基于多元局部二值模式的遥感图像纹理提取与分类

纹理信息已经广泛应用于遥感图像分类以提高地物识别的精度。为了描述多光谱遥感图像多个波段之间的空间信息变化规律,将新型纹理提取算法局部二值模式（Local Binary Pattern,LBP）扩展到多维空间以计算多元纹理。单波段纹理信息、多元纹理信息分别与光谱信息结合后用于遥感图像分类,并根据分类精度评价其有效性。实验表明,加入单波段或多元纹理信息的分类精度均比光谱分类有明显提高;基于多元LBP纹理的分类不仅避免了传统单波段纹理参与分类前进行波段选择的繁琐,其精度还能与基于单波段纹理分类精度最高者相当或者更高。     

安徽省利辛县平原区人工林树种分类研究

快速准确地绘制平原区人工林树种分布对研究人工林的生态水文和社会经济效益具有重要的意义。将资源3号（ZY-3）全色波段分别同ZY-3多光谱、哨兵2号多光谱进行融合,在图像分割基础上提取变量,采用分层优化变量组合的随机森林分类方法对安徽省利辛县人工林树种进行分类,并与分类回归树和随机森林相比较。结果表明：利用分层分类方法,平原区的人工林树种分类精度可以达到92%以上;基于哨兵2号和ZY-3融合的光谱特征变量分类精度比 ZY-3 数据本身的融合提高了2.49% ~ 2.91%;而分别加入纹理变量后,分层分类方法大幅度提高了树种分类精度。因此,基于高分辨率遥感数据的光谱和纹理特征,采用分层分类方法,可以有效提高平原区人工林树种的分类精度。     

基于子空间-粗集法的高光谱数据光谱与纹理特征优选

为提高光谱数据光谱信息和纹理信息利用率,提出基于自动子空间划分和粗集理论的光谱与纹理特征优选方法。该方法在传统子空间划分法的基础上,利用粗集约简思想对不同类别地物光谱特征进行约简,得到基于光谱的初选波段,再利用灰度共生矩阵法计算出初选光谱波段的纹理信息,并约简优选,得到基于光谱和纹理信息的终选波段。利用黑河生态水文遥感试验中所获取的机载高光谱数据CASI,开展该方法的实证研究。对原始光谱波段、初选光谱波段和终选波段进行SVM(Support Vector Machine)分类,结果表明:与原始光谱数据相比,经过光谱初选得到的初选波段和增加纹理优选的终选波段,总体分类精度分别提高了0.84%和2.78%,Kappa系数分别提高了0.01和0.035;对地物纹理信息进行深度挖掘可以进一步提高遥感影像分类精度。     

基于多元数据和不同分类算法的遥感影像信息提取及精度评价 ——以祁连山东段为例

以祁连山东段典型山地系统为研究区,通过提取研究区TM影像的主成分、各类植被指数、基于灰度共生矩阵的影像纹理特征以及研究区地形特征等数据,应用最优波段指数方法得到最优波段组合,并运用非监督分类、最大似然法、支持向量机分类法、决策树分类法对上述最优波段进行分类研究。结果表明多尺度数据挖掘有利于分类精度的提高,同时选取合适的判断标准的决策树分类方法在遥感信息提取中有比较直观意义和较高的分类精度。在上述分类方法中分类精度由高到低为决策树分类>支持向量机法>最大似然法>非监督分类法。决策树分类总体分类精度为94.50%,kappa系数为0.9122。
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基于小波卷积网络的高光谱图像分类

高光谱图像波段多、波段之间关联性强, 但其空间纹理和几何信息的表达较弱, 传统分类模型存在空间光谱特征提取不充分、计算量大的问题, 分类性能有待提高. 针对此问题, 提出一种基于小波变换的多尺度多分辨率注意力特征融合卷积网络 (wavelet transform convolutional attention network, WTCAN), 采用小波变换思想对光谱波段进行4次分解, 通过层次性提取光谱特征可减少计算量. 该网络设计了空间信息提取模块, 同时引入金字塔注意力机制, 通过设计逆向跳跃连接网络结构利用多尺度获取空间位置特征, 增强空间纹理表达能力, 可以有效改进传统2D-CNN特征提取尺度单一、忽略空间纹理细节等缺陷. 本文对所提出的WTCAN模型分别在不同空间分辨率高光谱数据集Indian Pines (IP)、WHU_Hi_HanChuan (HanChuan)、WHU_Hi_HongHu (HongHu)进行实验, 通过对比SVM、2D-CNN、DBMA、DBDA、HybridSN模型效果, WTCAN模型取得较好的分类效果, 3个数据集的分类总体精度分别达到了98.41%、99.64%、99.67%, 可为高光谱图像的分类研究提供参考依据.     

