利用SVM与灰度共生矩阵从QuickBird影像中提取枇杷信息

以福建省莆田市东圳水库库区为例,采用QuickBird卫星影像,利用主成分分析方法对灰度共生矩阵方法提取的地物纹理特征进行筛选,选择最佳的影像纹理特征,组成新的波段组合,并应用支持向量机方法(Support Vector Machine,SVM)进行枇杷树的提取分类,最后与只依靠光谱信息来分类的SVM法分类结果进行比较,其分类总精度由原来的71.33%提高到了86.67%,Kappa系数也由原来的0.6410提高到了0.8293,分类精度明显提高,表明光谱信息加入纹理特征信息能辅助并提升高分辨率遥感枇杷树信息提取的精度。     

HJ数据的LBV变换及其在面向对象分类中的应用

为了探讨环境卫星影像在分类中的应用潜力,通过对其地物光谱进行分析,计算推导出了适用于环境卫星数据的LBV变换公式,并且将变换后得到的LBV图像应用到面向对象分类中。实验结果表明:推导的针对HJ\|1B影像的LBV变换公式具有普适性,并且经过LBV变换后的影像有效地弥补了环境卫星数据光谱分辨率不高的缺点,在分割参数相同的情况下,分割效果明显好于原始影像分割结果。利用变换后的LBV图像进行面向对象分类,可以很好地提取出水体、植被、城镇和建筑用地4大类,总体分类精度达到93%,Kappa系数为0.8894,表明经LBV变换后的HJ影像在面向对象分类中具有很大的应用潜力。     

基于面向对象方法的沙化土地遥感信息提取技术研究

针对基于像元光谱特征提取沙化土地信息分类精度偏低的问题,以Landsat\|5 TM为数据源,基于面向对象的方法对沙化土地遥感信息提取技术进行研究。首先采用多尺度分割法对影像进行分割以获得同质区域,然后结合野外调查数据制成不同地物类型的多种特征图,从而确定提取目标地物的特征并建立沙化和非沙化地物提取决策树,最后对影像进行模糊分类,并对分类结果进行精度评价。结果表明,基于面向对象提取沙化土地信息的总精度达84.89%,Kappa系数为0.8077。研究结果为后续深入研究奠定了基础。     

面向对象的土地覆盖信息提取方法研究

利用遥感方法提取土地覆盖信息时,常用的自动分类方法只能利用遥感数据的光谱信息,而面向对象的分类方法在综合利用遥感数据光谱信息、纹理特征、拓扑关系和加入专题信息进行多尺度分割获取对象后的基础上,通过对对象的目视识别建立模糊判别函数来进行分类。利用面向对象的方法成功提取了三峡库区奉节县中部的土地覆盖信息,得到了较为满意的结果,利用野外采样数据进行验证其精度达89.2%。     

结合像元形状特征分割的高分辨率影像面向对象分类

针对高分辨率遥感影像空间分辨率高,结构形状、纹理、细节信息丰富等特点,提出一种新的融合特征的面向对象影像分类方法来提取城市空间信息。基本过程包含以下4个方面:①提取影像的几何纹理等结构;②融合几何与纹理特征的面向对象影像分割;③提取对象的形状、纹理和光谱特征,并优选最佳特征子集;④最后基于支持向量机(SVM)完成面向对象的影像分类。通过对福州IKONOS影像数据实验,结果表明融入影像特征后的分割效果明显优于原始影像的分割结果,而信息最大化(mRMR)的特征选择能够快速地获得较好的特征子集。通过与eCognition最邻近分类方法比较,表明本文方法的分类总体精度大约提高了6%,效果显著。     

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GEE云平台和Sentinel-2数据的普洱市森林覆盖制图

