资源三号卫星姿态检校方法

针对高分辨率卫星遥感影像姿态数据中含有误差、致使其直接定位精度较低的问题,验证了资源三号影像姿态系统误差的存在,对比分析了姿态四元数系统误差补偿模型和姿态角系统误差补偿模型对提高资源三号对地目标定位精度的能力。通过对资源三号卫星遥感影像的试验,利用不同数量控制点求解姿态四元数系统误差和姿态角系统误差,对姿态进行改正。实验表明,利用改正后的姿态进行定位,可以大幅度提高资源三号卫星对地目标定位精度。     

一种资源三号卫星影像的云量判读方法与应用

鉴于遥感影像上云的存在是影响影像质量判定的一个重要因素,对下垫面地物的识别与分类造成困难,降低了影像的利用率,分析了不同类型的云斑在资源三号卫星影像上的光谱亮度特征与直方图曲线分布特征,使用灰度共生矩阵提取云的纹理特征,依次构建云的光谱特征和纹理特征的判别准则,采用决策树分类法识别检测含云量,提出了一种针对资源三号卫星影像云量判读的算法。实验结果表明,该算法能够定性定量地判读出资源三号卫星影像上不同类型的云含量,正确率高达96%以上,具有效果好且速度快的特点。     

高分三号卫星影像在黄河洪水监测中的应用

高分三号（GF-3）卫星是我国首颗C频段多极化高分辨率合成孔径雷达（synthetic aperture radar,SAR）卫星,尝试利用高分三号（GF-3）卫星对黄河2017年第1号洪水开展遥感监测工作,获取了7月28日18时42分和7月29日6时52分的黄河小北干流河段遥感影像。经过影像处理和遥感解译,高分三号（GF-3）卫星影像清晰,层次分明,对河流水体、沙洲、以及河道整治工程、桥梁等反映明显,能够较好地识别洪水淹没范围信息。应用表明,高分三号（GF-3）卫星具有良好的编程成像能力,可以全天候全天时获取洪水遥感影像,在洪涝灾害监测和防灾减灾领域具有较大应用潜力。     

一种卫星遥感影像村庄区域提取算法

卫星遥感影像提取村庄区域在地理和气象领域均有十分重要的意义.针对卫星遥感影像的特点,提出了一种村庄区域提取方法.利用改进的去雾算法对卫星遥感影像进行预处理,通过遥感卫星影像的颜色特征实现分割,结合村庄区域分布特点进行去噪处理,实现卫星遥感影像村庄区域的提取.实验结果表明:该算法能够对卫星遥感图像中不同类型村庄区域进行提取,且提取准确率高,可以应用于地理以及气象等领域.     

资源三号卫星在汶川震区滑坡快速识别中的应用方法研究

针对当前采用野外人工调查和遥感目视解译获取地震灾区滑坡信息存在主观性强、耗时费力等缺点。以汶川震区为实验区,应用资源三号(ZY-3)卫星影像,采用多尺度最优分割方法构建多层次识别对象,融合光谱、纹理、几何等影像特征和地形特征建立多维滑坡识别规则集合,以及基于高分辨率影像认知模式与场景理解过程提出滑坡分层识别模型,从而实现滑坡空间分布及其滑源区、滑移区和堆积区的快速识别。通过实验结果发现最低识别精度为82.97%,而滑坡的堆积区最容易识别。研究结果验证了该方法及资源三号卫星数据快速识别地震滑坡信息的有效性,可为震区滑坡调查提供技术支持,并可促进国产卫星的推广应用。     

遥感影像反卷积复原处理

在遥感影像成像过程中,PSF是传感器和影像获取时刻的大气特性的共同作用结果,但是遥感影像中通常不具有明显的较理想点状结构来近似估计点扩展函数(PSF).本文讨论了一种基于遥感影像典型线状地物阶跃边界的PSF估计方法,并采用频域-小波域混合正则化ForWaRD算法对其进行复原处理.对SPOT及中巴资源卫星1号影像数据的实际处理结果表明,该方法能很好地改善影像质量,且计算量不大,具有较强的鲁棒性.     