利用高分二号数据提取香蕉林信息及精度分析

针对海南农田地块细碎以及多云多雨气候条件下获取多时相的高质量卫星影像往往存在困难等问题,提出了一种利用单时相高分二号高分辨率卫星影像和随机森林算法的香蕉林信息提取方法。主要通过从高分辨率遥感影像中提取香蕉林的光谱和纹理等特征变量,然后利用综合不同光谱与纹理特征变量的随机森林分类算法进行香蕉林信息提取,并与以往的支持向量机分类算法进行了精度对比。结果表明,综合光谱和纹理信息的随机森林分类算法提取香蕉林空间分布结果最优,提取的香蕉林制图精度(PA)达到93.56%,用户精度(UA)达到87.43%;相比于支持向量机分类算法,PA和UA分别提高了11.99%和7.55%;相比只考虑光谱信息的随机森林分类算法,考虑纹理信息的随机森林分类算法提取的香蕉林PA提高了7.41%,UA提高了16.80%。研究结果可为人工园林的遥感信息提取提供技术参考。     

基于多季相Sentinel-2影像的白洋淀湿地信息提取

白洋淀湿地是华北平原上重要的浅水湖泊湿地,对雄安新区绿色发展具有重要的生态价值。对白洋淀高度异质化的景观格局进行分类,能够为白洋淀湿地资源的遥感监测提供指导意义。针对湿地季节变化的特点,对白洋淀每个季节选取一期具有代表性的Sentinel-2影像,采用分类与回归树（CART）、支持向量机（SVM）、随机森林（RF）3种常用的机器学习分类器对15种季相组合实验方案进行分类,分析不同季相遥感影像及其组合对白洋淀湿地信息提取的优劣。结果表明：相较于使用单一季相影像分类,多季相影像的组合能够显著提高分类精度,春&夏季相组合能够得到最优的分类效果,相对单季影像总体分类精度提高了10.9%~25.5%,Kappa系数提高了0.09~0.29;SVM分类器的分类表现较为稳定,能够得到最高的平均分类精度,CART分类器在处理高维特征的能力不如随机森林和SVM;不同特征类型对湿地信息提取的贡献度从高到底依次是红边光谱特征、传统光谱特征、缨帽变换特征、主成分分析特征、纹理特征。实验成果能为湿地信息的遥感识别提供依据。     

机载中波红外影像分类性能分析

由于中波红外谱段复杂的辐射特性以及红外探测技术的限制,目前学界对中波红外的遥感分类应用探索较少。该文是在国内首幅可见光-中波红外高分辨率(中波红外0.6m分辨率)多光谱影像的基础上,探索地物的中波红外辐射特性,挖掘中波红外谱段的潜在价值,进而融合地物的中波红外与可见光的特征,分析中波红外影像的地物分类性能,提高遥感地物分类的精度。中波红外谱段的光谱辐射特性不同于可见光与热红外谱段,既包含地面反射辐射,也包含地面物体的发射辐射能量。研究中基于多尺度分割算法与随机森林分类器分别对可见光影像和中红外+可见光四波段融合影像进行面向对象分类。该方法融合了地物的可见光与中波红外特征,并且评估了光谱、形状、纹理等特征在分类中的重要程度,定量分析了融合中波红外波段后的特征空间。研究结果表明:针对中红外特征,最有效特征为中红外与可见光其中两波段组合HIS空间各分量特征,其次为灰度共生矩阵纹理信息;中波红外波段的引入可以稳定地提高地物分类的总精度;中波红外波段对于人工地物的分类效果优于非人工地物类型,其中建筑物的分类精度提升最为显著。     

