AMSS图像几何畸变来源分析

与星载MSS(或TM)相比,AMSS成像具有独特的特点,其为行扫描方式,扫描视场角大,飞机飞行高度低,运动变化大,姿态稳定性差。因此,各种成像干扰因素对AMSS图像的影响更为严重,引起的图像几何畸变更为复杂,校正不仅非常困难,而且仿卫星扫描图像几何模式得不到正确有效的校正效果。本文详细地定量分析了各种因素对AMSS图像几何特性的影响,希望有助于AMSS图像几何畸变校正方法的研究。     

基于华浩超算平台几何精校正模块的波段配准研究

KOMPSAT3(阿里郎三号)卫星携带高分辨率0.7 m全色和2.8 m多光谱有效载荷,回访周期短,覆盖范围广,得到了广泛的应用。但由于受太阳辐射、大气干扰、云折射等物理因素的影响,造成波段之间在局部区域图像上发生非线性几何畸变,导致多光谱假彩色合成数据产生重叠现象,严重影响多光谱数据质量。本文针对在极端条件下产生的图像局部非线性几何畸变现象,探索国内外各种波段间配准方法,研究发现基于华浩超算平台的几何精校正算法能很好地达到波段间配准效果,消除了多光谱假彩色合成数据的影像重叠现象。     

定位定向系统数据的航空高光谱影像几何校正

针对航空高光谱数据几何畸变严重的问题,该文探索了利用POS数据辅助航空高光谱影像几何校正的研究现状,分析了基于POS系统的导航数据的线阵推扫高光谱数据解算成像瞬间外方位元素的原理,以及行方位数据支持的线中心投影影像通过改进的共线方程方法几何校正的过程,实现了无地面控制点数据的直接几何校正。采用高光谱影像分块处理方法,实现了高吞吐快速几何校正。HySpex SWIR-384高光谱影像的实验结果表明,该文的校正算法可达到9.15m的绝对定位精度,最终利用少量地面控制点进行几何精校正,其均方根误差达到0.93m。     

一种去噪声的新方法

去噪声是进行辐射校正的重要手段,国内外已进行了大量的研究,开发了多种手段,对去除 图像上的一般噪声是切实有效的,但对于去除焦作矿区航空多光谱扫描图像上的噪声,用传统的 去噪声软件,效果很不理想,开发去噪声的新软件是非常必要的。结合航空多光谱扫描片上噪声 分布特征,开发的去噪声新软件功能全面,效果显着。     

惯导约束下的高光谱图像行相关几何校正算法

针对搭载于无人机的推扫式高光谱成像仪受气流影响显著,获取的高光谱图像几何畸变明显,无人机搭载POS精度相对较低,传统方法难以消除图像几何畸变等问题。该文提出了结合图像行相关和低精度GPS惯导数据校正的思路,首先进行行相关匹配完成图像行之间的相对平移量,然后经过反算修正POS的翻滚角数据,最后基于更新后的POS数据进行几何校正。该方法充分结合了行相关匹配和POS校正的优势,最大限度地消除了几何畸变,经过对无人机图像验证,比其他单一方法处理效果更为明显。     

合成孔径雷达图像辐值校正和几何校正的探讨

数字镶嵌是一种常用的数字图像处理技术。然而,用常规的数字镶嵌技术(利用地面控制点) 镶嵌合成孔径雷达图像并不理想。因为侧视雷达图像的畸变(几何和辐值)都与射束方向距离有 关。当相邻航带图像拼接时,图像色调和几何位置偏差较大。本文解决了侧视雷达图像数字镶嵌 中的问题；详述了雷达图像辐值校正和几何校正方法的数学理论；实例证明了校正方法是有效的。 通过对侧视雷达图像进行合适的辐值校正和几何校正后,就能获得色调均匀、位置准确的镶嵌图像。     

基本矩阵约束下的摄像机畸变校正及像主点确定

针对视觉测量系统中畸变校正过程烦琐、计算复杂等问题,提出了一种基于基本矩阵约束的镜头畸变自动校正及像主点坐标确定方法。基于对极几何的基本矩阵和一阶径向畸变模型构建了两视图同名点约束方程;为解决待求参数过多导致解不稳定的问题,采用分步求解策略分别求解基本矩阵及畸变参数和主点坐标,用RANSAC稳健估计方法求取基本矩阵,用迭代最小二乘优化求解畸变参数和主点坐标,两步交替进行。提出的算法仅使用两张图像即可获取径向畸变参数及主点坐标,可操作性强,且对噪声具有一定的鲁棒性,适用于自然场景图像的校正。     

