谐波分析光谱角制图高光谱影像分类

目的 针对光谱角制图(SAM)分类算法对高光谱像元光谱曲线的局部特征和其辐射强度不敏感,而且易受噪声和维数灾难影响,致使分类效率低和精度较差等缺陷,将谐波分析(HA)技术引入到SAM高光谱影像分类中,提出一种基于谐波分析的光谱角制图(HA-SAM)高光谱影像分类算法.方法 利用HA技术将高光谱影像从光谱维变换到能量谱特征维空间,并提取低次谐波分量及特征系数(谐波余项、相位和振幅),用特征系数组成的向量代替光谱向量,对高光谱影像进行SAM分类.结果 将SAM和HA-SAM同时应用于EO-1卫星的Hyperion高光谱影像分类,通过对比和分析,验证了HA-SAM的优越性,再选择AVIRIS(airborne visible infrared imaging spectrometer)高光谱影像对HA-SAM进行验证,结果表明该算法具有较强的普适性.结论 HA-SAM提高了传统SAM高光谱影像分类的效率和精度,而且适用性较强具有良好的应用前景.     

基于粒子群优化的高光谱影像端元提取算法

回顾了粒子群算法的基本原理,分析了端元提取算法的两种技术途径。利用粒子群优化的原理,结合凸面几何学理论和线性光谱混合模型,设计了一种粒子群优化端元提取算法,并设计了算法的快速实现方法。该算法不需要假设影像中存在纯像元,同时保持了端元光谱的形状。利用模拟数据和AVIRIS影像对该算法、SGA算法和NMF算法进行实验对比分析,实验结果证明该算法的端元提取精度优于其他二者。     

Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring

Mountain pine beetle red attack damage has been successfully detected and mapped using single-date high spatial resolution (< 4 m) satellite multi-spectral data. Forest managers; however, need to monitor locations for changes in beetle populations over time. Specifically, counts of individual trees attacked in successive years provide an indication of beetle population growth and dynamics. Surveys are typically used to estimate the ratio of green (current) attack trees to red (previous) attack trees or G:R. In this study, we estimate average stand-level G:R using a time-series of QuickBird multi-spectral and panchromatic satellite data, combined with field data for three forested stands near Merritt, British Columbia, Canada. Using a ratio of QuickBird red to green wavelengths (Red-Green Index or RGI), the change in RGI (ΔRGI) in successive image pairs is used to estimate red attack damage in 2004, 2005, and 2006, with true positive accuracies ranging from 89 to 93%. To overcome issues associated with differing viewing geometry and illumination angles that impair tracking of individual trees through time, segments are generated from the QuickBird multi-spectral data to identify small groups of trees. These segments then serve as the vehicle for monitoring changes in red attack damage over time. A local maxima filter is applied to the panchromatic data to estimate stem counts, thereby allowing an indication of the total stand population at risk of attack. By combining the red attack damage estimates with the local maxima stem counts, predictions are made of the number of attacked trees in a given year. Backcasting the current year's red attack damaged trees as the previous year's green attack facilitates the estimation of an average stand G:R. In this study area, these retrospective G:R values closely match those generated from field surveys. The results of this study indicate that a monitoring program using a time series of high spatial resolution remotely sensed data (multi-spectral and panchromatic) over select sample locations, could be used to estimate G:R over large areas, facilitating landscape level management strategies and/or providing a mechanism for assessing the efficacy of previously implemented strategies.     

局部投影可分离的高光谱图像异常检测

针对高光谱异常检测中临近异常像素相互干扰和背景地物复杂的问题,提出基于局部投影可分离的高光谱图像异常检测算法.在归一化的数据中,将待测像素光谱作为参考光谱,构造目标子空间,然后把邻域背景像素投影到该子空间,用投影后向量模值构造异常度计算式.最后将检测到的异常与全局主要背景地物进行比对,肖除部分虚警.利用HyMap高光谱数据进行仿真实验结果表明,本文算法具有克服背景复杂性和干扰点的影响,尤其对异类干扰点的抑制效果更佳.     