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。     

基于哨兵2时间序列组合植被指数的作物分类研究

时间序列是一种常用的物候研究方法。为充分利用哨兵2数据在红边波段的丰富信息,本文利用多种植被指数组合成时间序列进行作物分类。将NDVI、EVI、红边NDVI三种植被指数进行组合,构建时序植被指数图像,然后使用支持向量机、随机森林、CART决策树和最大似然4种不同的算法对四种作物、三种林草、裸露地表、水体进行分类。原始分类结果中,总体精度最高的随机森林为87.92%,最低的最大似然为80.07%,在分类细节上,随机森林和支持向量机的边界最清晰,4种分类结果中,农作物的分类精度均高于其他地类,仅次于水体的精度,误差主要来自三种林草的混分,表明时间序列组合植被指数用于农作物分类是可行的。     

遥感图像森林类型小波纹理的SVM法分类

利用遥感图像对森林类型进行分类是大面积地调查、监测、分析森林资源的快速与经济的方法,但由于不同森林的光谱特征非常相近而较难准确分类。因此,在GPS数据和高分辨率遥感图像的支持下,对水源林Landsat TM遥感图像用窗口法获得阔叶林、针叶林和竹林样本图像,然后计算其小波分解后小波系数的l1范数纹理测度构成分类特征向量,利用支持向量基SVM进行分类。结果表明,利用SVM对图像中阔叶林、针叶林和竹林分类平均精度在80%以上,可较准确地识别森林类型,图像总体分类精度达到90.2%,Kappa系数0.77,均比利用小波纹理特征的神经网络法和最大似然法有所提高,森林分类错误产生的主要原因是混交林造成两类森林间存在交集。该方法可以较有效地提高遥感图像森林类型的分类精度。     

Classification of SPOT HRV imagery and texture features

Abstract

Spatial co-occurrence matrices were computed for a SPOT HRV multispectral image for a moderate-relief environment in eastern Canada. The texture features entropy and inverse difference moment were used with the spectral data in landcover classification, and substantive increases in accuracy were noted. These range from 10 per cent for exposed bedrock to over 40 per cent in forest and wetland classes. The average classification accuracies were increased from 511 per cent (spectral data alone) to 86.7 per cent (spectral data plus entropy measured in band 2 and inverse difference moment in band 3). Classes that are homogeneous on the ground were characterized adequately by spectral tone alone, but classes containing mixed vegetation patterns or strongly related to structure were characterized more accurately by using a mixture of spectral tone and texture.     

Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery

The extraction of texture features from high‐resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey‐level co‐occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non‐forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro‐texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral‐only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21×21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT‐texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM‐mean texture (micro‐textures) than by WT‐derived texture (macro‐textures), the results show that the micro–macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer‐assisted classification of high‐spatial‐resolution remotely sensed imagery has a good potential to augment the present ground‐based forest inventory methods.     

云的光谱和纹理特征统计分析

利用静止卫星图像资料建立了夏季白天中低纬地区的11 种云/ 表面类型的样本集, 从中随机 挑选656 个样本, 提取116 个光谱和纹理特征参数并进行统计分析, 通过特征选择组成特征向量, 带入逐个修改聚类和模糊聚类的分类器进行敏感性试验。结果发现, 在反映云特征方面, 光谱特征 是云分类最基本的特征, 比纹理特征明显, 是云分类识别的主要依据; 除去水汽通道的标准差以外 其它光谱特征都比较明显, 红外和水汽通道的特征明显好于可见光通道, 尤其是对中低云和卷云的 描述。纹理特征在反映云特征方面也有一定的代表性, 特别是一阶概率特征中四通道的惯量及水汽 通道的逆差距; 纹理特征引入后分类准确率显著提高, 但在引入一阶概率特征基础上引入灰度级差 矢量特征效果改善并不明显。     

遥感图像林型纹理特征的ICA与SVM分类

遥感图像纹理特征是光谱相近林型准确分类的有效方法,然而其带来分类特征向量维数增加和计算量增大。因此,对南方山区林地TM图像进行独立成分分析ICA降维,通过计算灰度共生矩阵获取纹理特征,使用SVM分类,研究林地类型的快速分类方法。结果表明,ICA与SVM法利用遥感图像纹理特征可较准确地实现林地类型分类,分类总精度、Kappa系数分别为85.4%、0.73,均高于SVM法、BP神经网络法、最大似然法、最小距离法;其对阔叶林、针叶林、竹林的分类精度依次为78.2%、80.1%、84.3%,误识率主要是由于混交林而造成两类林地之间存在交集,易出现的针阔混交林使得阔叶林、针叶林的分类精度低于竹林。     

