基于独立分量分析的高光谱图像降维与压缩算法

论文提出了一种基于快速独立分量分析的高光谱图像降维算法.利用虚拟维数算法估计需要保留的独立分量数目,采用非监督端元提取算法自动获取端元矢量,并对快速独立分量分析的混合矩阵进行有效初始化.采用最大噪声分离变换对原始数据进行预处理,利用快速独立分量分析从变换后的主分量中依次提取出各端元对应的独立分量,最后对各个独立分量分别实施无损压缩.实验结果表明,该算法降维后的独立分量具有较好的地物分类性能,并且可以获得较好的压缩性能.     

基于测地线距离和正交投影的端元提取算法

端元提取是高光谱遥感图像混合像元分解的关键步骤。传统线性端元提取方法忽略了像元内地物的非线性混合因素,制约了混合像元分解精度的提升。针对高光谱图像数据的非线性结构,提出一种基于测地线距离的正交投影端元提取算法,将测地线距离引入端元单体提取过程,利用正交投影方法逐个提取端元。为了降低测地线距离计算量,在端元提取前先利用自动目标生成方法和无约束最小二乘法对原始高光谱数据进行数据约减。模拟和真实高光谱图像实验表明,该方法能够表征光谱数据中非线性因素,端元提取结果优于传统自动目标生成端元提取方法。     

基于线性光谱混合模型的油菜种植面积遥感监测方法研究

利用中低分辨率卫星影像进行油菜面积提取时,需要考虑混合像元产生的影响,以提高面积提取的精度。本文以2009年湖北省潜江市油菜种植面积为例,利用中巴地球资源卫星(CBERS-02B)遥感影像,选取线性光谱混合模型进行油菜种植面积的分解计算研究,将结果与基于GVG(GPS、VIDEO、GIS)农情采样系统得到的结果进行对比分析,面积提取精度为97.43%。表明线性光谱混合模型能够高精度地提取油菜的种植面积,不失为一种很好的监测油菜种植面积方法。     

Speed-up for N-FINDR algorithm

N-FINDR is a very popular algorithm of endmember (EM) extraction for its automated property and high efficiency. Unfortunately, innumerable volume calculation, initial random selection of EMs and blind searching for EMs lead to low speed of the algorithm and limit the applications of the algorithm. So in this paper two measures are proposed to speed up the algorithm. One of the measures is substituting distance calculation for volume calculation. Thus the avoidance of volume calculation greatly decreases the computational cost. The other measure is resorting dataset in terms of pixel purity likelihood based on pixel purity index (PPI) concept. Then, initial EMs can be selected well-founded and a fast searching for EMs is achieved. Numerical experiments show that the two measures speed up the original algorithm hundreds of times as the number of EMs is more than ten.     

基于目标正交子空间投影加权的高光谱图像异常检测算法

在高光谱图像的异常目标检测问题中,针对原始数据源不能正确地表征背景数据的分布而造成虚警概率较高的现象,本文提出了一种基于目标正交子空间投影加权的KRX高光谱图像异常检测算法。该算法从背景协方差矩阵的估算角度入手,将每个像素投影到目标的正交子空间中,为每个像素自适应地赋予合适的权值,从而减小目标信息的存在对背景特性估计的影响。并用AVIRIS高光谱数据进行了仿真实验,取得了较好的检测效果。将该算法与其他算法进行了比较,结果表明,本文提出算法的检测性能明显地优于传统算法,降低了虚警概率,具有较好的检测效果。     

一种反演积雪面积的范数最小二乘算法

针对常规光谱混合分析算法在积雪面积反演中存在的端元变化误差及运算效率的问题,提出了一种范数最小二乘算法(norm least squares,NLS).为验证算法的精度和实用性,利用藏南地区的MODIS影像进行反演实验,同时采用全约束最小二乘法(fully constrained least squares,FCLS)...     

从端元选择到光谱解混的距离测算方法

提出了基于支持向量机(SVM)的单纯形增长算法(SGA)新实现方法,该方法无需降维预处理,且采用低复杂度的距离尺度代替复杂的体积尺度;证明了线性SVM与传统线性光谱混合模型(LSMM)在光谱解混中的等效性,并探索了前者在信息的扩展利用和模型的非线性推广两方面的优势.实验结果表明,基于SVM的SGA实现方法在保证选择结果不变的前提下复杂度大大降低,SVM模型下解混精度明显提高.     

基于像元纯净指数的多端元提取算法

针对传统单端元提取方法不能描述端元变异、限制混合像元分解精度的缺点,提出一种基于像元纯净指数的多端元提取算法(Multiple Endmember Extraction Algorithm Based on Pixel Purity Index,PPI-MEE)。首先将图像划分为不重叠的图像块,并分别利用改进的PPI算法提取候选端元集,然后利用候选端元的邻域像元光谱信息对候选端元进行优化和精选。最后,对优化精选后的端元集分类得到每类地物的多端元光谱集。仿真数据和真实高光谱数据的实验结果表明,提出的多端元提取策略具有表征遥感图像中端元光谱变异的能力,能够提高端元提取精度和混合像元分解精度。     

Integration of spatial-spectral information for the improved extraction of endmembers

Spectral-based image endmember extraction methods hinge on the ability to discriminate between pixels based on spectral characteristics alone. Endmembers with distinct spectral features (high spectral contrast) are easy to select, whereas those with minimal unique spectral information (low spectral contrast) are more problematic. Spectral contrast, however, is dependent on the endmember assemblage, such that as the assemblage changes so does the “relative” spectral contrast of each endmember to all other endmembers. It is then possible for an endmember to have low spectral contrast with respect to the full image, but have high spectral contrast within a subset of the image. The spatial-spectral endmember extraction tool (SSEE) works by analyzing a scene in parts (subsets), such that we increase the spectral contrast of low contrast endmembers, thus improving the potential for these endmembers to be selected. The SSEE method comprises three main steps: 1) application of singular value decomposition (SVD) to determine a set of basis vectors that describe most of the spectral variance for subsets of the image; 2) projection of the full image data set onto the locally defined basis vectors to determine a set of candidate endmember pixels; and, 3) imposing spatial constraints for averaging spectrally similar endmembers, allowing for separation of endmembers that are spectrally similar, but spatially independent. The SSEE method is applied to two real hyperspectral data sets to demonstrate the effects of imposing spatial constraints on the selection of endmembers. The results show that the SSEE method is an effective approach to extracting image endmembers. Specific improvements include the extraction of physically meaningful, low contrast endmembers that occupy unique image regions.     

基于高精度端元的混合像元线性分解模型研究

从理论上阐述了混合像元的产生机理和混合像元线性分解模型,研制了一套地面成像光谱实验系统,直接获取了高精度的端元光谱数据,通过像元合并的方式构造混合像元,针对不同丰度的非立体空间混合像元端元,分析了根据线性分解模型加权计算得到的光谱与实际混合光谱的误差,进而论证了线性分解模型的精度.