基于地表参数真实性的GF-1和SPOT-7多光谱影像质量评价

目的 遥感影像中地表信息表达真实程度决定了影像信息提取和定量化应用水平,传统的从像素灰度和视觉特性角度的影像质量评价方法难以评价影像对地表信息表达能力,本文从地表反射率和NDVI（normalized difference vegetation index）两种地表参数真实性角度评价GF-1和SPOT-7多光谱影像质量。方法 提出了一种基于地表参数真实性的多光谱影像质量评价方法,完成GF-1和SPOT-7卫星对实验区同步成像,地面同步测量大气光学特性和典型地物样区光谱,获取同步观测数据并对多光谱影像进行辐射误差处理,计算地物样区在影像上的反射率和NDVI,通过与地面实测光谱数据比较分析了地表参数真实性,评价GF-1和SPOT-7多光谱影像质量。结果 人工靶标中GF-1影像在4个波段反射率误差均在5%内,精度优于SPOT-7;植被地物中SPOT-7影像在蓝绿红波段反射率误差在4%内,近红外波段误差在15%内,NDVI误差在16%内,反射率和NDVI精度均优于GF-1;硬地地物中GF-1影像在4个波段反射率误差在6%内,精度优于SPOT-7;评价结果表明SPOT-7多光谱影像对植被类地物光谱表达真实度更高,GF-1对硬地类地物光谱表达真实度更高。结论 提出的基于地表参数真实性的遥感影像质量评价方法,能够有效地从地物光谱信息表达精度的角度评价影像质量。     

基于a’trous小波与广义HIS变换的SAR与多光谱影像融合

为了提高SAR影像的解译水平,避免通常基于小波变换的融合方法造成的SAR影像信息损失,本文提出一种基于a’trous小波与广义HIS变换的SAR与多光谱影像融合方法,在将多光谱影像转换到HIS空间后,应用a’trous小波对I分量进行分解,通过加法的形式将多光谱影像的高频分量信息与SAR影像信息集成,并根据解译的需要,通过改变阈值来控制对多光谱影像信息的集成幅度。实验选取一组TM多光谱影像与ERS-2SAR影像进行融合研究,并将融合结果与另一小波融合方法融合结果进行视觉比较与统计分析。结果表明,另一小波融合方法的融合结果与本文方法融合结果阈值τ=1时的结果接近,而本文方法却可以根据不同的应用需要,在完整保留了SAR影像信息的基础上,通过调节多光谱影像信息的注入程度,为获取更能满足解译需要的SAR融合影像提供更多选择,拥有更好的鲁棒性。     

基于影像融合和面向对象技术的植被信息提取研究

高分辨率影像具有丰富的光谱信息和空间信息。采用不同的图像融合技术融合GeoEye影像全色波段和多光谱波段,用建立的参考多边形和对应多边形残差法评价分割质量,以确定研究区各地物类型的最优分割参数组合,选择目标地物分类特征,建立分类规则,在此基础上实现研究区内不同地物类型的面向对象信息提取。结果表明:Gram-Schmidt(GS)融合法具有最优的融合效果,所选特征能够很好地实现目标地物信息提取,并且具有明确的地学意义,面向对象信息提取总体精度达到90.3%,Kappa系数为0.86,该研究为高精度植被信息的提取提供了有效的方法。     

集成特征分量的阴影多尺度分割方法

针对高分影像阴影检测精度易受水体、深色地物和暗色植被影响等问题,结合GF-1影像自身特点,提出一种结合特征分量构建和多尺度分割面向对象的阴影检测方法。首先,对GF-1影像多光谱数据、全色数据进行正射校正和信息融合,以达到光谱与空间分辨率信息最大化利用。其次,集成特征分量(主成分第一分量PC1、亮度分量V、绿光波段G、归一化植被指数NDVI)以增强阴影信息。最后,对集成后的影像进行多尺度分割,并利用特征分量构建规则集,最终实现阴影信息提取。实验表明,该方法既能准确地检测出GF-1影像中的阴影信息,又能有效削弱水体、深色地物和暗色植被的影响。     