快速准确获取森林的空间分布对评估森林资源和生态环境状况具有重要的意义。以云南省普洱市为研究区,基于Google Earth Engine(GEE)平台和Sentinel-2影像数据,结合实地调查数据、机载遥感数据及地形辅助数据,提取影像的光谱特征、纹理特征以及地形特征,通过特征筛选,得到适合森林分类的最优特征数据集。结合简单线性非迭代聚类（SimpleNon-Iterative Clustering,SNIC）超像素分割算法,探究不同分类方法、特征变量等因素对分类精度的影响。结果表明：面向对象分类方法的分类精度要优于基于像元分类方法,分类总体精度为88.21%,Kappa系数为0.87,可以较为准确地对普洱市进行森林覆盖制图。面向对象方法可以有效减轻“椒盐现象”,特征优选避免了冗余信息对分类结果的影响,有效提高了分类效率。GEE平台与面向对象方法结合可以提供大区域、高精度的森林覆盖遥感快速制图。     

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。     

面向对象的天然绿洲与人工绿洲区分——以民勤县湖区绿洲为例

以地处河西走廊东端、石羊河下游的民勤县湖区绿洲为例,以Landsat 8 OLI影像为数据源,从天然绿洲和人工绿洲的基本概念出发,在影像数据预处理、多尺度分割的基础上,综合考虑光谱、纹理、形状、上下文等信息,引入NDVI、最大化差异、紧致度、形状指数和空间邻接关系等多个特征,构建规则集进行天然绿洲和人工绿洲的区分,并将区分结果与基于最大似然法监督分类的绿洲区分结果进行比较分析。结果表明:使用面向对象的影像分析方法区分天然绿洲和人工绿洲的总体精度达到了91.75%,Kappa系数为0.65;较之面向像元的最大似然法监督分类结果,总体精度提高了10.40%,Kappa系数提高了0.13,其中人工绿洲条件Kappa系数提高了0.19,天然绿洲条件Kappa系数提高了0.30。面向对象的影像分析方法能够在一定程度上克服单一光谱特征分类方法的局限性,避免"异物同谱"和"同物异谱"现象带来的混淆,提高天然绿洲和人工绿洲区分的精度。     

面向对象的天然绿洲与人工绿洲区分

以地处河西走廊东端、石羊河下游的民勤县湖区绿洲为例,以Landsat 8 OLI影像为数据源,从天然绿洲和人工绿洲的基本概念出发,在影像数据预处理、多尺度分割的基础上,综合考虑光谱、纹理、形状、上下文等信息,引入NDVI、最大化差异、紧致度、形状指数和空间邻接关系等多个特征,构建规则集进行天然绿洲和人工绿洲的区分,并将区分结果与基于最大似然法监督分类的绿洲区分结果进行比较分析。结果表明：使用面向对象的影像分析方法区分天然绿洲和人工绿洲的总体精度达到了91.75%,Kappa系数为0.65;较之面向像元的最大似然法监督分类结果,总体精度提高了10.40%,Kappa系数提高了0.13,其中人工绿洲条件Kappa系数提高了0.19,天然绿洲条件Kappa系数提高了0.30。面向对象的影像分析方法能够在一定程度上克服单一光谱特征分类方法的局限性,避免“异物同谱”和“同物异谱”现象带来的混淆,提高天然绿洲和人工绿洲区分的精度。     

Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery

Textural and local spatial statistical information is important in the classification of urban areas using very high resolution imagery. This paper describes the utility of textural and local spatial statistics for the improvement of object‐oriented classification for QuickBird imagery. All textural/spatial bands were used as additional bands in the supervised object‐oriented classification. The texture analysis is based on two levels: segmented image objects and moving windows across the whole image. In the texture analysis over image objects, the angular second moment textural feature at a 45° angle showed an improved classification performance with regard to buildings, depicting the patterns of buildings better than any other directions. The texture analysis based on moving windows across the whole image was conducted with various window sizes (from 3×3 to 13×13), and four grey‐level co‐occurrence matrix (GLCM) textural features (homogeneity, contrast, angular second moment, and entropy) were calculated. The contrast feature with the 7×7 window size improved classification up to 6%. One type of local spatial statistics, Moran's I feature with the vertical neighbourhood rule, improved the classification accuracy even further, up to 7%. Comparison of results between spectral and spectral+textural/spatial information indicated that textural and spatial information can be used to improve the object‐oriented classification of urban areas using very high resolution imagery.     

