草场资源分类专家系统的研究

在分析了基于光谱特征的统计模式识别方法用于遥感图像的计算机分类的不足之后，探讨了进一步提高遥感图像分类结果精度及可靠性的途径，指出了在遥感图像的计算机自动分类过程中，综合利用遥感图像多光谱特片及光谱特征以外的辅助信息对遥感图像进行分类是解决上述问题的有效方法，并通过笔者研制的草场资源分类专家系统ＧＥＳ说明了专家系统技术用于遥感图像分类能够有效地解决分类过程中综合利用各种辅助信息的问题。     

IMD法在多光谱图像分类中的应用

数学形态学着重于分析和处理图像的几何结构信息。根据这一原理，在图像分类预处理过程中，对各种地物区域的几何结构特点进行分析，构造相应的结构元素，然后对图像进行形态迭代分解法(IMD，Iterative Morphological Decompostion)变换。在变换结果中，地物区域的灰度信息进行了归一化处理，同时区域的几何结构特点和独立地物得到了有效保留。将多光谱图像的IMD变换结果进行分类实验的结果表明，该方法可以有效提高多光谱图像分类的精度和效率，具有较强的适用性。     

高光谱图像滚动引导递归滤波与地物分类

高光谱图像类内光谱变化较大,"同物异谱"现象普遍存在。利用原始地物光谱特征进行分类精度较低而且分类结果图中存在"椒盐现象"。为了获得好的分类结果,必须充分利用高光谱图像的光谱信息和空间信息,减少类内的光谱变化,并扩大类别间的光谱差异。为此,提出一种滚动引导递归滤波的高光谱图像光谱—空间分类方法。首先,利用主成分分析对高光谱图像进行降维;然后,利用高斯滤波对输入图像进行模糊化,消除图像中的噪声和小尺度结构;接下来,将模糊化后的图像作为引导图像,对输入图像进行边缘保持递归滤波,输出结果作为新的引导图像,重复迭代这个过程直至大尺度边缘被恢复;最后,利用提取的特征波段和支持向量机对高光谱图像进行分类。在两个真实高光谱数据集上进行了分类实验,结果表明本文方法的分类精度优于其他的高光谱图像分类方法。在训练样本极少的情况下,本文方法也能获得较高的分类精度。     

一种结合颜色特征的PolSAR图像分类方法

为了提出一种颜色特征与极化特征相结合的极化SAR图像分类方法,首先,通过极化目标分解得到极化特征向量;然后,采用最佳指数模型方法生成极化SAR的假彩色合成图像,并提取颜色特征向量;最后,将这2种特征组成综合特征向量,利用SVM方法进行分类。利用Radar Sat-2的Pol SAR数据进行了SAR图像分类实验,并对分类结果进行定性和定量比较分析。实验结果表明,颜色特征的加入能有效提高极化SAR图像的分类精度。     

草场资源分类专家系统的研究

在分析了基于光谱特征的统计模式识别方法用于遥感图像的计算机分类的不足之后，探讨了进一步提高遥感图像分类结果精度及可靠性的途径，指出了在遥感图像的计算机自动分类过程中，综合利用遥感图像多光谱特征及光谱特征以外的辅助信息对遥感图像进行分类是解决上述问题的有效方法，并通过笔者研制的草场资源分类专家系统ＧＥＳ（ＧｒａｓｓｌａｎｄＲｅｓｏｕｒｃｅｓＣｌａｓｓｉｆｉｃａｔｉｏｎＥｘｐｅｒｔＳｙｓｔｅｍ）说明了专家系统技术用于遥感图像分类能够有效地解决分类过程中综合利用各种辅助信息的问题。     

基于空间特征与纹理信息的高光谱图像半监督分类

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PaviaU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。     

面向城市地物分类的SAR图像纹理特征提取与分析

合成孔径雷达(SAR)图像含有丰富的纹理信息,特别是进行城市地物分类时,纹理特征对于图像的解译具有重要的意义。本文对基于灰度共生矩阵和Gabor变换两种纹理特征提取方法进行了研究,将灰度和不同纹理特征组合应用于SAR图像城市地物分类,并以ALOS PALSAR影像为数据源进行了实验。通过对不同分类结果进行定性和定量分析,结果表明,引入纹理特征后的SAR图像分类结果要优于无纹理信息参与的分类结果,基于不同纹理特征组合的SAR图像分类结果要优于基于单一纹理特征的分类结果。     

面向城市地物分类的 SAR 图像纹理特征提取与分析

合成孔径雷达（ SAR）图像含有丰富的纹理信息,特别是进行城市地物分类时,纹理特征对于图像的解译具有重要的意义。本文对基于灰度共生矩阵和Gabor变换两种纹理特征提取方法进行了研究,将灰度和不同纹理特征组合应用于SAR图像城市地物分类,并以ALOS PALSAR影像为数据源进行了实验。通过对不同分类结果进行定性和定量分析,结果表明,引入纹理特征后的SAR图像分类结果要优于无纹理信息参与的分类结果,基于不同纹理特征组合的SAR图像分类结果要优于基于单一纹理特征的分类结果。     