多目标无人机微型凝视高光谱成像仪辐射校正

微型凝视高光谱成像仪可以同时获取两个空间维度和一个光谱维度图像，且仅记录整个高光谱图像的外方位元素而不是记录每一帧图像的外方位元素，避免了扫描时的几何不稳定性，有效解决了小型线扫式高光谱成像仪成像几何变形大的问题，适合于姿态不稳定小型无人机负载平台。目前，研究大多集中于线扫式高光谱成像仪辐射校正方法。凝视型高光谱成像仪应用时间较短，国内外没有较为成熟的数据处理研究，阻碍了无人机微型凝视高光谱成像系统的应用。本文研究了无人机载微型凝视高光谱成像仪辐射响应线性度特性和辐射响应变异性的校正方法，并定量评估该方法的有效性。结果表明，辐射响应变异性校正前，高光谱图像存在明显的渐晕效应和条带现象，校正后，不同波段中像元的辐射响应变异系数显著下降，且渐晕效应和图像条带明显减少。本文提出了基于多目标辐射定标方法，并通过比较定标后的高光谱图像光谱与地面光谱仪实测地物光谱来验证辐射定标的精度。结果表明，多目标辐射定标方法的定标结果表现出更好的效果，特别对于近红外波段，与光谱仪实测的地物反射率差异较小。     

新型航空扫描仪系列及其相关的技术系统

本文介绍了在“七五”期间研制的新型航空扫描仪系列以及配套的技术系统的情况。扫描仪的工作波长已覆盖了从紫外到热红外的遥感窗口,其光谱分辨率也有了长足的提高,并已形成了波段系列。飞机上与扫描仪配套的电子系统在多通道、高速传感、大容量和操作自动化等方面均取得了进展,能采集记录定量化遥感据数和图像预处理、几何校正配准等工作所需的平台位置及姿态等参数,并具备了尽可能灵活的配置和完善的功能;地面预处理系统则能提供快速的回放、处理服务,能提供规格化的信息产品-计算机兼容(CCT)磁带、软盘和图片;在利用平台参数作几何初校正之后,图像中像点的定位精度可达0.1′。     

应用遥感技术进行西藏高原当雄县牧区土地利用现状详查的方法研究

西藏自治区当雄县的土地利用现状调查工作已经完成。在调查过程中采用了经IRSA-2图像处理系统增强和几何精纠正处理的多季相陆地卫星MSS图像,以及黑白全色航空像片,建立了利用多时相陆地卫星MSS假彩色合成图像和航空像片进行土地利用调查的综合判读标志,完成了利用IBMPC/AT计算机进行土地面积量算数据处理的全部程序并解决了内业工作中的技术问题。 调查结果证明,在当雄县使用的遥感方法进行高原土地利用调查,较常规方法可取得更高的调查精度和经济效益。其方法的研究对于完成全藏调查工作具有重要的参考价值。     

Towards 3D map generation from digital aerial images

This paper describes the fusion of information extracted from multispectral digital aerial images for highly automatic 3D map generation. The proposed approach integrates spectral classification and 3D reconstruction techniques. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and near infrared (NIR) channels and form the basis for information extraction.Our land use classification is a 2-step approach that uses RGB and NIR images for an initial classification and the panchromatic images as well as a digital surface model (DSM) for a refined classification. The DSM is generated from the high resolution panchromatic images of a specific photo mission. Based on the aerial triangulation using area and feature-based points of interest the algorithms are able to generate a dense DSM by a dense image matching procedure. Afterwards a true ortho image for classification, panchromatic or color input images can be computed.In a last step specific layers for buildings and vegetation are generated and the classification is updated.     

Hypercomplex Quality Assessment of Multi/Hyperspectral Images

This letter presents a novel image quality index which extends the Universal Image Quality Index for monochrome images to multispectral and hyperspectral images through hypercomplex numbers. The proposed index is based on the computation of the hypercomplex correlation coefficient between the reference and tested images, which jointly measures spectral and spatial distortions. Experimental results, both from true and simulated images, are presented on spaceborne and airborne visible/infrared images. The results prove accurate measurements of inter- and intraband distortions even when anomalous pixel values are concentrated on few bands.     

