高光谱激光雷达谱位合一的角度效应分析

高光谱激光雷达以其谱位合一的技术优势为实现超三维精准遥感观测提供了可行途径,因此成为当前激光雷达与高光谱遥感领域共同大力推进的前沿发展方向。目前已有多型原型系统研发出来并得到了原理性验证,然而针对其数据处理核心环节问题的基础技术仍较为欠缺。典型问题之一是不同波段回波信号受激光入射角度的影响,该角度效应限制了高光谱激光雷达实现高性能遥感。以芬兰空间信息研究所高光谱激光雷达原型系统扫描桦树树干为例探讨了该角度效应,发现了不同激光波段对不同入射角度的回波强度响应模式,推导出了角度效应的基本规律及其精细尺度的统计规律,为后续该方向的系统研发、数据处理及信息提取等提供了可借鉴的底层机理与技术基础。     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.     

Application of Vis–NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles

Fresh and frozen-thawed (F-T) pork meats were classified by Vis–NIR hyperspectral imaging. Eight optimal wavelengths (624, 673, 460, 588, 583, 448, 552 and 609 nm) were selected by successive projections algorithm (SPA). The first three principal components (PCs) obtained by principal component analysis (PCA) accounted for over 99.98% of variance. Gray-level-gradient co-occurrence matrix (GLGCM) was applied to extract 45 textural features from the PC images. The correct classification rate (CCR) was employed to evaluate the performance of the partial least squares-discriminate analysis (PLS-DA) models, by using (A) the reflected spectra at full wavelengths and (B) those at the optimal wavelengths, (C) the extracted textures based on the PC images, and (D) the fused variables combining spectra at the optimal wavelengths and textures. The results showed that the best CCR of 97.73% was achieved by applying (D), confirming the high potential of textures for fresh and F-T meat discrimination.     

Mapping nonnative plants using hyperspectral imagery

Nonnative plant species are causing enormous ecological and environmental impacts from local to global scale. Remote sensing images have had mixed success in providing spatial information on land cover characteristics to land managers that increase effective management of invasions into native habitats. However, there has been limited evaluation of the use of hyperspectral data and processing techniques for mapping specific invasive species based on their spectral characteristics. This research evaluated three different methods of processing hyperspectral imagery: minimum noise fraction (MNF), continuum removal, and band ratio indices for mapping iceplant (Carpobrotus edulis) and jubata grass (Cortaderia jubata) in California's coastal habitat. Validation with field sampling data showed high mapping accuracies for all methods for identifying presence or absence of iceplant (97%), with the MNF procedure producing the highest accuracy (55%) when the classes were divided into four different densities of iceplant.     

Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region

The objective of this research is to develop a non-destructive method for predicting cooked beef tenderness using optical scattering of light on fresh beef muscle tissue. A hyperspectral imaging system (λ = 496–1,036 nm) that consists of a CCD camera and an imaging spectrograph, was used to acquire beef steak images. The hyperspectral image consisted of 120 bands with spectral intervals of 4.54 nm. Sixty-one fresh beef steaks, including 44 strip loin and 17 tenderloin cuts, were collected. After imaging, the steaks were cooked and Warner-Bratzler shear (WBS) force values were collected as tenderness references. The optical scattering profiles were derived from the hyperspectral images and fitted to the modified Lorentzian function. Parameters, such as the peak height, full scattering width at half maximum (FWHM), and the slope around the FWHM were determined at each wavelength. Stepwise regression was used to identify 7 key wavelengths and parameters. The parameters were then used to predict the WBS scores. The model was able to predict WBS scores with an R = 0.67. Optical scattering implemented with hyperspectral imaging shows limited success for predicting current status of tenderness in beef steak.     