基于遥感光谱和空间变量随机森林的黄河三角洲刺槐林健康等级分类

对刺槐林健康状况进行准确分类制图,是进行刺槐林健康状况评估与生态修复的前提。以高分辨率IKONOS影像、基于影像提取的不同窗口、不同灰度共生矩阵纹理信息以及反映局部空间自相关的Local Getis-Ord Gi(Getis统计量)为数据源,结合实测生态样方数据,利用多决策树的组合分类模型随机森林(RF)对刺槐林健康进行分级,对6种方法的分类精度进行了比较且对分类变量的重要性进行了排序。结果显示:19m×19m是最佳纹理计算窗口;灰度共生矩阵均值是最优纹理变量;基于波段4计算的Getis统计量对RF分类具有最重要的作用;较之利用全部光谱、纹理和Getis统计量的80个波段/变量,利用前向选择得到的前16个重要性变量进行RF分类,获得了最高的分类精度(总精度为93.14%,Kappa系数为0.894)。研究证实了从高分影像提取的空间特征信息有助于提高对具有规则分布格局的人工刺槐林健康等级的分类精度;前向选择方法可以利用较少的预测变量获得较高的分类精度。     

Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale,texture and image objects

This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes in a salt marsh. Three classification issues were investigated in the context of segmentation scale: (1) a comparison of single- and multi-scale GEOBIA using spectral bands, (2) the relative benefit of incorporating texture derived from the grey-level co-occurrence matrix (GLCM) in classifying the salt marsh features in single- and multi-scale GEOBIA and (3) the effect of quantization level of GLCM texture in the context of multi-scale GEOBIA. The single-scale GEOBIA experiments indicated that the optimal segmentation was both class and scale dependent. Therefore, the single-scale approach produced an only moderately accurate classification for all marsh classes. A multi-scale approach, however, facilitated the use of multiple scales that allowed the delineation of individual classes with increased between-class and reduced within-class spectral variation. With only spectral bands used, the multi-scale approach outperformed the single-scale GEOBIA with an overall accuracy of 82% vs. 76% (Kappa of 0.71 vs. 0.62). The study demonstrates the potential importance of ancillary data, GLCM texture, to compensate for limited between-class spectral discrimination. For example, gains in classification accuracies ranged from 3% to 12% when the GLCM mean texture was included in the multi-scale GEOBIA. The multi-scale classification overall accuracy varied with quantization level of the GLCM texture matrix. A quantization level of 2 reduced misclassifications of channel and bare mud and generated a statistically higher classification than higher quantization levels. Overall, the multi-scale GEOBIA produced the highest classification accuracy. The multi-scale GEOBIA is expected to be a useful methodology for creating a seamless spatial database of marsh landscape features to be used for further geographic information system (GIS) analyses.     

Building Height Estimation from GF-2 Image based on Morphological Shadow Index

GF-2 is a high resolution earth observing satellite with sub\|meter resolution which is developed by our own technique.To estimate urban building height based on GF\|2 remote sensing image combined with the idea of mathematical morphology and object\|oriented classification.First of all,segment image based on multi\|scale segmentation.Then extract shadow and calculate its length based on object\|oriented classification combined with spectral,shape,Morphological Shadow Index (MSI) and other features.In the end,estimate building height based on the geometrical model of satellite,sun and building and then accuracy evaluation and error analysis are carried out by using the field measurement data.Experimental results showed that 90% of the buildings’ absolute error is less than 1 m.This experiment demonstrate that the method can extract the height of urban building from the GF\|2 image effectively and the immense potential of domestic high resolution remote sensing image in applications on urban building information extraction.     

