自1997年8月23日首颗高光谱卫星LEWIS发射以来,航天高光谱成像技术走过了15年的路程,我国在该领域的技术也取得了跨越式的发展。当前我国正按照国家中长期科技规划,努力构建下一代高分辨率对地观测系统,深入全面地总结过去若干年的航天高光谱成像技术发展情况,并分析发达国家的发展计划,对制定我国的航天高光谱成像技术发展战略具有重要意义。从当前的主流高光谱分光技术、应用情况和发展趋势三个方面进行了全面的分析总结,认为民用高光谱成像系统应该朝着宽幅、定量化和应用细分的方向发展;军用高光谱成像系统的发展重点应该是高空间分辨率、宽波段以及快速信息处理技术。

We introduce key concepts and issues including the effects of atmospheric propagation upon the data, spectral variability, mixed pixels, and the distinction between classification and detection algorithms. Detection algorithms for full pixel targets are developed using the likelihood ratio approach. Subpixel target detection, which is more challenging due to background interference, is pursued using both statistical and subspace models for the description of spectral variability. Finally, we provide some results which illustrate the performance of some detection algorithms using real hyperspectral imaging (HSI) data. Furthermore, we illustrate the potential deviation of HSI data from normality and point to some distributions that may serve in the development of algorithms with better or more robust performance. We therefore focus on detection algorithms that assume multivariate normal distribution models for HSI data

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

利用最大似然分类器对高光谱遥感图像分类时,由于波段数目多、波段间的相关度大,使协方差矩阵的行列式近似于奇异,因而导致了不合理的分类结果。本文调整了波段协方差矩阵对分类的影响,改进了最大似然分类判决函数,利用改进后的判决函数进行分类,试验证明这种方法是有效的。另外,根据图像特征和经验对图像进行波段选择,也是改善高光谱遥感分类效果的有效途径。

This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines (SVMs). First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialit...

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

超像素能够捕获图像冗余信息,降低后续处理任务复杂度,已受到了国内外研究者的日益关注。首先分析了超像素分割领域的发展现状,以基于图论的方法和基于梯度下降的方法为视角,对现有超像素分割方法进行归纳和论述。在此基础上,就目前常用的超像素分割算法进行了实验对比,分析各自的优势和不足。最后,对超像素分割技术的最新应用进行了介绍和展望。

<p>利用ALOS数据,在Definiens Developer 7软件中用分形网络演化法(FNEA)进行多级分割,获取影像对象。综合运用对象的光谱、空间特征和不同层对象之间的关系,提取了湖北省洪湖市试验区土地覆盖与土地利用信息。最后,用一种基于单层分割的面向对象分类方法和基于像素的最大似然法与这种基于多级分割的面向对象分类方法进行了对比分析。结果表明,基于多级分割的面向对象分类方法,不仅克服了基于像素的最大似然法出现的&ldquo;椒盐&rdquo;现象,在分类精度上较这两种分类方法也有大幅度的提高。</p>

Summary: A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced. A review of watersheds and related motion is first presented, and the major methods to determine watersheds are discussed. The algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using of queue of pixel. It is described in detail provided in a pseudo C language. The accuracy of this algorithm is proven to be superior to that of the existing implementations, and it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images (and even to graphs) are straightforward. The algorithm is reported to be faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for magnetic resonance (MR) imagery and for digital elevation models. An example of 3-D watershed is also provided.

针对岩屑颗粒密集和颗粒表面纹理复杂的特点,提出一种基于熵率超像素分割和区域合并的分割方法。熵率超像素分割将图像分为一系列紧凑的、具有区域一致性的区域,不仅边缘定位准确且降低图像计算的复杂度;针对存在的过分割情况,提出一种结合颜色直方图和形状信息的合并准则,进行基于RAG结构的快速区域合并,得到最后分割结果。实验结果表明,将该方法用于岩屑颗粒图像分割,能够取得较好的实验效果。

马尔可夫随机场方法是图像分割中一个极为活跃的研究方向。本文介绍了基于马尔可夫随机场模型的一般理论与图像的关系，给出它在图像分割中的通用框架：包括空域和小波域图像模型的建立、最优准则的选取、标号数的确定、图像模型参数的估计和图像分割的实现，评述了其在图像分割中的应用，展望其发展的方向。

Abstract A new method for segmentation and classification of hyperspectral images is proposed. The method is based on the construction of a minimum spanning forest (MSF) from region markers. Markers are defined automatically from classification results. For this purpose, pixelwise classification is performed, and the most reliable classified pixels are chosen as markers. Each classification-derived marker is associated with a class label. Each tree in the MSF grown from a marker forms a region in the segmentation map. By assigning a class of each marker to all the pixels within the region grown from this marker, a spectral-spatial classification map is obtained. Furthermore, the classification map is refined using the results of a pixelwise classification and a majority voting within the spatially connected regions. Experimental results are presented for three hyperspectral airborne images. The use of different dissimilarity measures for the construction of the MSF is investigated. The proposed scheme improves classification accuracies, when compared to previously proposed classification techniques, and provides accurate segmentation and classification maps.

众所周知,特征提取方法在降低计算复杂度和增加高光谱图像分类精确度方面十分有效,针对此,提出了一种优化的图像融合和迭代率波的特征提取方法.首先,将高光谱图像分割成多个相邻波段的子集;然后,通过均值法把每个子集中的各个波段融合在一起;最后,对融合后的波段进行递归滤波处理,获得分类的特征信息.结果表明,该方法展示出较高的分类精确度.

