As the number falls,alternatives to the Hagberg–Perten falling number method: A review

This review examines the application, limitations, and potential alternatives to the Hagberg–Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.     

用于单芯片系统的改进型WXGA LCoS成像器

本文讨论用于单芯片时序混色的菲利浦DD-720硅基液晶(LCoS)片。这种芯片主要用于HDTV背投影机和多媒体系统。与菲利浦以前的单片LCoS设计相比，由于该芯片具有电接口接点较少、封装简单和温度传感器内置等许多特点，使其应用于投影系统时成本降低。     

Single and matrix HgCdTe photovoltaic detectors

The recent researches and technological developments of middle and long wavelength infrared HgCdTe photovoltaic detectors are presented. Structure, topology, design and performance of HgCdTe photodiodes, silicon readout electronics, Focal Plane Arrays both staring and time delay and integration types, thermal imagers are discussed. Negative differential conductance, bistability and high frequency oscillations under background infrared radiation in HgCdTe photodiodes are reported.     

Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.     

New developments in CdTe and CdZnTe detectors for X and γ-ray applications

There has been considerable recent progress in II-VI semiconductor material and in methods for improving performance of the associated radiation detectors. New high resistivity CdZnTe material, new contact technologies, new detector structures, new electronic correction methods have opened the field of nuclear and x-ray imaging for industrial and medical applications. The purpose of this paper is to review new developments in several of these fields. In addition, we will present some recent results at LETI concerning first the CdTe 2-D imaging system (20 × 30 mm² with 400 × 600 pixels) for dental radiology and second the CdZnTe fast pulse correction method applied to a 5 × 5 × 5 mm³ CdZnTe detector (energy resolution = 5% for detection efficiency of 85% at 122 keV) for medical imaging.     

The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping

Compact Airborne Spectrographic Imager (CASI) hyperspectral data is used to investigate the effects of topography on the selection of spectral end members, and to assess whether the topographic correction improves the discrimination of rock units for lithologic mapping. A publicly available Digital Elevation Model (DEM), at a scale of 1:50,000, is used to model the radiance variation of the scene as a function of topography, assuming a Lambertian surface. Skylight is estimated and removed from the airborne data using a dark object correction. The CASI data is corrected on a pixel-by-pixel basis to normalize the scene to a uniform solar illumination and viewing geometry. The results show that topography has the effect of expanding end member clusters at times resulting in the overlap of clusters and that the correction process can effectively reduce the variation in detected radiance due to changes in local illumination. When topographic effects are embedded in the hyperspectral data, methods typically used for the selection of end members, such as the convex hull method, can miss end members or result in the selection of nonrepresentative pixels as end members. Thus, end members selected by some conventional methods are very likely “incomplete” or “nonrepresentative” if the topographic effect is embedded in the data. As shown in this study, the topographic correction can reveal hidden end members and achieve a better representation of end members via the statistical center of isolated clusters.     

Graph-Based Dimensionality Reduction for Hyperspectral Imagery: A Review

Hyperspectral image(HSI) contains a wealth of spectral information, which makes fine classification of ground objects possible. In the meanwhile, overly redundant information in HSI brings many challenges. Specifically, the lack of training samples and the high computational cost are the inevitable obstacles in the design of classifier. In order to solve these problems, dimensionality reduction is usually adopted. Recently, graph-based dimensionality reduction has become a hot topic. In this paper, the graph-based methods for HSI dimensionality reduction are summarized from the following aspects. 1) The traditional graph-based methods employ Euclidean distance to explore the local information of samples in spectral feature space. 2) The dimensionality-reduction methods based on sparse or collaborative representation regard the sparse or collaborative coefficients as graph weights to effectively reduce reconstruction errors and represent most important information of HSI in the dictionary. 3) Improved methods based on sparse or collaborative graph have made great progress by considering global low-rank information, local intra-class information and spatial information. In order to compare typical techniques, three real HSI datasets were used to carry out relevant experiments, and then the experimental results were analysed and discussed.Finally, the future development of this research field is prospected.     

Multi-Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification

基于多尺度空洞卷积神经网络的高光谱图像分类

郑姗姗¹,刘文¹,单锐¹,赵静一²,江国乾³,张智⁴

（1. 燕山大学理学院,河北 秦皇岛 066004;2. 燕山大学机械工程学院,河北 秦皇岛 066004;3. 燕山大学电气工程学院,河北 秦皇岛 066004;4. 北京航天研究所,北京 100094）

创新点说明：

1）将图像分割方法——空洞卷积用于卷积神经网络进行高光谱图像分类,提取更加广泛、抽象的图像特征。

2）构建基于多尺度空洞卷积神经网络的高光谱图像分类方法。搭建多尺度聚合结构,在每个通道中使用快捷连接和空洞卷积结构,有效提取图像特征,避免信息丢失。

研究目的：

针对图像信息丢失问题,得到高精度的高光谱图像分类方法。

研究方法：

在Indian Pines和Pavia University数据集上,与4个已有的高光谱图像分类方法进行对比实验,比较OA, AA和Kappa值。

研究结果：

多尺度空洞卷积神经网络在Indian Pines和Pavia University数据集上OA值分别达到了99.58%,99.92%。AA值分别达到了99.57%,99.90%。Kappa分别达到了99.52%,99.89%。

结论：

1）在卷积神经网络中引入空洞卷积,可以有效避免图像信息丢失。

2）多尺度空洞卷积神经网络能提取更佳的判别性特征,实现高分类性能。

关键词：多尺度聚合;空洞卷积;高光谱图像分类;快捷连接

     

一种改进的超宽条带噪声消除算法

针对高光谱遥感图像中的超宽条带噪声干扰现象,在深入研究高光谱图像特点和条带噪声产生机理的基础上,提出了一种新的基于最小序列值、小波变换和矩匹配相结合的滤波算法（OWM算法）。该算法主要包括灰度对比度处理、最小序列值处理、小波变换系数归零处理和矩匹配处理等四个步骤。用实际的高光谱图像进行了一系列的验证比较实验,获得了好的实验效果。实验结果表明OWM算法不仅能够有效滤除高光谱图像中的超宽条带噪声,而且还具有较好的普适性。     

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.