Target detection based on a dynamic subspace

For hyperspectral target detection, it is usually the case that only part of the targets pixels can be used as target signatures, so can we use them to construct the most proper background subspace for detecting all the probable targets? In this paper, a dynamic subspace detection (DSD) method which establishes a multiple detection framework is proposed. In each detection procedure, blocks of pixels are calculated by the random selection and the succeeding detection performance distribution analysis. Manifold analysis is further used to eliminate the probable anomalous pixels and purify the subspace datasets, and the remaining pixels construct the subspace for each detection procedure. The final detection results are then enhanced by the fusion of target occurrence frequencies in all the detection procedures. Experiments with both synthetic and real hyperspectral images (HSI) evaluate the validation of our proposed DSD method by using several different state-of-the-art methods as the basic detectors. With several other single detectors and multiple detection methods as comparable methods, improved receiver operating characteristic curves and better separability between targets and backgrounds by the DSD methods are illustrated. The DSD methods also perform well with the covariance-based detectors, showing their efficiency in selecting covariance information for detection.     

基于Fisher字典学习稀疏表示的高光谱图像分类

近年基于稀疏表示的分类框架(Sparse Representation based Classification,SRC)在计算机视觉和模式识别领域取得了巨大成功,高光谱图像解译也逐渐引入稀疏表示方法。针对基于SRC的高光谱图像分类算法随机抽取训练样本构成字典较难捕获相似类别的相对差异性信息问题,提出采用Fisher字典学习方法增强相似类训练样本的可区分性。此外,考虑到高光谱图像具有较强空间相关性的特点,设计一种简单有效的投票策略进行类别判决。大量实验表明:基于Fisher字典学习的联合投票分类方法能够较好地改善高光谱分类精度。     

基于最佳邻域重构指数的水下高光谱目标检测

水下机器人仅通过传统光学相机获取图像很难在复杂水下环境中或目标物具有保护色的情况下检测到目标,而通过高光谱技术进行水下目标检测可以改善这一情况;由于直接运用传统高光谱检测方法难以满足水下机器人对水下目标检测的要求,提出了一种基于最佳邻域重构指数(ONRIF)的高光谱目标检测方法,该方法通过线性重构的思想进行邻域寻优,选出信息量高且波段相关性低的波段组合,并使用所选波段的融合图像进行目标检测;结果表明,与直接对原始水下海产品高光谱图像进行检测相比,该方法在保证检测效果的前提下,大量减少了检测时间和数据冗余程度;还提出了一种在相同环境下对同类目标物的单波段快速采集检测方法,大大提高了采集数据的速度,可以满足水下机器人对海产品检测的需求.     

高光谱遥感反演土壤重金属含量研究进展

土壤重金属污染是当今重要的世界性环境问题之一。如何快速高效地获取土壤重金属含量及分布信息是进行污染风险评价及预测研究的前提。高光谱遥感具有无损、实时获取信息的优势,利用高光谱技术反演土壤重金属含量仍处于探讨阶段,对其反演机理和建模方法的研究成果进行了介绍和分析。认为充分研究不同类型土壤对重金属的吸附特性,是深入理解重金属含量反演机理的基础。加强不同土壤类型、不同污染程度的案例对比分析,发展光谱信号处理方法,引入非线性建模算法,构建重金属形态量反演模型,是土壤重金属含量遥感估算研究的重要研究方向。     

高光谱影像的BDT-SVM地物分类算法与应用

面对海量数据的特征空间高维性及训练样本的有限性,高光谱遥感影像若采用常规统计模式的分类方法难以获得较好的分类结果。因此探讨支持向量机(SVM)分类器的基本原理,针对EO-1Hyperion高光谱影像的分类特点及现有多类SVM算法所存在的训练时间长及分类精度低等问题,引入二叉决策树SVM(BDT-SVM)分类算法,并提出一种新的类间分离度定义方法及相应的客观确定二叉树结构的策略,由此生成改进的BDT-SVM算法。实验结果表明:与其他多类分类方法相比,基于改进的BDT-SVM算法的高光谱影像地物分类效果更好,总体精度达到90.96%,Kappa系数为0.89,该算法还解决了经典SVM多类分类可能存在的不可分区域问题。