傅里叶谱纹理和光谱信息结合的高分辨率遥感影像地表覆盖分类

以高空间分辨率遥感影像为研究对象,将纹理特征与影像的光谱特征结合起来,用于地表覆盖类型分类。设计了一种基于傅里叶谱纹理的分类策略,对主成分分析后的第1、2主分量特征影像,利用径向谱（r-spectrum）提取纹理特征,并将纹理与光谱特征结合起来,构建了不同的分类特征用于支持向量机分类模型。以Salinas数据集和QuickBird影像为例,验证该算法。结果表明,纹理与光谱信息的结合可以明显提高高分辨率遥感影像的分类精度;由傅里叶径向谱提取的纹理特征可以很好的应用到高分辨率遥感影像的分类问题中,分类精度高于基于傅里叶总能量谱和灰度共生矩阵的分类精度;利用该算法对PCA变换后的第1和第2分量提取的纹理特征具有一定的互补性,并且结合多特征图像的纹理特征提取优于单特征图像的纹理特征提取。     

Use of field reflectance data for crop mapping using airborne hyperspectral image

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question “what is the prospect of using independent reference reflectance spectra for image classification”, while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of “non-existence of characteristic reflectance spectral signatures for vegetation”, results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.     

Multi-Feature Classification Approach for High Spatial Resolution Hyperspectral Images

High spatial resolution hyperspectral images not only contain abundant radiant and spectral information, but also display rich spatial information. In this paper, we propose a multi-feature high spatial resolution hyperspectral image classification approach based on the combination of spectral information and spatial information. Three features are derived from the original high spatial resolution hyperspectral image: the spectral features that are acquired from the auto subspace partition technique and the band index technique; the texture features that are obtained from GLCM analysis of the first principal component after principal component analysis is performed on the original image; and the spatial autocorrelation features that contain spatial band X and spatial band Y, with the grey level of spatial band X changing along columns and the grey level of spatial band Y changing along rows. The three features are subsequently combined together in Support Vector Machine to classify the high spatial resolution hyperspectral image. The experiments with a high spatial resolution hyperspectral image prove that the proposed multi-feature classification approach significantly increases classification accuracies.     

Is it possible to discern Striga weed (Striga hermonthica) infestation levels in maize agro-ecological systems using in-situ spectroscopy?

The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum (EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis: LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga (Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 waveband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 using published spectral response functions. We sampled and detected seven Striga infestation classes based on three flowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture of maize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to select the most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2 multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algorithms was compared using classification accuracy assessment metrics. We were able to positively discriminate Striga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurring weeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampled Sentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highest overall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands, respectively, across the four different discriminant models tested in this study. The class with the highest detection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurring weeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 most relevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectral wavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectral wavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hyperspectral features (i.e. wavebands and VIs) significantly (p ≤ 0.05) increased the overall classification accuracy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Our results show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learning discriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems.     

Understanding the Unique Spectral Signature of Winter Rape

Driven by significant technological developments in the hyperspectral imaging, material mapping using reference spectra has received renewed interest of the remote sensing community. The applicability of reference spectral signatures in image classification depends mainly on the material type and its spectral signature behaviour. Identification and spectral characterization of materials which exhibit unique spectral behaviour is the first step in this approach. Consequently there have been active researches for the identification of surface materials which exhibit unique spectral signatures. The uniqueness of reflectance signature of winter rape relative to its co-occurring crop species was reported in this study. Reflectance spectral libraries constructed from field spectral reflectance measurements collected over five agricultural crops (alfalfa, winter barley, winter rape, winter rye, and winter wheat) during four subsequent growing seasons were classified by the linear discriminant analysis (LDA). Further, the reference field spectral database was used for the spectral feature fitting and classification of a historical HyMAP airborne hyperspectral imagery acquired at a separate site, by spectral library search. Results indicate the existence of a meaningful spectral matching between image and field spectra for winter rape and demonstrate the potential for transferring spectral library for hyperspectral image classification. The observed consistency in the discrimination of winter rape demonstrates experimentally the fundamental principle of remote sensing which suggests the theoretical existence of unique spectral signatures for materials which can be incorporated as reference spectral signatures for hyperspectral image classification.     

Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image

The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r?=?0.982 and computed SID equivalent r?=?0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure.     

Evaluation of semivariogram features for object-based image classification

Inclusion of textures in image classification has been shown beneficial. This paper studies an efficient use of semivariogram features for object-based high-resolution image classification. First, an input image is divided into segments, for each of which a semivariogram is then calculated. Second, candidate features are extracted as a number of key locations of the semivariogram functions. Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features. Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier. The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix (GLCM) features and window-based semivariogram texture features (STFs). Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features. The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.     

日光温室的纹理特征分层提取

针对高分辨率影像上日光温室的信息提取问题,该文提出了利用支持向量机、最近邻算法结合纹理特征在不同层上分别提取连片日光温室和独栋日光温室的方法。实验表明:纹理特征能提高分类精度,在大尺度的层上,分类精度提升幅度较大,但在小尺度的层上,分类精度提升幅度会比较小;并不是参与运算特征数越多,分类精度越高,多数情况下光谱+纹理组合的分类精度最高;提取连片日光温室的最优方案是支持向量机和光谱+形状+纹理(7像素×7像素),总精度为92.86%,Kappa系数为0.90,而提取独栋日光温室最优方案为SVM和光谱+纹理(11像素×11像素),总精度为88.39%,Kappa系数为0.86。     

高光谱影像光谱-空间多特征加权概率融合分类

提出了一种基于光谱-空间多特征加权概率融合的高光谱影像分类方法。首先,利用最小噪声分离(minimum noise fraction,MNF)方法对高光谱影像进行降维和特征提取,并以得到的MNF特征影像作为光谱特征,联合灰度共生矩阵(gray level co-occurrence matrix,GLCM)提取的纹理特征、基于OFC算子建立的多尺度形态学特征以及采用连续最大角凸锥(sequential maximum angle convex cone,SMACC)提取的端元组分特征,组成3组光谱-空间特征;然后利用支持向量机(support vector machine,SVM)对每一组光谱-空间特征进行分类,得到每组特征的概率输出结果;最后,建立多特征加权概率融合模型,应用该模型将不同特征的概率输出结果进行加权融合,得到最终分类结果。为了验证该方法的有效性,利用ROSIS和 AVIRIS影像进行试验,总体分类精度分别达到97.65%和96.62%。结果表明本文的方法不但较好地克服了传统基于单一特征高光谱影像分类的局限性,而且其分类效果也优于常规矢量叠加(vector stacking,VS)和概率融合的多特征分类方法,有效地改善了高光谱影像的分类结果。     

Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images.     

基于分形理论的航空图像分类方法

提出一种基于分形理论和BP神经网络的航空遥感图像有监督分类方法。该方法尝试将航空图像的光谱信息和纹理特征相结合。它首先将彩色航空图像由RGB格式转化为HSI格式，然后，根据亮度计算分数维、多重分形广义维数谱q-D(q)和“空隙”等基于分形的纹理特征，同时加入归一化的色度和饱和度作为光谱特征，采用BP神经网络作为分类器。通过对彩色航空图像的分类实验，结果证实该方法行之有效。     

基于分形理论的航空图像分类方法

提出一种基于分形理论和BP神经网络的航空遥感图像有监督分类方法。该方法尝试将航空图像的光谱信息和纹理特征相结合。它首先将彩色航空图像由RGB格式转化为HSI格式，然后，根据亮度计算分数维、多重分形广义维数谱q-D(q)和“空隙”等基于分形的纹理特征，同时加入归一化的色度和饱和度作为光谱特征，采用BP神经网络作为分类器。通过对彩色航空图像的分类实验，结果证实该方法行之有效。     