结合多光谱影像降维与深度学习的城市单木树冠检测

多光谱数据的降维处理对基于深度学习的单木树冠检测研究有重要意义,如何使用合适的降维方法以提高单木检测的精度却少有研究讨论。本文使用无人机搭载多光谱相机进行航拍作业,采集研究区内银杏树种多光谱影像。将原始多光谱影像通过特征波段选择、特征提取、波段组合的方法生成5种不同的数据集用于训练3种经典的深度学习网络FPN-Faster-R-CNN,YOLOv3,Faster R-CNN。其中由波段组合方法得到的近红外、红色、绿色波段组合在不同类型的目标检测网络中都有最好的检测结果,其中FPN-Faster-R-CNN网络对银杏树冠的检测精度最高为88.4%,由OIF指标得到的蓝色、红色、近红外波段组合信息量最高,但在所有网络中的平均检测精度最低,仅为79.3%。实验结果表明：在不同波段降维方法中,若降维后的影像中目标物体的色彩与背景差异较明显,且轮廓清晰,则深度学习网络对树冠的检测可获得较好的结果。而影像自身的信息量则对深度学习网络的树冠检测能力的提升作用有限。本研究中针对多光谱影像的降维方法分析,为基于深度学习的单木树冠检测研究提供了重要的实验参考。     

Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B

China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS-2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images.     

Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping

Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, the inventory and characterization of wetland habitats are most often limited to small areas. This explains why the understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. While LiDAR data and multispectral Earth Observation (EO) images are often used separately to map wetland habitats, their combined use is currently being assessed for different habitat types. The aim of this study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data to precisely map the distribution of wetland habitats. The image classification was performed combining an object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5) and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately. Another classification was then applied integrating summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, microtopography and intensity return. The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classification accuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectral images. Moreover, this study highlights the advantage of integrating vegetation height, microtopography and intensity parameters in the classification process. This article demonstrates that information provided by the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment     

机载三维成像仪的定位原理与误差分析

本文论述了“机载三维成像仪”的定位原理,并在系统定位原理的基础上,详细讨论了与“机载三维成像仪”的对地定位精度有关的传感器的误差对系统定位精度的影响,这一问题的研究不仅对研制针对不同目的的激光地形制图系统的设计具有指导意义,而且对激光扫描制图系统的数据平差具有重要意义。     

Comparison between four methods for data fusion of ETM+ multispectral and pan images

Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation (SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat—7 ETM+ multispectral bands with ETM+ panchromatic band. Each of them improves the spatial resolution effectively but distorts the original spectral signatures to some extent. SFIM model can produce optimal fusion data with respect to preservation of spectral integrity. However, it results the most blurred and noisy image if the coregistration between the multispectral and pan images is not accurate enough. The spectral integrity for all methods is preserved better if the original multispectral images are within the spectral range of ETM+ pan image.     

Comparison Between Four Methods for Data Fusion of ETM＋ Multispectral and Pan Images

Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation(SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat--7 ETMq- multispectral bands with ETM panchromatic band. Each of them improves the spatial resolution effectively but distorts the original spectral signatures to some extent. SFIM model can produce optimal fusion data with respect to preservation of spectral integrity. However, it results the most blurred and noisy image if the coregistration between the multispectral and pan images is not accurate enough. The spectral integrity for all methods is preserved better if the original multispectral images are within the spectral range of ETM pan image.     

A novel image-fusion method based on the un-mixing of mixed MS sub-pixels regarding high-resolution DSM

A major reason for the spectral distortions of fused images generated by current image-fusion methods is that the fused versions of mixed multispectral (MS) sub-pixels (MSPs) corresponding to panchromatic (PAN) pure pixels remain mixed. The MSPs can be un-mixed spectrally to pure pixels having the same land cover classes in a fine classification map during the fusion process. Since it is difficult to produce such a land cover classification map using only MS and PAN images, a Digital Surface Model (DSM) derived from airborne Light Detection And Ranging data were employed in this study to facilitate the classification. In a novel fusion method proposed in this paper, MSPs near and across boundaries between vegetation and non-vegetation are identified using MS, PAN, and normalized Digital Surface Model (nDSM). The identified MSPs then are fused to pure pixels with respect to the corresponding land cover class in the classification map. In a test on WorldView-2 images over an urban area and the corresponding nDSM, the fused image generated by the proposed method was visually and quantitatively compared with fused images obtained using common image-fusion methods. The fused images generated by the proposed method yielded minimal spectral distortions and sharpened boundaries between vegetation and non-vegetation.     

基于分辨率退化模型的全色和多光谱遥感影像融合方法

从影像成像的频率特性出发，提出了一种影像分辨率退化模型，并在此基础上提出了一种新的全色和多光谱遥感影像融合方法。