基于高分辨遥感数据的胡杨与柽柳树冠提取

胡杨、柽柳是干旱荒漠区生境的指示种,其树冠提取是荒漠生境遥感定量监测的基础。以塔里木河下游胡杨、柽柳为研究对象,基于QuickBird数据,使用光谱单数据源SVM、光谱结合纹理SVM、面向对象分类和最大似然分类法提取树冠。结果表明:1光谱结合纹理SVM比光谱单源SVM分类精度高9.65%,冠幅估测精度高7.18%,表明高分辨影像上纹理是提高分类精度的重要因素;2面向对象分类法精度最高,分类总体精度86.47%,较光谱单源SVM提高15.67%,较光谱结合纹理SVM提高6.02%,较最大似然法提高22.58%,其冠幅估测精度达87.45%。它兼顾面向对象影像分割与支持向量机方法优点,有效利用分割对象光谱、纹理和空间等信息,较好地解决了其他方法"同物异谱、异物同谱"造成提取树冠破碎的问题,使树冠提取具有较好的稳定性和较高精度。     

基于面向对象的高分影像分类研究

遥感技术已经成为实现地表信息提取的主要手段。以高分辨率影像为主要数据源,采用面向对象的多尺度分割算法,根据对象的光谱、形状等特征,实现了面向高分遥感数据的土地利用分类算法。该算法结合了面向地物对象和综合对象特征的分类方法,充分发挥了高分辨率影像进行精细地物分类的优势,得到了高精度的分类结果。通过西双版纳纳板河流域国家级自然保护区实例验证表明:该算法总体精度达到88.58%,Kappa系数达到0.77,精度符合应用要求,能够实现土地利用高精度、快速的分类。     

基于CASI影像的黑河中游种植结构精细分类研究

基于CASI高光谱影像资料,计算出NDVI和纹理数据并综合进行SVM(Support Vector Machine)分类,3种信息的组合形成4种分类方案,是为了探讨CASI数据在种植结构精细分类中的应用潜力,为定量研究和监测提供数据基础。数据在分类前利用同步ASD数据和CE\|318数据进行了辐射定标和大气校正。分类结果与地面实际调查数据对比验证结果表明:① 4种分类结果均与地面实际调查情况基本一致,并分别取得了96.78%、97.21%、88.00%、88.38% 的分类精度和0.9676、0.9691、0.8674、0.8716的Kappa系数;② CASI数据信息丰富,在植被的精细分类方面具有很大的应用潜力;③ 结合空间特征信息和NDVI数据可以有效地提高分类精度。     

Mapping reedbed habitats using texture-based classification of QuickBird imagery

Many organisms rely on reedbed habitats for their existence, yet, over the past century there has been a drastic reduction in the area and quality of reedbeds in the UK due to intensified human activities. In order to develop management plans for conserving and expanding this threatened habitat, accurate up-to-date information is needed concerning its current distribution and status. This information is difficult to collect using field surveys because reedbeds exist as small patches that are sparsely distributed across landscapes. Hence, this study was undertaken to develop a methodology for accurately mapping reedbeds using very high resolution QuickBird satellite imagery. The objectives were to determine the optimum combination of textural and spectral measures for mapping reedbeds; to investigate the effect of the spatial resolution of the input data upon classification accuracy; to determine whether the maximum likelihood classifier (MLC) or artificial neural network (ANN) analysis produced the most accurate classification; and to investigate the potential of refining the reedbed classification using slope suitability filters produced from digital terrain data. The results indicate an increase in the accuracy of reedbed delineations when grey-level co-occurrence textural measures were combined with the spectral bands. The most effective combination of texture measures were entropy and angular second moment. Optimal reedbed and overall classification accuracies were achieved using a combination of pansharpened multispectral and texture images that had been spatially degraded from 0.6 to 4.8 m. Using the 4.8 m data set, the MLC produced higher classification accuracy for reedbeds than the ANN analysis. The application of slope suitability filters increased the classification accuracy of reedbeds from 71% to 79%. Hence, this study has demonstrated that it is possible to use high resolution multispectral satellite imagery to derive accurate maps of reedbeds through appropriate analysis of image texture, judicious selection of input bands, spatial resolution and classification algorithm and post-classification refinement using terrain data.