县级耕地遥感动态监测方法研究

利用不同时相的TM数据，采用遥感数字图像处理技术，首先对TM图像进行几何精校正，然后在图像增强处理的基础上，分别采用图像复合分类、图像比值和分类结果数学运算3种方法实现耕地遥感动态监测。在耕地信息准确提取的前提下，分类结果数学运算不失为耕地监测的一种有效方法。     

多分类器联合监督分类方法研究

针对遥感图像监督分类方法适用范围不同且分类机制各有优劣的特点,本文提出将最大似然法与最小距离法结合的监督分类法。对eTM 影像进行分类,结果表明,与单一分类器的分类结果相比,分类器结合的监督分类技术能有效提高遥感图像专题信息提取的精度。     

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.     

Cross-sensor calibration between Ikonos and Landsat ETM+ for spectral mixture analysis

Spectral mixture analysis is an algorithm that is developed to overcome the weakness in traditional land-use/land-cover (LULC) classification where each picture element (pixel) from remote sensing is assigned to one and only one LULC type. In reality, a remotely sensed signal from a pixel is often a spectral mixture from several LULC types. Spectral mixture analysis can derive subpixel proportions for the endmembers from remotely sensed data. However, one frequently faces the problem in determining the spectral signatures for the endmembers. This study provides a cross-sensor calibration algorithm that enables us to obtain the endmember signatures from an Ikonos multispectral image for spectral mixture analysis using Landsat ETM+ images. The calibration algorithm first converts the raw digital numbers from both sensors into at-satellite reflectance. Then, the Ikonos at-satellite reflectance image is degraded to match the spatial resolution of the Landsat ETM+ image. The histograms at the same spatial resolution from the two images are matched, and the signatures from the pure pixels in the Ikonos image are used as the endmember signatures. Validation of the spectral mixture analysis indicates that the simple algorithm works effectively. The algorithm is not limited to Ikonos and Landsat sensors. It is, in general, applicable to spectral mixture analysis where a high spatial resolution sensor and a low spatial resolution sensor with similar spectral resolutions are available as long as images collected by the two sensors are close in time over the same place.     

变端元混合像元分解冬小麦种植面积测量方法

针对线性混合像元分解(Linear Spectral Unmixing,LSU)在端元(Endmember)个数不变情况下常会出现端元分解过剩现象导致分解结果精度不高的问题,以地物分布的聚集性特征为基础,提出了基于格网的变端元线性混合像元分解(Dynamic Endmember LSU,DELSU)方法.以冬小麦为研究...     

Recent advances in hyperspectral image processing

Hyperspectral images (HSI) provide a new way to exploit the internal physical composition of the land scene. The basic platform for acquiring HSI data-sets are airborne or spaceborne spectral imaging. Retrieving useful information from hyperspectral images can be grouped into four categories. (1) Classification: Hyperspectral images provide so much spectral and spatial information that remotely sensed image classification has become a complex task. (2) Endmember extraction and spectral unmixing: Among images, only HSI have a complete model to represent the internal structure of each pixel where the endmembers are the elements. Identification of endmembers from HSI thus becomes the foremost step in interpretation of each pixel. With proper endmembers, the corresponding abundances can also be exactly calculated. (3) Target detection: Another practical problem is how to determine the existence of certain resolved or full pixel objects from a complex background. Constructing a reliable rule for separating target signals from all the other background signals, even in the case of low target occurrence and high spectral variation, comprises the key to this problem. (4) Change detection: Although change detection is not a new problem, detecting changes from hyperspectral images has brought new challenges, since the spectral bands are so many, accurate band-to-band correspondences and minor changes in subclass land objects can be depicted in HSI. In this paper, the basic theory and the most canonical works are discussed, along with the most recent advances in each aspect of hyperspectral image processing.     

基于PCM改进算法的遥感混合像元模拟分析

混合像元的存在是影响遥感图像分类精度的主要原因,模糊分类是进行混合像元分解的重要方法,其效果的好坏取决于各像元分类后对各类别的隶属度值能否准确地反映像元的类别组成。当非监督分类中的聚类数目与实际类别数目不符,或者监督分类中训练样本存在未训练类别时,常用的模糊c-均值(FCM)方法的效果将大大降低,而可能性c-均值(PCM)方法则可以解决这个问题。该文提出了基于PCM算法的遥感图像混合像元分解方法,并用监督分类方法实例说明PCM方法的优越性。     