辅以纹理特征的面向对象的遥感影像分类方法研究——以陕西省杨陵县为例

随着遥感图像分辨率的不断提高,图像的纹理特征更加明显,使得对图像进行纹理分析成为可能。以陕西省杨陵县为试验区,将TM多光谱影像和SPOT 5高分辨率全色影像进行融合,先对影像各种纹理特征进行比较,选用对比度这一属性辅助进行面向对象的分割,同时结合地物的光谱信息,对该地区进行监督分类。与单纯基于光谱特征的分类方法进行比较,该方法在一定程度上改善了分类精度,细化了地物类别。     

基于纹理的雷达与多光谱遥感数据小波融合研究

众所周知,多光谱与雷达影像融合具有重要的意义,但雷达影像小尺度的纹理特征在先前的融合方法中却没有被考虑。为了更好地对多光谱与雷达影像进行融合,基于双正交小波变换,提出了一种小尺度纹理影像参与融合的三影像小波融合方法。该方法借鉴多通道滤波及基于亮度调节的平滑滤波(SFIM)融合的原理,首先提取多时相雷达影像的小尺度纹理数据;然后再将该纹理数据、单时相JERS-1SAR数据及TM多光谱数据进行小波融合。分析表明,该方法的融合结果较雷达与多光谱影像小波融合的结果不仅具有更丰富的光谱特征,而且由于继承了雷达影像丰富的小尺度纹理特征,因而具有更高的清晰度。实验证明,该方法可获得较好的融合结果,是一种切实有效的融合法。     

辅以波谱分析的高分辨率影像面向对象分类研究

随着遥感影像空间分辨率的提高,地物的空间信息更加丰富,地物尺寸、 形状以及相邻地物的关系得到更好的反映,因此目前高分辨率影像分类方法更侧重于利用地 物的空间信息,分类过程中参与较多的人为主观因素,在地物类型未知的地区很难进行解译 工作。另外,分割过于细碎导致操作数据量太大也是高分辨率影像分类的难题之一。论文提 出了辅以波普分析的高分辨率影像面向对象分类方法,即在传统面向对象分类方法的基础上 结合影像波谱分析,先对影像光谱角制图粗分类、掩膜操作,再面向对象精分类,较好解决 了以往面向对象分类方法地物类型的不确定性和分割细碎等问题。试验以空间分辨率为0.5 米的八波段WorldView2 影像为研究数据提取西部那曲地区道路和河流,精度达到96.36%。     

高分一号与Landsat-8影像在荒漠绿洲过渡带应用对比

针对国产高分一号卫星(GF-1)成像质量是否可以满足区域生态环境监测需求的问题,开展了宽幅多光谱相机(wide field view,WFV)在荒漠绿洲过渡带的成像质量评估研究。从辐射质量、纹理、地类识别精度和归一化植被指数等方面构建评估指标,定量分析了GF-1 WFV和Landsat-8OLI在荒漠绿洲过渡带的成像质量差异。结果表明:GF-1 WFV影像虽然具有较高的空间分辨率,但在辐射质量、地类识别效果、纹理信息及植被指数等方面与Landsat-8OLI相比有一定差距;GF-1 WFV影像的信噪比优势明显,对噪声的抑制效果较好;通过与纹理信息的波段组合,可以有效提高GF-1WFV影像的地物识别效果,缩小与Landsat-8OLI在分类精度上的差距;鉴于明显的光谱范围差异,二者归一化植被指数数据在协同应用的过程中宜分地物类型转换,在西北荒漠绿洲过渡带的国土资源调查、城市规划、农情监测等方面可发挥积极作用。     

深度学习U-Net方法及其在高分辨卫星影像分类中的应用

高分辨率遥感影像有精确的几何结构和空间布局,但是光谱信息有限,增大了对光谱特征相似地物的分类难度。针对高分辨率遥感影像分类的问题,采用深度学习U-Net模型分类方法。基于黑河下游额济纳绿洲高分二号遥感影像,通过U-Net模型提取胡杨、柽柳、耕地、草地和裸地五种地物覆被类型,分类总体精度和Kappa系数分别为85.024%和0.795 6,并与传统的支持向量机（SVM, Support Vector Machine）和面向对象的分类方法比较,结果表明：相对于SVM和面向对象,基于U-Net模型的高分辨率卫星影像地物覆被分类,能够更好地对地物本质特征进行提取,分类效果较好,满足精度要求。     