基于目标优化的高光谱图像亚像元定位

目的 高光谱图像混合像元的普遍存在使得传统的分类技术难以准确确定地物空间分布,亚像元定位技术是解决该问题的有效手段。针对连通区域存在孤立点或孤立两点等特例时,通过链码长度求周长最小无法保证最优结果及优化过程计算量大的问题,提出了一种改进的高光谱图像亚像元定位方法。方法 以光谱解混结合二进制粒子群优化构建算法框架,根据光谱解混结果近似估计每个像元对应的亚像元组成,通过分析连通区域存在特例时基于链码长度求周长最小无法保证结果最优的原因,提出修改孤立区域的周长并考虑连通区域个数构造代价函数,最后利用二进制粒子群优化实现亚像元定位。为了减少算法的时间复杂度,根据地物空间分布特点,采用局部分析代替全局分析,提出了新的迭代优化策略。结果 相比直接基于链码长度求周长最小的优化结果,基于改进的目标函数优化后,大部分区域边界更明显,并且没有孤立1点和孤立两点的区域,识别率可以提高2%以上,Kappa系数增加0.05以上,新的优化策略可以使算法运算时间减少近一半。结论 实验结果表明,本文方法能有效提高亚像元定位精度,同时降低时间复杂度。因为高光谱图像中均匀混合区域不同地物的分布空间相关性不强,因此本文方法适用于非均匀混合的高光谱图像的亚像元定位。     

基于改进RX算法的高光谱异常检测

针对RX算法中局部背景协方差矩阵估计的局限性,提出一种改进的RX (I-RX)异常检测算法。基于奇异值分解(SVD),将高光谱图像投影到背景的正交子空间上,获得仅包含噪声和异常的残留图像。在此基础上,通过计算各样本的空间秩深度将残留图像划分为噪声背景和潜在异常两个样本集,利用噪声背景集估计整幅图像的背景协方差矩阵,并将潜在异常集作为测试样本进行异常检测。对模拟数据和真实高光谱数据进行了实验仿真,ROC曲线表明,在相同的虚警概率下,I-RX算法的检测概率相较于RX平均提高了2倍左右。     

结合多尺度分割和形态学运算的高分辨率遥感影像阴影检测

目的 针对阴影在高分辨率遥感影像的特性,提出了一种多尺度分割和形态学运算相结合的阴影检测方法。方法 基于面向对象思想,首先利用均值漂移法实现影像分割生成对象,并以对象为基本单元分别进行形态学膨胀和腐蚀运算,从而获得面向对象的阴影指数;然后对影像进行多尺度分割,生成阴影指数矢量;最后对阴影指数矢量和亮度均值分别指定高低阈值,进而获得阴影检测结果。结果 选取高分二号和Google earth影像进行实验,采用误检率、漏检率和总错误率3个指标进行定量分析,并将实验结果与结合多特征法和形态学阴影指数法进行比较。在阴影检测定量精度分析中,相比于对比方法,本文方法的误检率偏高,但漏检率平均降低了7.31%;在建筑物阴影检测实验中,本文方法的漏检率同样下降了4.5个百分点;在多尺度效果融合分析中,本文方法在多组尺度组合下,各项精度指标均较理想;在阴影压盖地物实验中,3种方法的误检情况差异不大,但本文方法的漏检率得到较大改善,其下降程度平均达到了19.29%。结论 本文提出的阴影检测方法具备一定的抗干扰能力,适用性强,可靠性高。     

Real-time constrained linear discriminant analysis to target detection and classification in hyperspectral imagery

In this paper, we present a constrained linear discriminant analysis (CLDA) approach to hyperspectral image detection and classification as well as its real-time implementation. The basic idea of CLDA is to design an optimal transformation matrix which can maximize the ratio of inter-class distance to intra-class distance while imposing the constraint that different class centers after transformation are along different directions such that different classes can be better separated. The solution turns out to be a constrained version of orthogonal subspace projection (OSP) implemented with a data whitening process. The CLDA approach can be applied to solve both detection and classification problems. In particular, by introducing color for display the classification is achieved with a single classified image where a pre-assigned color is used to display a specified class. The real-time implementation is also developed to meet the requirement of on-line image analysis when the immediate data assessment is critical. The experiments using HYDICE data demonstrate the strength of CLDA approach in discriminating the targets with subtle spectral difference.     