基于多特征的Gaofen-1海冰影像监督分割

冬季沿海地区的海冰检测工作对该地区居民的生产生活具有重要的指导意义,同时可以根据海冰变化检测气候变暖情况。海冰影像分割是海冰检测的基础。在众多海冰影像数据源中,Gaofen-1海冰影像因其丰富的光谱特征、较高的空间分辨率、简单的数据结构,在变化监测中具有重要应用价值。本文提出了一种基于红绿蓝3个光谱通道的灰度共生矩阵,提取遥感影像的纹理特征和光谱特征构成多特征的Gaofen-1海冰影像监督分割方法。以Gaofen-1合成的模拟海冰影像和某海湾地区真实Gaofen-1海冰影像进行分割实验,实验结果很好地证明了算法的可行性和可靠性。     

不同传感器的模拟植被指数对水稻叶面积指数的估测精度和敏感性分析

本研究利用水稻冠层高光谱数据,模拟NOAA-AVHRR,Terra-MODIS和Landsat-TM的可见光波段反射率数据,计算各传感器的多种植被指数(NDVI,RVI,EVI,GNDVI,GRVI和Red-edge RVI),比较植被指数模型对水稻LAI的估测精度,分析不同植被指数对LAI变化的敏感性.相对于红波段植被指数,红边比值植被指数(Red-edge RVI)和绿波段指数GRVI与LAI有更好的线性相关关系,而GNDVI和LAI呈现更好的对数相关关系.MODIS的Red-edge RVI指数不仅模型拟合的精度最高,还有独立数据验证的估测精度也最高,而且它的验证精度较拟合精度下降幅度最小;其次是绿波段构建的GNDVI和GRVI植被指数的估测精度,再次是NDVI和EVI的估测精度,而RVI的估测精度最差.敏感性分析发现,13个植被指数对水稻LAI的估测能力都随着LAI的增加而下降,但归一化类植被指数和比值类植被指数对LAI变化反应的差异明显,归一化类植被指数在LAI较低时(LAI<1.5)对LAI变化的反应开始非常敏感,但迅速下降,而比值类植被指数在LAI较低时,明显小于归一化类植被指数,之后随着LAI的增大(LAI>1.5)比值类植被指数对LAI的变化敏感性,则明显高于归一化类植被指数.Red-edge RVI和绿波段指数GRVI和LAI不仅表现了很好的线性相关关系,而且在LAI大于2.9左右保持较高的敏感性.     

Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons

Remote sensing images are widely used to map leaf area index (LAI) continuously over landscape. The objective of this study is to explore the ideal image features from Chinese HJ-1 A/B CCD images for estimating winter wheat LAI in Beijing. Image features were extracted from such images over four seasons of winter wheat growth, including five vegetation indices (VIs), principal components (PC), tasseled cap transformations (TCT) and texture parameters. The LAI was significantly correlated with the near-infrared reflectance band, five VIs [normalized difference vegetation index, enhanced vegetation index (EVI), modified nonlinear vegetation index (MNLI), optimization of soil-adjusted vegetation index, and ratio vegetation index], the first principal component (PC1) and the second TCT component (TCT2). However, these image features cannot significantly improve the estimation accuracy of winter wheat LAI in conjunction with eight texture measures. To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values. Four remote sensing features (TCT2, PC1, MNLI and EVI) were chosen based on VIP values. The result of leave-one-out cross-validation demonstrated that the PLSR model based on these four features produced better result than the ten features’ model, throughout the whole growing season. The results of this study suggest that selecting a few ideal image features is sufficient for LAI estimation.     