基于LightGBM和多光谱—SAR多特征综合的中亚干旱区城市不透水面提取

不透水面是评价城市化水平和城市生态环境的重要指标,是近年来城市遥感研究中的热点方向之一。与湿润、半湿润区相比,干旱区城市植被覆盖度较低,不透水面与裸土、荒漠之间相似的光谱特征导致传统基于光学影像的亚像元分解法与光谱指数法在干旱区不透水面提取的适用性降低。针对该问题,提出一种多光谱与合成孔径雷达（SAR）影像多特征综合的方法以增大不透水面与其他地物覆盖类型之间的特征差异,从而提取干旱区城市不透水面。以阿斯塔纳、塔什干和杜尚别3个中亚城市为研究区,哨兵2号和哨兵1号影像为数据源,通过LightGBM算法对多光谱和SAR图像的空间特征、SAR的极化特征进行分类并提取不透水面。研究对比了不同特征组合以及不同分类方法的不透水面提取结果,实验结果表明：多光谱与SAR影像多特征综合的方法能有效提高干旱区不透水面提取精度,明显改善干旱区其他土地覆盖类型错分为不透水面的问题;LightGBM算法与XGBoost、HistGBT等基于梯度提升决策树的算法和随机森林等方法相比能获取更高的精度,更适用于干旱区不透水面提取。这表明基于LightGBM以及多光谱和SAR多特征联合的方法能够有效提取中亚干旱区城市不...     

Identification of Typical Land Features in the Lijiang River Basin with Fusion Optics and Radar

Lijiang River is the core of Guilin's landscape. Protecting the ecological environment of Lijiang River Basin has become a national strategy. In this paper, Lijiang River Basin was used as the research area. The GF-1 multispectral image and SAR image were used as the data source. The wavelet fusion algorithm was used to fuse the GF-1 multispectral image and the SAR VV polarized backscatter image. Using random forest algorithm to construct a high-precision recognition model for GF-1 multispectral imagery, GF-1 and sentinel fusion images. The model can extract rivers, coniferous forests, broad-leaved forests, paddy fields, drylands, residential land and other land types that are closely related to the ecological environment of the Lijiang River. The results show that ①the overall accuracy based on GF-1 image classification reaches 96.15% in the 95% confidence interval, and the overall accuracy based on GF-1 and sentinel-1A backscatter coefficient reaches 94.40%. ②The classification accuracy of rivers, broad-leaved forests and drylands based on GF-1 multispectral images reached 97.74%, 93.20%, and 90.90%. They are 7.57%, 8.96%, and 1.22% higher than those based on the fused GF-1 multispectral and SAR data, respectively. The classification accuracy of the other features is similar. ③In the fusion of GF-1 multispectral and SAR data, wavelet transform was used for image fusion. It was found that the karst topography of the fusion image was prominent, which increased the difference of the features of the ground features.     

基于多卷积神经网络融合的SAR舰船分类

针对SAR图像中小型舰船分类准确率较低的问题,提出一种多卷积神经网络加权融合的方法。首先构建高分辨率卷积神经网络对特征图进行多尺度融合,引入微调模型和标签平滑减少训练过拟合的问题;然后利用高分辨网络、MobileNetv2网络和SqueezeNet网络训练3种单分类模型;最后采用加权投票方式对3种分类模型的结果进行融合。采用融合算法对GF-3号舰船数据集进行分类实验,取得94.83%的准确率、95.43%的召回率和0.9513的F1分数的分类性能。实验结果表明,该舰船分类算法模型具有较优的分类能力,验证了其在高分辨率SAR图像舰船分类上的有效性。     

Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data

Land‐cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land‐cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet‐merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey‐level co‐occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum‐likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land‐cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8–6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land‐cover classification accuracies.     