资源三号卫星在地质灾害调查中的应用对比——以宝鸡黄土区为例

针对我国地质灾害遥感调查工作对国产卫星数据使用不足的现象,该文以宝鸡黄土区为试验区,通过测试国产资源三号卫星(简称ZY-3)与国外卫星应用于地质灾害遥感调查时表现出的数据质量、成图比例尺精度、灾害体解译效果以及灾害体解译精度等指标要素,客观评价资源三号卫星数据的优略性,为今后国产卫星在地质灾害中的使用与推广提供有效依据。结果表明:国产ZY-3卫星数据质量主客观评价结果基本一致,数据质量为良;灾害体解译的最大成图比例尺可达1∶25 000,能够有效保障黄土区大、中型灾害体的遥感精确识别。同时,1∶25 000比例尺下的灾害体目视解译中,资源三号数据的解译总体精度可达74.6%,而国外数据(WorldView-2与QuickBird-2镶嵌影像)解译总体精度为79.4%,表明国产数据基本可替代国外数据。但是,在1∶1万比例尺度,ZY-3数据在清晰度、色彩、对比度方面仍然劣于国外WorldView-2数据,融合结果的定量分析比较也表明WorldView-2在客观指标上优于资源三号。作为自主发射的国产高分卫星,资源三号卫星实时数据充足,数据性价比高,是今后开展中、小比例尺地质灾害遥感调查的理想数据之一。     

高分3号卫星影像在黄河洪水监测中的应用

高分3号卫星是我国首颗C频段多极化高分辨率合成孔径雷达卫星,尝试利用高分3号卫星对黄河2017年第1号洪水开展遥感监测工作,获取7月28日18时42分和29日6时52分的黄河小北干流河段遥感影像。经过影像处理和遥感解译,高分3号卫星影像清晰,层次分明,对河流水体、沙洲,以及河道整治工程、桥梁等反映明显,能够较好地识别洪水淹没范围信息。应用表明,高分3号卫星具有良好的编程成像能力,可以全天候、全天时获取洪水遥感影像,在洪涝灾害监测和防灾减灾领域具有较大应用潜力。     

利用卫星遥感数据提取生态干扰信息的方法研究

利用卫星遥感数据获取具有高时空分辨率的生态干扰信息,是生态保护规划与自然资源管理的重要需求。本文通过系统研究国内外相关进展,总结提出了利用卫星遥感数据提取生态干扰信息的三种方法。详细阐述了基于高分一号、Landsat 和 MODIS (Moderate Resolution Imaging Spectroradiometer) 等三种遥感影像的生态干扰信息提取方法,并讨论了不同方法的适用性和局限性。     

多重观测卫星影像的无控区域网平差

为了保证卫星遥感影像获取地面目标区域信息的准确性,需要对影像数据进行几何校正,几何校正的精度决定着遥感图像应用的效果。常规的校正方法需要地面控制点信息,但在境外、我国西部、荒漠等这些地方很难获取控制点。针对上述问题,提出了多重观测卫星影像的无控区域网平差方法,提高无控定位的精度。与常规区域网平差不同,该方法的误差方程基于"单物方—多像方"的连接点集建立,在无控条件下误差方程能够收敛到更加精确的解。通过误差补偿模型调整每幅影像的有理函数模型系数,使得不同方向的定位误差进行抵消,从而在无控制点的条件下提高遥感影像的定位精度。首先,利用卫星影像的RPC文件和像方误差补偿模型,建立多重重叠的区域网平差模型,然后,利用共轭梯度算法迭代求解误差方程,最后调整RPC系数,提高遥感图像定位精度。通过资源三号卫星影像试验表明:多重观测区域网平差将遥感图像的平面定位精度由19.8m提高至12.9m,能够有效提高影像的几何定位精度。     

资源一号02D可见近红外和高光谱影像辐射质量评价

资源一号02D(ZY1-02D)卫星搭载了我国自主研制的可见近红外相机（VNIC）和高光谱相机（AHSI）,是我国首颗民用高光谱业务卫星,具有广泛的应用前景。通过整体辐射精度、信噪比、清晰度以及信息熵4个评价指标,对ZY1-02D VNIC和AHSI数据进行辐射质量评价,并分别采用Sentinel-2 MSI和GF-5 AHSI数据进行对比。结果表明：ZY1-02D VNIC数据在可见光波段具有亮度高、信噪比高等优势;在红边近红外等波段,影像具有灰度范围大、信息量大的特点。ZY-1-02D VNIC数据在影像亮度、灰度范围、清晰度和信息量方面均优于Sentinel-2,二者信噪比近似。ZY-1-02D AHSI数据在395—1 341 nm范围内辐射质量良好;在1 929—2 501 nm范围,存在噪声严重的波段,影像质量较差。与GF-5 AHSI数据对比,ZY-1-02D AHSI数据的影像亮度和信噪比相当,但ZY-1-02D AHSI数据在灰度范围方面优势明显,且短波红外谱段的清晰度和信息量优于GF-5 AHSI数据。     

