基于不规则尺度区域光谱信息的高光谱图像亚像元定位（英文）

亚像元定位技术可以分析混合像元,并实现从丰度图像到亚像元级精细土地覆盖定位图像的转换。然而,传统的亚像元定位方法所使用的光谱信息通常在指定的矩形局部窗口中构造,并且很少使用所有波段的光谱信息,影响了亚像元定位的性能。为了解决这一问题,本文提出了一种基于不规则尺度区域光谱信息的高光谱图像亚像元定位方法 （SIISA）。在三幅遥感图像上的实验结果表明,所提出的SIISA优于现有的亚像元定位方法。     

基于端元字典稀疏解混的高光谱图像亚像元定位

针对高光谱图像中普遍存在的混合像元中各端元空间分布定位困难的问题,文中提出一种基于K-SVD的光谱解混算法,利用其解混结果进行亚像元定位。算法首先通过KNN分类来区分待处理图像中的混合像元和纯像元,然后借鉴基于冗余字典的稀疏分解相关理论,以标准光谱库为基础,通过基于K-SVD的字典训练算法训练产生最具代表性的地物光谱曲线,构建端元冗余字典,通过基于K-SVD的稀疏分解算法实现各端元丰度的求解。最后利用求得的丰度系数在两种空间性相关性约束下进行亚像元定位。实验结果表明,采用该算法进行模拟数据和真实数据的亚像元的定位可以取得不错的定位结果。     

一种新型基于利用全色锐化技术的插值高光谱图像亚像元定位（英文）

利用全色锐化技术提出了一种新型基于插值的高光谱图像亚像元定位方法。在该方法中,在现有的基于插值的亚像元定位方法处理路径中加入一条新的处理路径。首先,在新的处理路径中利用全色锐化技术对原始粗高光谱图像的空间分辨率进行改进,通过对改进后的图像进行光谱解混得到新型精细丰度图像。其次,将新路径下产生的新型精细丰度图像与现有路径下的精细丰度图像进行融合,得到具有更多空间-光谱信息的更精细丰度图像。最后,根据更细分数图像的预测值,类别分配方法给每个亚像元分配类标签,得到最终的定位结果。实验结果表明,该方法比现有的基于插值的亚像元定位方法产生具有更高的定位精度。     

基于扩展数学形态学的高光谱亚像元目标检测

提出了一种基于扩展数学形态学和光谱角度匹配相结合的高光谱亚像元目标检测算法。在目标与背景未知的情况下,同时利用光谱和空间信息实现目标的定位与检测,实现高光谱亚像元目标的检测识别。通过扩展的形态学膨胀和腐蚀运算实现端元提取,采用光谱角度匹配算法进行感兴趣目标的检测识别。算法性能通过AVIRIS数据进行评价,与仅利用光谱角度匹配的算法和RX异常检测算法进行比较。实验证明,所提出的算法性能优于其他两种算法,具有低虚警率的亚像元目标检测结果。     

基于两帧图像"亚像元"技术的插值方法研究

分析了两帧图像“亚像元”法的图像数据特征、插值处理的可行性以及提高图像空间分辨率的合理性。推导出三种插值方法——双线性插值、加权插值和线性代数插值的计算公式。在Matlab下进行模拟“亚像元”技术的插值计算,计算结果表明图像分辨率确能提高到“半个探测器像元”的空间分辨率。并对三种插值方法进行了比较。     

高光谱图像混合像元分解算法

传统的高光谱图像混合像元分解技术包括端元提取和估计每个端元的混合比例.虽然很多模型都能得到可以接受的解混结果,但是一些未知端元的存在使得结果在包含未知端元的像素点处出现偏差.因此,提出了一种基于支持向量数据描述的高光谱图像混合像元分解算法.首先高光谱图像数据被分成类内和类外两部分,类内是完全由已知端元数据混合的像素点,而类外数据是包含未知端元的像素点.两类数据交界处被认为是已知端元和未知端元混合的数据.然后再对这些像素点进行混合像元分解,分别对仿真数据和真实高光谱图像进行实验.结果表明该算法可以有效地解决因存在未知端元对解混精度的影响,而且能给出未知端元的解混分量.该方法的解混结果几乎不受未知端元的影响,优于直接解混结果     

一种结合结构与能量信息的全色与多光谱图像融合方法

分量替换是遥感图像融合中的一种经典方法,其具有良好的空间保真度,但容易产生光谱失真,为此本文提出一种结合结构与能量信息的全色与多光谱图像融合方法。方法首先通过超球面颜色空间变换分解多光谱图像的空间和光谱信息。其次,通过联合双边滤波引入了两层分解方案。然后,将全色图像和强度分量分解为结构层和能量层。最后,提出结构层通过邻域空间频率策略融合,强度分量的纯能量层用作预融合图像的能量层。强度分量定义颜色的强度,通过将预融合结构层与强度分量的能量层结合,可以有效地结合源图像的空间和光谱信息,从而减少全色锐化图像的光谱失真。本文在Pléiades和QuickBird数据集上进行大量实验,并对实验结果进行定性和定量分析,结果表明所提方法与现有先进方法相比具备一定优越性。     

多偏移遥感图像的BP神经网络亚像元定位

提出了一种借助多偏移遥感图像来改进基于BP神经网络(BPNN)的亚像元定位新方法.不同于原BPNN方法使用单幅低空间分辨率观测图像,新方法利用多幅带有亚像元偏移的低空间分辨图像来确定亚像元属于各类的概率,然后根据概率值和地物覆盖比例确定亚像元类别,以降低BPNN定位模型中的不确定性和误差.实验表明,提出方法在视觉和定量评价上,均能获得更高精度的亚像元定位结果,验证了提出方法的有效性.     