结合遗传算法和蚁群算法的高光谱图像波段选择

随着遥感技术和成像光谱仪的发展,高光谱遥感图像的应用越来越广泛,但其自身的特点给高光谱图像的分类、识别等带来了很大的困难.如何快速地从高达数百个波段的高光谱图像中选择出具有较好分类识别能力的波段组合是亟待解决的问题.针对上述问题分析了已有的波段选择方法,提出一种结合遗传算法和蚁群算法的高光谱图像波段选择方法.该算法首先利用遗传算法以较快的寻优能力获得几组较优解,以此来初始化蚁群算法的初始信息素列表,然后用蚁群算法以较高的求精解能力获得最优解,并且在遗传算法部分中采用四进制的编码方式,使得算法编/译码简单、遗传算子操作简捷、且处理时所占空间小,同时在蚁群算法部分中巧妙地对预处理图像进行子空间划分来缩小蚂蚁搜索的范围,提高了算法的搜索效率,减小了输出波段组合的相关性和冗余度.由于该算法充分地吸取遗传算法和蚁群算法的优点、克服各自的缺陷,是一种计算耗时少、收敛性能好的波段选择方法.利用AVIRIS(airborne visible infrared imaging spectrometer)图像对提出的算法进行实验,实验结果表明,本文算法在所选波段性能和计算耗时方面都获得令人满意的效果.     

A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery

Sudden Oak Death is a new and virulent disease affecting hardwood forests in coastal California. The spatial-temporal dynamics of oak mortality at the landscape scale are crucial indicators of disease progression. Modeling disease spread requires accurate mapping of the dynamic pattern of oak mortality in time through multi-temporal image analysis. Traditional mapping approaches using per-pixel, single-date image classifications have not generated consistently satisfactory results. Incorporation of spatial-temporal contextual information can improve these results. In this paper, we propose a spatial-temporally explicit algorithm to classify individual images using the spectral and spatial-temporal information derived from multiple co-registered images. This algorithm is initialized by a spectral classification using Support Vector Machines (SVM) for each individual image. Then, a Markov Random Fields (MRF) model accounting for ecological compatibility is used to model the spatial-temporal contextual prior probabilities of images. Finally, an iterative algorithm, Iterative Conditional Mode (ICM), is used to update the classification based on the combination of the initial SVM spectral classifications and MRF spatial-temporal contextual model. The algorithm was applied to two-year (2000, 2001) ADAR (Airborne Data Acquisition and Registration) images, from which three classes (bare, dead, forest) are detected. The results showed that the proposed algorithm achieved significantly better results (Year 2000: Kappa = 0.92; Year 2001: Kappa = 0.91), compared to traditional pixel-based single-date approaches (Year 2000: Kappa = 0.67; Year 2001: Kappa = 0.66). The improvement from the contributions of spatial-temporal contextual information indicated the importance of spatial-temporal modeling in multi-temporal remote sensing in general and forest disease modeling in particular.     

改进协同表示的高光谱图像异常检测算法

针对协同表示的高光谱图像异常检测算法中双窗口中心为异常像元同时背景字典存在同种异常像元的情况,中心像元的输出较小难以与背景区分的问题,提出一种改进协同表示的高光谱图像异常检测算法。为了减小背景字典中异常像元的权重,使用背景字典原子与均值的距离调整原子的权重,从而增大上述情况下中心像元的输出。实验结果表明,提出的算法在不同双窗口下都取得了较好的检测效果,验证了算法的有效性。     

Fast real-time onboard processing of hyperspectral imagery for detection and classification

Remotely sensed hyperspectral imagery has many important applications since its high-spectral resolution enables more accurate object detection and classification. To support immediate decision-making in critical circumstances, real-time onboard implementation is greatly desired. This paper investigates real-time implementation of several popular detection and classification algorithms for image data with different formats. An effective approach to speeding up real-time implementation is proposed by using a small portion of pixels in the evaluation of data statistics. An empirical rule of an appropriate percentage of pixels to be used is investigated, which results in reduced computational complexity and simplified hardware implementation. An overall system architecture is also provided.