资源三号卫星在轨MTF测量与图像复原

随着高分辨率遥感卫星发射数量的增多,高分辨率遥感图像数据应用需求也日益增强,而遥感数据的质量直接影响到其数据应用的可靠性和精确性。为此,对高分辨率遥感图像数据进行图像复原是非常有必要的。针对资源三号卫星电荷耦合装置相机遥感图像高空间分辨率的特点,分析在仅有图像信息的情况下系统调制传递函数（MTF）的在轨测量方法,验证双边缘法的可行性。探讨双边缘线状地物选取方法,同时推导证明二维MTF矩阵的构建方法,并借助二维MTF矩阵对原图像进行修正反转滤波实现图像复原。试验结果表明,复原后的图像质量明显提高。     

Study on Mangrove Species Classification based on ZY-3 Image

A hybrid mangrove forest extraction and species classification model for large coastal region was proposed using a ZY-3 (ZiYuan-3) image in the eastern part of Beibu Gulf (located at the junction of Guangdong and Guangxi).Firstly,the coastline was extracted according to the spectral features of ZY-3 image.Secondly,the buffer zone along with the coastline was generated as the suitable area of mangrove distribution.Mangrove forests and non-mangrove areas were then further classified using threshold method based on object-based image classification in these areas.Finally,Mangrove forests were classified at specie level using three pixel-based supervised classification methods,k-Nearest Neighbor,Bayes,and Random Forest.The classification results and accuracies were also compared and discussed.The results indicated that object-based threshold method can extract the coastline accurately and map the mangrove forests effectively.The pixel-based random forest classifier can obtain satisfactory results (the overall accuracy of 82.24%) of mangrove species classification than the other classifiers.In summary,the hybrid mode proposed in this paper is suitable for mangrove forests mapping and species classification in a large region.It is also validated the feasibility application of ZY-3 image in coastal mangrove research.     

Detection of attitude constant error and in-orbit calibration for the three-line CCD sensor of the ZY-3 satellite

In-orbit calibration of satellite optical sensors is of crucial importance for improving the accuracy and stability of high-resolution satellite stereo mapping and positioning. For the in-orbit calibration of Ziyuan 3 (ZY-3), the first Chinese three-line stereo mapping satellite, a rigorous imaging geometric model of the three-line sensors is established and the internal systematic error is modelled during analysis of the satellite structure and charge-coupled device (CCD) array. To improve the accuracy and stability of the in-orbit calibration, a new trajectory model, piece-point with weight polynomial model, is used and compared with models in current use. In addition, an attitude constant error model is constructed and applied to reduce attitude errors and provide a more accurate initial attitude value, thus increasing the accuracy and stability of the in-orbit calibration. In addition, ground control point (GCP) stripes, established by partitioning GCPs into stripes, are proposed and used to eliminate random errors during in-orbit calibration. Finally, in-orbit calibration is performed for single and multiple sensors and the result of the calibration is assessed using GCPs and checkpoints. Based on the calibration result, variations of the three-line CCD array on ZY-3 are calculated and analysed in the along- and cross-track directions.     

Research on Wenchuan Earthquake-induced LandslidesRapid Recognition from ZY-3 Imagery

Presently,regional earthquake-induced landslides is mainly obtained by field survey and visual interpretation from remote sensing images; but these methods are objective,and time-consuming.In this study,with a main data source of domestic high-resolution remote sensing images from ZY-3 satellite as well as the study area of the Wenchuan earthquake region,objects of multilevel landslides were established using the multi-scale optimum partition method based on in-depth analysis of landslide features.A recognition rule set of multi-dimensional landslides was also built through the combination of topographic features and image features,such as spectrum,texture,and geometry.Additionally,recognition models for landslide stratification were proposed based on the recognition models of high-resolution images and an understanding of the scenes.Through all of the aforementioned efforts,the spatial distribution of the seismic landslide as well as the sliding source area,transport area,and depositional area can be identified intelligently.The analysis results of the experimental area showed a minimum recognition accuracy of 82.97%,with the depositional zone of landslides being the easiest zone to recognize,and the effectiveness of the proposed method as well as ZY-3 data.These findings may provide technical support for regional earthquake-induced landslides investigations and further promote geological hazard application of domestic high-resolution satellites.      