基于Fisher判别零空间的高光谱图像混合像元分解

传统的光谱混合分析方法假设每个端元必须具有完全稳定的光谱特性,而在实际问题中同类地物的端元光谱往 往存在着差异。为了有效地抑制同物异谱对混合像元分解的影响,本文提出一种基于Fisher判别零空间的高光谱遥感图像混合像元分 解算法。Fisher判别零空间方法通过对高光谱图像数据进行线性变换,使得变换后的数据中同一端元内的光谱差异减小为零,而不同 端元间的光谱差异尽可能地增大。利用变换后的光谱数据对混合像元进行分解就可以较大程度地减少同物异谱现象对分解结果的影响。 对模拟高光谱图像数据以及Indiana地区和Cuprite地区的实际AVIRIS数据的解混结果表明,用Fisher判别零空间方法处理混合像元分 解问题,可以得到较高的分解精度。     

基于独立分量分析的高光谱遥感图像混合像元盲分解

高光谱遥感图像中各端元的分布不是相互独立的,传统基于独立分量分析的分解方法,只能先提取混合像元中的端元,后解混丰度,具有较高的统计不变性,盲分解效果差。基于此在独立分量分析方法中添加丰度非负约束和丰度和为一约束条件,使该方法能降低传统方法的统计不变性,通过变换主成分中心化处理原始高光谱遥感图像数据,降低波段数据之间存在的相关性;采用牛顿迭代法多次分解迭代高光谱遥感图像数据获取多个解混矩阵,通过正交化投影求解多个解混矩阵,初始化处理多个解混矩阵后,对其进行归一化处理,当临近两个矩阵值之差绝对值无限趋于零时,能获取最佳解混矩阵,采用该矩阵同步分解高光谱遥感图像混合图像的端元光谱矩阵和丰度向量,完成高光谱遥感图像混合像元的盲分解。经过实验分析发现在信噪比为15dB时,该方法分解高光谱遥感图像端元均方根误差和平均光谱角距离误差,最小值分别是0.07%和0.02%,且误差变化幅度小,即该方法分解效果较好。     

采用MTF定量评估CCD错位成像的成像质量

为提高CCD错位成像系统成像质量,提出用MTF方法定量评估CCD错位成像的成像质量,推导了CCD错位成像两种模式的MTF,取人眼能分辨的最低对比度0.05为阈值,分析表明,交错采样和四点采样模式的理论极限分辨率分别提高到1.4倍和1.86倍,奈奎斯特频率处MTF值分别提高了0.110 6（27%）和0.167 9（41%）。用空间频率范围（0,0.5）内MTFA评估CCD成像质量,结果表明,交错采样模式和四点采样模式成像质量均优于CCD普通模式,且四点采样模式比交错采样模式成像质量进一步提高。建立了Matlab仿真CCD错位成像的数学模型,鉴别率板仿真结果验证了应用MTF定量评估CCD错位成像系统成像质量的正确性。     

基于约束空间光谱联合的亚像素定位方法北大核心CSCD

针对高光谱遥感图像,提出了一种约束空间光谱的亚像素定位方法。传统的亚像素定位方法以解混的结果作为输入,可能无法充分利用高光谱图像丰富的光谱信息。本文所提出的基于约束空间光谱联合的亚像素定位方法(constraint spatial-spectral subpixel mapping,CSSSM),利用下采样将像素丰度与亚像素丰度显式联系起来,代入线性解混模型得到亚像素丰度求解的新模型。在求解过程中,通过添加稀疏性约束与平滑性约束,以限制亚像素丰度的解空间,亚像素丰度求解更精确。其中,针对亚像素丰度稀疏性先验采用重加权1范数作为新的约束,并自适应地更新权重;针对亚像素丰度空间先验信息则采用全变分(total variational,TV)正则化作为约束,然后使用乘法迭代算法求解亚像素丰度,最后利用赢者通吃的策略进行类别确定。在两个合成数据集上进行了实验,结果表明,本方法能够进一步提高亚像素定位的精度。     

MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor

This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchomatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate observation model relating the "true" scene with the low-resolutions observations. Experimental results with hyperspectral data derived from the airborne visible-infrared imaging spectrometer are presented to demonstrate the efficacy of the proposed estimator.     

