithm. The goal of this study is to produce the fused image that has a better spectral and spatial quality with respect to the original images in term of visual comparison and the classification result. In this regard, an improved statistical approach is developed to combine an intensity band from IHS algorithm and an input high resolution image such as SAR or Panchromatic image. Then the intensity image is replaced by the combined image band. Final fused images are attained using the inverse IHS algorithm. The proposed fusion algorithm is tested on two data sets of: a) panchromatic and multi spectral bands of IKONOS image with the same acquisition date, and b) multi spectral and HH bands of IKONOS and TerraSAR-X images respectively with different acquisition dates. Moreover, the obtained results are compared with other fusion methods like IHS, Gungor, Brovey and synthetic variable ratio. The results show less spectral discrepancy of the proposed method comparing to other methods. Finally, the outcome of proposed method is classified and classification overall accuracy is improved by 5.6 and 2 percentage for data set ‘a’ and ‘b’ respectively.     

Tetrolet域利用时空相关性的云图压缩感知重建

针对卫星云图数据量大,但传输通道和存储空间相对狭小的问题,本文基于Tetrolet变换,利用相邻时次云图的时空相关性,实现了一种高重构质量的卫星云图压缩感知方法。该方法将善于表达图像方向纹理及边缘信息的Tetrolet变换引入压缩感知的稀疏表示环节,从而很好地体现了卫星云图细节丰富、纹理结构复杂的特性;同时,考虑到卫星云图序列间的相关性,将时间相邻的卫星云图组成图像组,以中间时刻云图作为参考图像,计算其与相邻时次云图的差异,通过在参考图片及序列差异图片间合理分配采样率,获取测量数据,在压缩感知框架下,采用带平滑处理的投影Landweber算法,重构出相邻时次的图像组。实验结果表明,Tetrolet变换适用于卫星云图的稀疏表示,而且图像组时空相关性的利用可显著提高重构云图的视觉效果及客观评价指标;在采样率低于0.2时,红外1、水汽和可见光3个通道重构云图的峰值信噪比(PSNR)较传统方法平均提高了7.48 dB,13.51 dB和6.15 dB。由此可见,本文方法可以通过获取少数随机测量值,重构出高质量的卫星云图,不仅为云图数据的低比特率压缩提供了一种可行的解决方案,而且对于其他序列图像的压缩采样具有借鉴意义。     

GPS-gravimetric geoid determination in Egypt

The main objective of this study is to improve the geoid by GPS/leveling data in Egypt. Comparisons of the gravimetric geoid with GPS/leveling data have been performed. On the basis of a gravimetric geoid fitted to GPS/leveling by the least square method, a smoothed geoid was obtained. A high-resolution geoid in Egypt was computed with a 2.5′×2.5′ grid by combining the data set of 2600 original point gravity values, 20″×30″ resolution Digital Terrain Model (DTM) grid and the spherical harmonic model EGM96. The method of computation involved the strict evaluation of the Stokes integral with 1D-FFT. The standard deviation of the difference between the gravimetric and the GPS/leveling geoid heights is ±0.47 m. The standard deviation after fitting of the gravimetric geoid to the GPS/leveling points is better than ±13 cm. In the future we will try to improve our geoid results in Egypt by increasing the density of gravimetric coverage.     

U-Net模型对不同空间分辨率防护林提取精度的影响

针对当前无人机影像获取精度高但数据规模小的问题,本文提出通过U-Net模型探讨不同空间分辨率对防护林提取精度的影响.以CW-20复合翼无人机搭载Micro MAC12 Snap多光谱传感器获取的300 m(空间分辨率0.15 m)、400 m(空间分辨率0.20 m)、500 m(空间分辨率0.25 m)3种不同高度的...