基于卷积神经网络的遥感图像分类研究

遥感图像分类是模式识别技术在遥感领域的具体应用,针对遥感图像处理中的分类问题,提出了一种基于卷积神经网络(convolutional neural networks,CNN)的遥感图像分类方法,并针对单源特征无法提供有效信息的问题,设计了一种多源多特征融合的方法,将遥感图像的光谱特征、纹理特征、空间结构特征等按空间维度以向量或矩阵的形式进行有效融合,以此训练CNN模型。实验表明,多源多特征相融合能够加快模型收敛速度,有效提高遥感图像的分类精度;与其他分类方法相比,CNN能够取得更高的分类精度,获得更优的分类效果。     

Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey

Hazelnuts and tea are two major agricultural crops grown in the eastern Black Sea region in Turkey. Since this part of Turkey is not industrialized, most of the local people work in agriculture, making hazelnuts and tea a part of their lives. For the government side, it is crucial to keep records of the amount of harvested croplands to implement agricultural policies. In fact, the harvested area and crop type of each cadastral parcel are collected either during cadastral surveys or with the declaration of individual farmers, yet this information is mostly not up-to-date and does not reflect the current land-use status. This study aims to determine the extent and distribution of hazelnuts and tea grown areas using the Random Forest (RF) classification algorithm. Tea and hazelnuts give similar spectral reflectance values to surrounding vegetation, which makes it difficult to distinguish them using only their spectral properties. To tackle this problem, the normalized difference vegetation index (NDVI) and texture extraction methods such as the Grey Level Co-occurrence Matrix (GLCM) and Gabor filter were integrated with the RF algorithm, and their contributions to the success of the RF classification method were examined. WorldView-2 satellite images, which have eight multispectral bands (MS: 2 m) and one higher spatial resolution panchromatic band (PAN: 0.5 m), were used. Since the study area contains agricultural products grown in different seasons, satellite images belonging to both summer and winter periods were used. Preliminary results acquired using only spectral values indicated that the RF method gives 79.05% and 71.84% overall accuracies for summer and winter periods, respectively. Integrating texture information improves the performance of the RF algorithm such that the overall classification accuracies are increased to 83.54% and 87.89% when texture information extracted with GLCM and the Gabor filter is added. The classification performance of the winter image is also boosted to be 77.41% and 79.73% with the contribution of texture information obtained with GLCM and the Gabor filter, respectively. Finally, produced thematic maps were compared with the latest cadastral maps to validate classification results with ground truth data. The obtained results reveal the success of integrating texture features in classification since the created thematic maps are consistent with actual land use. The results also show that the crops grown on some cadastral parcels are not coherent with the most current cadastral database, which implies that the cadastral maps need to be updated.     

基于面向对象的多源卫星遥感影像玉米倒伏面积提取

风灾引起的玉米倒伏可能导致玉米大量减产,利用遥感技术准确监测玉米倒伏面积与空间分布信息对灾情的评估非常重要。利用Planet和Sentinel-2影像分别结合面向对象与基于像元方法提取研究区玉米倒伏,同时评估了不同影像特征（光谱特征、植被指数和纹理特征）与不同分类方法（支持向量机法SVM、随机森林法RF和最大似然法MLC）对玉米倒伏提取精度的影响。结果表明：①使用高空间分辨率的Planet影像进行玉米倒伏提取的精度普遍高于Sentinel-2影像;②从分类精度和面积精度来看,Planet影像的光谱特征+植被指数+均值特征结合面向对象RF分类,总体精度和Kappa系数分别为93.77%和0.87,面积的平均误差最低为4.76%;③采用Planet和Sentinel-2影像结合面向对象分类提取玉米倒伏精度高于基于像元分类。研究不仅分析了面向对象方法的优势,还评估了使用不用影像数据结合面向对象方法的适用性,可以为遥感提取作物倒伏相关研究提供一定的借鉴。     

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.