A novel method for progressive image transmission using blocked wavelets

The techniques of progressive image transmission (PIT) divide image delivery into several phases. PIT's main objective is to efficiently and effectively provide an approximate reconstruction of the original image in each phase. Therefore, this study proposes the blocked wavelet progressive image transmission (BWPIT) method based on the wavelet transformation and the spatial similarity of pixels, to reduce the bit-rate and increase the image quality in an early phase of PIT. Experimental results show that the transmission bit-rate and the image quality of BWPIT are significantly better than those of bit-plane method (BPM), improved bit-plane method (IBPM), and wavelet-based progressive image transmission (WbPIT) method in each early phase.     

A novel approach using structure tensor for medical image fusion

Medical image fusion has been shown to be effective in supporting clinicians make better clinical diagnoses. Although many algorithms have been proposed for synthesis, they still have certain limitations. Some limitations can be seen as the synthesized image is reduced in contrast or details are not preserved. In this paper, we propose an image fusion algorithm to solve the problems mentioned above. Firstly, an image decomposition method is proposed to decompose the image into two components. This method is based on the Gaussian filter and the Weighted mean curvature filter. Secondly, a fusion method for high-frequency components is based on local energy function using Structure tensor saliency. Finally, we create an adaptive fusion rule using the Marine Predators Algorithm optimization method to fuse low-frequency components. Five latest algorithms and five evaluation indexes have been used to test the proposed algorithm’s effectiveness. The obtained experimental results show that the composite image is significantly improved in quality as well as well preserved the information from the input image.

     

Kernel-based methods for hyperspectral image classification

This paper presents the framework of kernel-based methods in the context of hyperspectral image classification, illustrating from a general viewpoint the main characteristics of different kernel-based approaches and analyzing their properties in the hyperspectral domain. In particular, we assess performance of regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVMs), kernel Fisher discriminant (KFD) analysis, and regularized AdaBoost (Reg-AB). The novelty of this work consists in: 1) introducing Reg-RBFNN and Reg-AB for hyperspectral image classification; 2) comparing kernel-based methods by taking into account the peculiarities of hyperspectral images; and 3) clarifying their theoretical relationships. To these purposes, we focus on the accuracy of methods when working in noisy environments, high input dimension, and limited training sets. In addition, some other important issues are discussed, such as the sparsity of the solutions, the computational burden, and the capability of the methods to provide outputs that can be directly interpreted as probabilities.     

Perceptual-based image fusion for hyperspectral data

Three hierarchical multiresolution image fusion techniques are implemented and tested using image data from the Airborne Visual/Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The methods presented focus on combining multiple images from the AVIRIS sensor into a smaller subset of images white maintaining the visual information necessary for human analysis. Two of the techniques are published algorithms that were originally designed to combine images from multiple sensors, but are shown to work well on multiple images from the same sensor. The third method presented was developed specifically to fuse hyperspectral images for visual analysis. This new method uses the spatial frequency response (contrast sensitivity) of the human visual system to determine which features in the input images need to be preserved in the composite image(s) thus ensuring the composite image maintains the visually relevant features from each input image. The image fusion algorithms are analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor. After analyzing the signal-to-noise ratios and visual aesthetics of the fused images, contrast sensitivity based fusion is shown to provide excellent fusion results and, in every case, outperformed the other two methods     

Signal processing for hyperspectral image exploitation

Electro-optical remote sensing involves the acquisition of information about an object or scene without coming into physical contact with it. This is achieved by exploiting the fact that the materials comprising the various objects in a scene reflect, absorb, and emit electromagnetic radiation in ways characteristic of their molecular composition and shape. If the radiation arriving at the sensor is measured at each wavelength over a sufficiently broad spectral band, the resulting spectral signature, or simply spectrum, can be used (in principle) to uniquely characterize and identify any given material. An important function of hyperspectral signal processing is to eliminate the redundancy in the spectral and spatial sample data while preserving the high-quality features needed for detection, discrimination, and classification. This dimensionality reduction is implemented in a scene-dependent (adaptive) manner and may be implemented as a distinct step in the processing or as an integral part of the overall algorithm. The most widely used algorithm for dimensionality reduction is principal component analysis (PCA) or, equivalently, Karhunen-Loeve transformation     

Multi-spectral and hyperspectral image fusion using 3-D wavelet transform

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspeetral image. According to the characteristics and 3-Dimensional （3-D） feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures： Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling （RIBSR）method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution （A＆S） rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A＆S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.     

Representative band selection for hyperspectral image classification

High dimensional curse for hyperspectral images is one major challenge in image classification. In this work, we introduce a novel spectral band selection method by representative band mining. In the proposed method, the distance between two spectral bands is measured by using disjoint information. For band selection, all spectral bands are first grouped into clusters, and representative bands are selected from these clusters. Different from existing clustering-based band selection methods which select bands from each cluster individually, the proposed method aims to select representative bands simultaneously by exploring the relationship among all band clusters. The optimal representative band selection is based on the criteria of minimizing the distance inside each cluster and maximizing the distance among different representative bands. These selected bands can be further applied in hyperspectral image classification. Experiments are conducted on the 92AV3C Indian Pine data set. Experimental results show that the disjoint information-based spectral band distance measure is effective and the proposed representative band selection approach outperforms state-of-the-art methods for high dimensional image classification.