单变量特征选择的苏北地区主要农作物遥感识别

遥感识别多源特征综合和特征优选是提高遥感影像分类精度的关键技术。农作物遥感识别中,识别特征的相对单一和数量过多均会导致作物识别精度不理想。随机森林(random forests)采用分类与回归树(CART)算法来生成分类树,结合了bagging和随机选择特征变量的优点,是一种有效的分类方法。单变量特征选择(univariate feature selection)能够对每一个待分类的特征进行测试,衡量该特征和响应变量之间的关系,根据得分舍弃不好的特征,优选得到的特征用于分类。本文基于随机森林和单变量特征选择,利用多时相光谱信息、植被指数信息、纹理信息及波段差值信息,设计多组分类实验方案,对江苏省泗洪县的高分一号(GF-1)和环境一号(HJ-1A)影像进行分类研究,旨在选择最佳的分类方案对实验区主要农作物进行识别和提取。实验结果表明:(1)多源信息综合的农作物分类精度明显高于单一的原始光谱特征分类,说明不同类型特征的引入能改善分类效果;(2)基于单变量特征选择算法的优选特征分类效果最佳,总体精度97.07%,Kappa系数0.96,表明了特征优选在降低维度的同时,也保证了较高的分类精度。随机森林和单变量特征选择结合的方法可以提高遥感影像的分类精度,为农作物的识别和提取研究提供了有效的方法。     

Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach

This paper proposes an efficient paddy field mapping method using object-based image analysis and a bitemporal data set acquired by Landsat-8 Operational Land Imager. In the proposed approach, image segmentation is the first step and its quality has a serious impact on the accuracy of paddy field classification. In order to improve segmentation quality, a new segmentation algorithm based on a frequently used method, fractal net evolution approach, is developed, with improvement mainly in merging criteria. In order to automate the process of scale parameter determination, an unsupervised scale selection method is utilized to determine the optimal scale parameter for the proposed image segmentation approach. After segmentation, four types of object-based features including geometric, spectral, textural, and contextual information are extracted and input into the subsequent classification procedure. By using a random forest classifier, paddy fields and nonpaddy fields are separated. The proposed image segmentation method and the final classification result are both quantitatively evaluated. Our segmentation method outperformed two popular algorithms according to three supervised evaluation criteria. The classification result with overall accuracy of 91.00% and kappa statistic of 0.82 validated the effectiveness of the proposed framework. Further analysis on feature importance indicated that spectral features made the most contribution as compared to the other three types of object-based features.     

领域知识辅助下基于多尺度与主方向纹理的遥感影像土地利用分类

基于灰度共生矩阵(GLCM)的纹理特征在影像空间分析中具有重要作用,提出了一种在领域空间知识辅助下构建GLCM多尺度窗口与主方向权值的方法,从而提高纹理特征的有效性,并解决影像土地利用分类中存在的不确定性问题。为此,根据人类目视解译的特点,对GIS与RS数据进行集成计算:首先,在图像配准的基础上,利用经典的GIS空间数据挖掘算法,渐近式地提取领域形态知识;接着,采用关联分析法建立其与GLCM构造因子之间的响应机制,并设计了基于地类形状指数的多尺度窗口建立算法,以及基于地类主方向分布指数的方向权值测度算法。试验结果表明,领域形态知识与GLCM空间因子之间具有强相关关系,该方法提取出的纹理特征可以描述复杂地物的空间意义,算法复杂度低,性能优越,有效提高了影像土地利用分类的精度。     

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using Object Based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 data comparisons were also extended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific spectral contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overall accuracies respectively, evaluated over the whole study area.     

Exploitation of TerraSAR-X Data for Land use/Land Cover Analysis Using Object-Oriented Classification Approach in the African Sahel Area, Sudan

Recently, object-oriented classification techniques based on image segmentation approaches are being studied using high-resolution satellite images to extract various thematic information. In this study different types of land use/land cover (LULC) types were analysed by employing object-oriented classification approach to dual TerraSAR-X images (HH and HV polarisation) at African Sahel. For that purpose, multi-resolution segmentation (MRS) of the Definiens software was used for creating the image objects. Using the feature space optimisation (FSO) tool the attributes of the TerraSAR-X image were optimised in order to obtain the best separability among classes for the LULC mapping. The backscattering coefficients (BSC) for some classes were observed to be different for HH and HV polarisations. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value 0.82 was resulted from the classification scheme, while accuracy differences among the classes were kept minimal. Finally, the results highlighted the importance of a combine use of TerraSAR-X data and object-oriented classification approaches as a useful source of information and technique for LULC analysis in the African Sahel drylands.