针对当前高光谱影像稀疏分类模型中光谱重构方法单一性的问题，该文将稀疏分类模型中光谱的线性重构理解为光谱间的相似性度量，进而引入其他相似性测度指标，提出多相似测度稀疏表示的高光谱影像分类模型，并给出模型的统一解算方法———一般正交匹配追踪算法；随后，考虑地物空间连续性和一致性，将多相似测度稀疏分类模型扩展到空间联合的多相似测度稀疏分类模型，提出了一般联合匹配追踪算法；最后，利用两幅标准高光谱影像数据验证了所提出的多相似测度稀疏分类模型对于高光谱影像分类的有效性和实用性。

传统高光谱图像分类方法主要使用图像的光谱特征信息,没有充分利用高光谱图像的空间特性及样本的其他信息。本文提出了一种基于空间特征与纹理信息的高光谱图像半监督分类方法。首先,将高光谱图像每一像素的光谱特征与其邻域范围内的光谱特征进行结合,得到了这一像素的空-谱特征;然后用灰度共生矩阵提取了高光谱图像的纹理特征,并与空-谱特征进行了融合;最后,用基于图的半监督分类算法进行了分类。通过在Indian Pines数据集和Pavia U数据集上进行试验,结果表明本文提出的方法能取得较高的分类结果。

基于形态学变换的遥感图像分类提取技术在影像识别中得到了广泛应 用,也取得了令人满意的效果.但在处理多光谱、高光谱影像中,由于较少考虑色差信息而使分类识别受到限制.本文在分析形态学算子特点的基础上,提出了基于 形态学变换色差的水域特征提取策略.实验表明,该算法具有明显的优越性和较强的适应性.

This letter presents a framework of composite kernel machines for enhanced classification of hyperspectral images. This novel method exploits the properties of Mercer's kernels to construct a family of composite kernels that easily combine spatial and spectral information. This framework of composite kernels demonstrates: 1) enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only: 2) flexibility to balance between the spatial and spectral information in the classifier; and 3) computational efficiency. In addition, the proposed family of kernel classifiers opens a wide field for future developments in which spatial and spectral information can be easily integrated.

In hyperspectral remote sensing image classification, multiple features, e.g., spectral, texture, and shape features, are employed to represent pixels from different perspectives. It has been widely acknowledged that properly combining multiple features always results in good classification performance. In this paper, we introduce the patch alignment framework to linearly combine multiple features in the optimal way and obtain a unified low-dimensional representation of these multiple features for subsequent classification. Each feature has its particular contribution to the unified representation determined by simultaneously optimizing the weights in the objective function. This scheme considers the specific statistical properties of each feature to achieve a physically meaningful unified low-dimensional representation of multiple features. Experiments on the classification of the hyperspectral digital imagery collection experiment and reflective optics system imaging spectrometer hyperspectral data sets suggest that this scheme is effective.

Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency.

A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse representation of an unknown pixel is expressed as a sparse vector whose nonzero entries correspond to the weights of the selected training samples. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Two different approaches are proposed to incorporate the contextual information into the sparse recovery optimization problem in order to improve the classification performance. In the first approach, an explicit smoothing constraint is imposed on the problem formulation by forcing the vector Laplacian of the reconstructed image to become zero. In this approach, the reconstructed pixel of interest has similar spectral characteristics to its four nearest neighbors. The second approach is via a joint sparsity model where hyperspectral pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few common training samples, which are weighted with a different set of coefficients for each pixel. The proposed sparsity-based algorithm is applied to several real hyperspectral images for classification. Experimental results show that our algorithm outperforms the classical supervised classifier support vector machines in most cases.

Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. A morphological profile is constructed based on the repeated use of openings and closings with a structuring element of increasing size, starting with one original image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral imagery are computed. The most significant principal components are used as base images for an extended morphological profile, i.e., a profile based on more than one original image. In experiments, two hyperspectral urban datasets are classified. The proposed method is used as a preprocessing method for a neural network classifier and compared to more conventional classification methods with different types of statistical computations and feature extraction.

The integration of spatial context in the classification of hyperspectral images is known to be an effective way in improving classification accuracy. In this paper, a novel spectral-spatial classification framework based on edge-preserving filtering is proposed. The proposed framework consists of the following three steps. First, the hyperspectral image is classified using a pixelwise classifier, e.g., the support vector machine classifier. Then, the resulting classification map is represented as multiple probability maps, and edge-preserving filtering is conducted on each probability map, with the first principal component or the first three principal components of the hyperspectral image serving as the gray or color guidance image. Finally, according to the filtered probability maps, the class of each pixel is selected based on the maximum probability. Experimental results demonstrate that the proposed edge-preserving filtering based classification method can improve the classification accuracy significantly in a very short time. Thus, it can be easily applied in real applications.

This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation with active learning, which consists of two main steps. First, we use a multinomial logistic regression (MLR) model to learn the class posterior probability distributions. This is done by using a recently introduced logistic regression via splitting and augmented Lagrangian algorithm. Second, we use the information acquired in the previous step to segment the hyperspectral image using a multilevel logistic prior that encodes the spatial information. In order to reduce the cost of acquiring large training sets, active learning is performed based on the MLR posterior probabilities. Another contribution of this paper is the introduction of a new active sampling approach, called modified breaking ties, which is able to provide an unbiased sampling. Furthermore, we have implemented our proposed method in an efficient way. For instance, in order to obtain the time-consuming maximum a posteriori segmentation, we use the -expansion min-cut-based integer optimization algorithm. The state-of-the-art performance of the proposed approach is illustrated using both simulated and real hyperspectral data sets in a number of experimental comparisons with recently introduced hyperspectral image analysis methods.