结合稀疏识别的自适应Wallis滤波在高分辨率影像控制点匹配中的应用

目的：随着国内遥感卫星的迅速发展,卫星图像的图幅越来越大,分辨率越来越高。在轨遥感图像的几何精度评价,要求从待评遥感图像和多源参考图像之间精确地提取出分布均匀的控制点信息。使用Wallis滤波对高分辨率影像进行增强时,会产生过增强和饱和现象,影响了控制点提取效果。为了克服上述缺陷,提出了一种基于稀疏识别的自适应Wallis图像增强算法。方法：首先计算图像子区域的辐射质量参数并构建分类特征;然后通过稀疏识别算法确定子区域的地物类型;最后根据子区域所属地物类型,选择不同的Wallis滤波参数,实现整幅图像的自适应增强,并在增强的遥感图像上提取控制点信息,实现遥感图像的几何精度自动化评价。结果：针对资源三号影像的实验结果表明,针对不同的子区域地物类型进行自适应Wallis增强,有效防止了基于全局统一参数的Wallis滤波带来的过增强和饱和现象,有效增强了高分辨率图像的纹理。结论：本文提出来一种新的高分辨率遥感影像增强策略,增强了高分辨率图像的纹理,提高了控制点的获取数目和准确率。     

Selection and Evaluation of the Optimal Scale in Multi-scale Segmentation of Remote Sensing Images

Multi-scale segmentation is the premise and key step of Object-Based Image Analysis (OBIA). The quality of multi-scale segmentation directly affects the accuracy of object-oriented classification. However, scale selection and evaluation remains a challenge in multi-scale segmentation. According to the fact that the optimal segmentation scale of the remote sensing image is closely related to the complexity of the objects of the image, a top-down method to select the optimal scale based on the complexity of segmented objects is proposed. In the top-down segmentation process, image features of each segmented object are extracted to construct the complexity function, and the optimal scale of each object is determined by setting a threshold value and iterating calculation. Then, the segmentation results with the best scale are obtained and applied to the ZY-3 satellite multispectral image and the GF-2 fusion image to obtain segmentation and classification results. Qualitative visual evaluation method, unsupervised evaluation method and supervised classification evaluation method were used to compare them with results obtained by the optimal single-scale segmentation and the unsupervised evaluation method. The experimental results show that the method can accurately obtain the scale matching with the ground targets, and improve segmentation effect and the classification accuracy, it is of practical value.     

遥感影像多尺度分割中最优尺度的选取及评价

多尺度分割是面向对象图像分析技术的前提和关键,多尺度分割的质量直接影响着面向对象分类的精度,但尺度选择仍然是多尺度分割中的一个难题。针对此问题,根据遥感影像的最优分割尺度与影像上目标复杂度密切相关的事实,提出了一种自上而下基于分割对象复杂度选取最优尺度的方法。该方法在分割过程中,提取每一对象的影像特征构建其复杂度函数,通过设置阈值,经迭代计算来确定每一对象的最优分割尺度,进而得到具有全局最优尺度的分割结果,并将其应用于ZY-3多光谱数据和GF-2融合影像,得到分割和分类结果。并将其与单一最优尺度和非监督评价法的分割及分类结果进行比较,结果表明：该方法能够获取与地面目标相匹配的分割尺度,改善了分割效果,提高了分类精度,具有一定实用价值。     

Destriping high-resolution satellite imagery by improved moment matching

High-resolution satellite imagery is frequently contaminated with vertical and non-periodic stripes, because of the differential sensitivities of the detector elements to incoming radiation, and the imaging mechanism of the pushbroom-type instruments. The high spatial resolution also implies more structural details and limitations in the spectral range and the number of bands. In this article, focusing on these factors, an improved moment-matching method is proposed for the automatic destriping of high-resolution satellite imagery. Two novel approaches – Gaussian mixture model based grey slicing and an adaptive local window – are specifically designed to facilitate the homogeneity of the local image. In this way, the limitation of moment matching is eliminated. The stripes are suppressed and serious artefacts are avoided, while the radiation characteristics and structural details are preserved. The performance of the proposed approach was evaluated and compared both visually and quantitatively, employing Ziyuan-1-02C of China (ZY-1-02C) and Shijian-9A of China (SJ-9A) panchromatic images. The experimental results confirm the superiority of the proposed approach in suppressing stripes while preventing unwanted artefacts and structural distortion.     

基于形态学建筑物指数的城市建筑物提取及其高度估算

通过引入高分辨率影像的形态学建筑物指数和阴影指数,并结合面向对象的地物信息提取思想,准确地提取出城市建筑物及其阴影,进而实现了城市建筑物的高度估算。首先,利用形态学建筑物指数的多方向多尺度特征,将建筑物与邻近光谱相似的道路目标进行分离;其次,采用双阈值策略提取建筑物与相应的阴影,进一步提高了建筑物的提取精度;最后,根据成像时刻卫星和太阳的高度角、方位角,建立建筑物阴影长度与建筑物高度的估算模型。试验利用厦门市思明区软件园资源三号(ZY\|3)数据进行城市建筑物提取及其高度估算,证实该方法能够较准确地估算出建筑物的高度信息,并且比基于SVM的监督分类方法具有更高的建筑物提取精度,建筑物高度估算的中误差可达±1 m。