基于独立分量分析的高光谱遥感影像决策树分类

为解决高光谱遥感影像波段众多所带来的信息丰富与“维数灾难”间的矛盾并提高分类精度,针对传统特征选择方法信息损失大的缺陷,基于EO-1 Hyperion高光谱遥感影像,采用独立分量分析(ICA)和决策树分类(DTC)方法联合运作流程,开展影像的地物分类实验研究,提出了ICA-DTC模型。首先运用ICA方法对影像进行特征提取,并以所提取的独立分量特征及其他地理辅助要素组成分类指标集;继而选择适当的指标组合和阈值设定判别规则,建立DTC模型进行影像的地物分类;最后将分类结果与传统最大似然分类法进行比对。结果显示：从分类的总体精度看,前者可达89.34%,高出后者18.8%;从单一地物的分类精度看,前者仅水体的精度略低于后者,而其他11种地物的精度都高于后者。理论分析与实验结果均表明,ICA-DTC模型可有效提高复杂地形条件下的地物分类精度。     

A projection pursuit algorithm for anomaly detection in hyperspectral imagery

The main goal of this paper is to propose an innovative technique for anomaly detection in hyperspectral imageries. This technique allows anomalies to be identified whose signatures are spectrally distinct from their surroundings, without any a priori knowledge of the target spectral signature. It is based on an one-dimensional projection pursuit with the Legendre index as the measure of interest. The index optimization is performed with a simulated annealing over a simplex in order to bypass local optima which could be sub-optimal in certain cases. It is argued that the proposed technique could be considered as seeking a projection to depart from the normal distribution, and unfolding the outliers as a consequence. The algorithm is tested with AHS and HYDICE hyperspectral imageries, where the results show the benefits of the approach in detecting a great variety of objects whose spectral signatures have sufficient deviation from the background. The technique proves to be automatic in the sense that there is no need for parameter tuning, giving meaningful results in all cases. Even objects of sub-pixel size, which cannot be made out by the human naked eye in the original image, can be detected as anomalies. Furthermore, a comparison between the proposed approach and the popular RX technique is given. The former outperforms the latter demonstrating its ability to reduce the proportion of false alarms.     

空域协同自编码器的高光谱异常检测

目的 自编码器作为一种无监督的特征提取算法,可以在无标签的条件下学习到样本的高阶、稠密特征。然而当训练集含噪声或异常时,会迫使自编码器学习这些异常样本的特征,导致性能下降。同时,自编码器应用于高光谱图像处理时,往往会忽略掉空域信息,进一步限制了自编码器的探测性能。针对上述问题,本文提出一种基于空域协同自编码器的高光谱异常检测算法。方法 利用块图模型优良的背景抑制能力从空域角度筛选用于自编码器训练的背景样本集。自编码器采用经预筛选的训练样本集进行网络参数更新,在提升对背景样本表达能力的同时避免异常样本对探测性能的影响。为进一步将空域信息融入探测结果,利用块图模型得到的异常响应构建权重,起到突出目标并抑制背景的作用。结果 实验在3组不同尺寸的高光谱数据集上与5种代表性的高光谱异常检测算法进行比较。本文方法在3组数据集上的AUC(area under the curve)值分别为0.990 4、0.988 8和0.997 0,均高于其他算法。同时,对比了不同的训练集选择策略,与随机选取和使用全部样本进行对比。结果表明,本文基于空域响应的样本筛选方法相较对比方法具有较明显的优势。结论 提出的基...     

Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery

Exotic plant invasion is a major environmental and ecological concern and is a particular issue for Mediterranean-type ecosystems. Early detection of invasive plants is crucial for effective weed management. Several studies have explored hyperspectral imagery for mapping invasive plants with promising results. However, only a few extensive or comparative studies about image processing techniques for invasive plant detection have been reported, and even fewer studies have involved very high spatial and spectral resolution imagery. The primary goal of this study was to investigate the utility of very high spatial (0.5 m) and spectral (4 nm) resolution imagery and several classification approaches for detecting tamarisk (Tamarix spp.) infestations, the most problematic invasive plant species in the riparian habitats of southern California.Hierarchical clustering was a particularly effective and efficient statistical method for identifying wavebands and spectral transforms having the greatest discriminatory power. Products resulting from the classification of airborne hyperspectral image data varied by scene, input data type, classifier, and minimum patch size. Overall accuracy of image classification accuracy of products co-varied with commission error rates, such that products having strong agreement with reference data also had a high number of false detections. Integrating the findings from qualitative map analysis, areal proportion statistics, and object-based accuracy assessment indicates that the parallelepiped classifier with several narrow wavebands selected through hierarchical clustering yielded the most accurate and reliable tamarisk classification products.     

Comparison of bathymetry and seagrass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment

We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging (lidar) for shallow water (<2 m) bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.     

Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery

Target detection is one of the most important applications of hyperspectral imagery in the field of both civilian and military. In this letter, we firstly propose a new spectral matching method for target detection in hyperspectral imagery, which utilizes a pre-whitening procedure and defines a regularized spectral angle between the spectra of the test sample and the targets. The regularized spectral angle, which possesses explicit geometric sense in multidimensional spectral vector space, indicates a measure to make the target detection more effective. Furthermore Kernel realization of the Angle-Regularized Spectral Matching (KAR-SM, based on kernel mapping) improves detection even more. To demonstrate the detection performance of the proposed method and its kernel version, experiments are conducted on real hyperspectral images. The experimental tests show that the proposed detector outperforms the conventional spectral matched filter and its kernel version.     

局部梯度轮廓变换的高光谱异常检测

目的 高光谱异常检测由于其重要的应用价值,引起了研究人员的广泛关注,但大部分的检测算法,往往直接利用输入的高光谱遥感影像所携带的光谱信息或者空谱信息进行检测。考虑到由于成像过程的限制,如成像条件的复杂性以及光谱通道众多导致的每个通道光子数量有限等问题,所获取的高光谱遥感影像往往在一定程度上偏离真实场景,而这也制约了异常检测的精度。针对此问题,本文提出了一种局部梯度轮廓变换的高光谱遥感影像异常检测算法。方法 为了在不影响算法性能的基础上减少计算复杂度,首先选取部分可能的异常像元,只对这些局部的异常像元可能位置进行梯度轮廓变换。其次,将变换后的梯度轮廓用于指导原始高光谱遥感影像的空域增强。最后,对增强后的高光谱遥感影像进行检测。通过将局部梯度轮廓用于影像的增强,避免了成像过程中由于细节损失而造成检测精度受限的情况。结果 实验在来自4个数据集的6幅高光谱遥感影像上进行了性能验证。首先利用经典的Global-RX （Reed Xiaoli）检测算法同时检测本文算法增强后的影像和原始影像,分别取得的平均AUC （area under curve）值为0.987 1和0.933 6,本文算法带来了0.053 5的精度提升;同时,通过与其他3种预处理方法进行比较,证明了本文局部梯度轮廓变换方法的有效性;更进一步,利用基于协同表示CRD （collaborative representation-based detector）的检测器对增强后的影像和原始影像分别进行检测,分别取得的平均AUC值为0.990 7和0.977 5,检测结果再次验证了本文算法能够有效提升影像的检测精度;通过对比,实验数据表明本文所采用的局部梯度轮廓变换可减少约37.82%的时间复杂度。结论 本文算法通过将局部的梯度轮廓进行变换并用于指导原始影像的增强过程,使得影像的空间轮廓信息更为锐利,更为接近真实场景,从而获得异常检测结果的提升。     

Multivariate forest structure modelling and mapping using high resolution airborne imagery and topographic information

Remote sensing has been widely used for modelling and mapping individual forest structural attributes, such as LAI and stem density, however the development and evaluation of methods for simultaneously modelling and mapping multivariate aspects of forest structure are comparatively limited. Multivariate representation of forest structure can be used as a means to infer other environmental attributes such as biodiversity and habitat, which have often been shown to be enhanced in more structurally diverse or complex forests. Image-based modelling of multivariate forest structure is useful in developing an understanding of the associations between different aspects of vertical and horizontal structure and image characteristics. Models can also be applied spatially to all image pixels to produce maps of multivariate forest structure as an alternative to sample-based field assessment. This research used high spatial resolution multispectral airborne imagery to provide spectral, spatial, and object-based information in the development of a model of multivariate forest structure as represented by twenty-four field variables measured in plots within a temperate hardwood forest in southern Quebec, Canada. Redundancy Analysis (RDA) was used to develop a model that explained a statistically significant proportion of the variance of these structural attributes. The resulting model included image variables representing mostly within-crown and within-shadow brightness variance (texture) as well as elevation, taken from a DEM of the study area. It was applied spatially across the entire study area to produce a continuous map of predicted multivariate forest structure. Bootstrapping validation of the model provided an RMSE of 19.9%, while independent field validation of mapped areas of complex and simple structure showed accuracies of 89% and 69%, respectively. Multiscale testing using resampled imagery suggested that the methods could possibly be used with current pan-sharpened, or future sub-metre resolution, multispectral satellite imagery, which would provide much greater spatial coverage and reduced image processing compared to airborne imagery.