Land-cover classification using multi-temporal GF-1 wide field view data

ABSTRACT

Vegetation is an important land-cover type and its growth characteristics have potential for improving land-cover classification accuracy using remote-sensing data. However, due to lack of suitable remote-sensing data, temporal features are difficult to acquire for high spatial resolution land-cover classification. Several studies have extracted temporal features by fusing time-series Moderate Resolution Imaging Spectroradiometer data and Landsat data. Nevertheless, this method needs assumption of no land-cover change occurring during the period of blended data and the fusion results also present certain errors influencing temporal features extraction. Therefore, time-series high spatial resolution data from a single sensor are ideal for land-cover classification using temporal features. The Chinese GF-1 satellite wide field view (WFV) sensor has realized the ability of acquiring multispectral data with decametric spatial resolution, high temporal resolution and wide coverage, which contain abundant temporal information for improving land-cover classification accuracy. Therefore, it is of important significance to investigate the performance of GF-1 WFV data on land-cover classification. Time-series GF-1 WFV data covering the vegetation growth period were collected and temporal features reflecting the dynamic change characteristics of ground-objects were extracted. Then, Support Vector Machine classifier was used to land-cover classification based on the spectral features and their combination with temporal features. The validation results indicated that temporal features could effectively reflect the growth characteristics of different vegetation and finally improved classification accuracy of approximately 7%, reaching 92.89% with vegetation type identification accuracy greatly improved. The study confirmed that GF-1 WFV data had good performances on land-cover classification, which could provide reliable high spatial resolution land-cover data for related applications.     

基于独立分量分析和支持向量机的遥感影像融合分类算法

遥感影像分类是遥感定量化分析的重要手段,遥感影像融合是提高分类正确率的有效途径之一。本文提出一种遥感影像的融合分类算法。首先采用Contourlet变换对多光谱影像和全色影像进行融合,然后结合独立分量分析的去相关性、稀疏特性以及很好地捕捉影像重要边缘信息、纹理信息的能力,提取融合影像的独立分量特征,并用支持向量机实现分类。与其他算法的主、客观比较结果表明,该算法的实验效果较好,可有效地提高遥感影像的分类精度。     

基于HSV和红黑小波变换的多光谱图像融合

提出一种基于HSV和红黑小波变换的多光谱图像融合方法。对多光谱图像进行HSV变换,将得到的明度分量和全色图像做多尺度红黑不可分小波分解。采用不同的融合算子对高低频分量进行融合,对融合后的图像进行红黑重构和HSV逆变换得到融合结果,并采用客观性能指标对融合结果进行评价。实验结果表明,该方法对多光谱图像和高空间分辨率图像有较好的融合效果。     

复小波包域遥感图像局部自适应融合算法

为了使融合后的图像在保持原IKONOS 卫星图像多光谱特性的同时,最大可能地提高图像空间 分辨率,提出了一种基于四树复小波包变换的SAR 图像与多光谱IKONOS 卫星图像相融合的新方法．该方法 利用复小波包变换的多方向性和对高频细节信号良好的时频局部化分析能力,分别对IKONOS 图像经HIS 空 间变化的I 分量子图和SAR 图像进行复小波包分解,并对分解后的低频复系数采用取平均或取大值的方法、 对高频方向复系数采用邻域一致性测度的局部自适应方法进行复系数融合．用融合后复系数经复小波包反变 化得到的图像代替原IKONOS 图像经HIS 变换的I 分量,再经HIS 空间反变换得到最终的融合图像．实验结 果表明,该融合算法在光谱保留和空间质量提高方面,比传统的基于小波变换的融合算法具有更高的性能．     

U-Net Neural Networks and Its Application in High Resolution Satellite Image Classification

High-resolution remote sensing images have precise geometric structure and spatial layout, but the spectral information is limited, which increases the difficulty of classifying similar features of spectral features. Aiming at the problem of high resolution remote sensing image classification, a U-Net convolutional neural network classification method based on deep learning is proposed. Based on the remote sensing image of the Ejina Oasis GF-2 in the lower reaches of the Heihe River, the U-Net model was used to extract the five types of land cover types of Populus euphratica, Tamarix chinensis, cultivated land, grassland and bare land. The overall classification accuracy and Kappa coefficient were 85.024% and 0.795 6 respectively. Compared with the traditional Support Vector Machine(SVM) and object-oriented method, the results show that compared with SVM and object-oriented method, the U-Net model is used to classify the high-resolution remote sensing, and the classification effect is better. The ground extracts the essential features of the features to meet the accuracy requirements.