基于单次散射反照率的矿物高光谱稀疏解混

矿物的混合多属于致密型混合,在可见光—短波红外波段的混合呈现非线性特征,同时由于矿物混合的复杂性以及图像中完全纯净的像元可能不存在等原因,使得从图像上提取端元具有较大不确定性。本文根据矿物单次散射反照率的线性可加性,提出一种基于矿物单次散射反照率光谱库的稀疏解混算法,利用Hapke模型将矿物反射率转换成矿物单次散射反照率,构建矿物单次散射反照率光谱库,以半监督的方式通过稀疏回归的方法从光谱库中寻找最优端元组合,并估算混合像元中各端元的丰度。利用RELAB矿物混合光谱库进行算法验证,结果表明,丰度反演的平均绝对误差为3.12%;将本文方法应用于美国内华达州铜矿区的AVIRIS高光谱图像数据,所得丰度图与美国地质勘探局USGS矿物识别结果具有较好的一致性。本文算法不需要从图像提取端元,并且考虑到了矿物的非线性混合特征,能够得到较高的反演精度,在近地行星和卫星表面岩矿成分的探测等领域具有较好的应用前景。     

结合空间信息选取最优端元组合的混合像元分解

传统的混合像元分解算法认为每个像元都包含图像中所能提取的全部端元组分,但这并不符合实际情况。实际上图像中大多数混合像元仅由少部分端元混合而成。由于端元提取精度及噪声的影响,采用全部端元对混合像元进行分解,会使得混合像元中实际并不存在的端元的丰度估计值不为零,分解结果存在较大误差。由于混合像元大多存在于不同地物的交界处,基于此,本文提出了一种结合图像的空间信息选取混合像元最优端元子集的方法。利用一个空间结构元素,从混合像元的附近邻域开始搜索,将搜索到的纯净像元光谱与所提取的图像端元光谱进行对比,并确定混合像元的端元子集进行分解。根据RMSE大小和变化情况,逐步扩大结构元素的大小,不断调整搜索范围,直至得到最优端元组合。模拟数据和真实数据的试验结果表明,该方法相比传统的全端元光谱分解方法,在总体上获得了更好的分解效果。     

高光谱端元自动提取的迭代分解方法

混合像元线性分解技术是进行高光谱影像处理的常用方法,应用这种方法的一个主要问题是难以有效、自动地确定影像的端元光谱。利用非监督的方法快速自动提取高光谱遥感图像的端元光谱是解决这个问题的主要技术手段。根据迭代误差分析思路,通过对线性混合像元模型分解的误差传播分析后,得到了端元选择的约束条件。结合端元存在的空间信息,自动提取出端元光谱并进行了混合像元分解。利用不同地区、不同传感器的高光谱数据实例测试了该文的方法,分析和讨论了选择迭代初始值与参数阈值的敏感性问题。研究结果表明此方法可以自动提取端元光谱,并且精度较高。     

Independent Base Vector Representation to Address Endmember Variability in Hyperspectral Unmixing

The normal compositional model (NCM) is a well-known and powerful model in hyperspectral unmixing which represents endmembers as independent Gaussian vectors to capture endmember variability. However, the assumption of independent endmembers diminishes the model accuracy because the high degree of correlation between endmembers of a scene and identical sources of variability demonstrate that the endmembers are dependent. This paper proposes a new hyperspectral unmixing algorithm which represents endmembers using dependent Gaussian vectors to estimate abundance fractions. To overcome the higher complexity caused by dependence assumption, this algorithm introduces new independent Gaussian vectors named Base Vectors to represent different endmembers by a weighted linear combination. Also, the proposed unmixing algorithm uses maximum likelihood method to estimate weight coefficients of Base Vectors which are used to represent mixed pixel. Finally, abundance estimation can be done using the new representation for endmembers and mixed pixel. The proposed algorithm is evaluated and compared with other state-of-the-art unmixing algorithms using simulated and real hyperspectral images. Experimental results demonstrate that the proposed unmixing algorithm can unmix pixels composed of correlated endmembers in hyperspectral images in the presence of spectral variability more accurately than previous methods.     

Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis

Development of salt-affected soils in the irrigated lands of arid and semi-arid region is major cause of land degradation. Hyperion hyperspectral remote sensing data (EO-1) was used in the present study for characterization and mapping of salt-affected soils in a part of irrigation command area of Indo-Gangetic alluvial plains. Linear spectral mixture analysis approach was used to map various categories of salt affected soils represented by spectral endmembers of slightly, moderately and highly salt-affected soils. These endmembers were related to surface expression of various categories of salt-affected soils in the area. The endmembers were selected by performing minimum noise fraction (MNF) transformation and pixel purity index (PPI) on Hyperion (EO-1) data with reference to high resolution LISS IV data and field data. The results showed that various severity classes of salt-affected soils could be reliably mapped using linear spectral unmixing analysis. A low RMSE value (0.0193) over the image was obtained that revealed a good fit of the model in identification and classification of endmembers of various severities of salt affected soils. The overall classification accuracies for slight, moderate and highly salt-affected soils were estimated of 78.57, 79.81 and 84.43% respectively.