Multispectral photodetection using low-cost sputtered NiO/Ag/ITO heterostructure: From design concept to elaboration

High-performance multispectral photodetectors (PDs) are highly attractive for the emerging optoelectronic applications. In this work, a new broadband PD based on p-NiO/Ag/n-ITO heterostructure was fabricated by RF magnetron sputtering technique at room temperature. The tri-layered structure offering multispectral detection property was first identified using theoretical calculations based on combined FDTD and Particle Swarm Optimization (PSO) techniques. The crystal structure of the elaborated sensor was analyzed using X-ray diffraction (XRD) method. The device optical properties were investigated by UV–Vis–NIR spectroscopy. The NiO/Ag/ITO heterostructured PD shows a high average absorbance of 63% over a wide spectrum range of [200 nm–1100nm]. Compared with NiO and ITO thin-films, the performances of the heterostructured device are considerably enhanced. It was found that the prepared PD with NiO/Ag/ITO heterostructure merges the benefits of multispectral photodetection with reduced optical losses and efficient transfer of photo-induced carrier. The device demonstrated a high I_ON/I_OFF ratio of 78 dB and an enhanced responsivity under UV, visible and NIR lights (171 mA/W at 365 nm, 67 mA/W at 550 nm and 93 mA/W at 850 nm). The broadband photodetection property enabled by the optimized NiO/Ag/ITO heterostructure opens a new route for the elaboration of low-cost devices that can offer multiple sensing purposes, which are highly suitable for optoelectronic applications.     

Development of a hybrid method for estimating land surface shortwave net radiation from MODIS data

Accurate estimation of shortwave net radiation (S_n) at a high spatial resolution is critical for regional and global land surface models. Current surface radiation budget products have fine temporal resolutions, but only coarse spatial resolutions that are not suitable for land applications. A hybrid algorithm was developed in this study to estimate S_n from MODIS data under both clear and cloudy sky conditions without requiring coarser resolution ancillary data. This algorithm was validated using ground measurements at seven sites of the SURFace RADiation budget observing network (SURFRAD) in the United States. Instantaneous S_n estimated by this method was also compared with GEWEX/SRB and ISCCP data, and other methods. The results indicate that our algorithm can produce S_n at 1-km resolution with improved accuracy and is easily implemented to generate operational global products. Daily integrated S_n is estimated at 1-km resolution using instantaneous S_n. These finer spatial resolution datasets capture the specific sequence of the redistribution of the available energy at the Earth's surface. Therefore, they support recent high resolution land surface models.     

Determining optimal wavebands using genetic algorithm for detection of internal insect infestation in tart cherry

This paper reports the results of waveband selection for detecting internal insect infestation in tart cherries as a precursor to development of a dedicated multispectral vision system. A genetic algorithm (GA) approach was applied on hyperspectral transmittance images (580–980 nm) and reflectance spectral data (590–1,550 nm) acquired from both intact and infested tart cherries. The GA analysis indicates that the ability of using transmittance imaging approach for detecting internal insect infestation in tart cherries would be limited. According to the GA analysis on the reflectance spectra, visible wavelengths were of less importance than NIR wavelengths for the purpose of distinguishing intact cherries from infested ones. The PLSDA results indicate that models built with three or four GA selected wavelength regions gave similar classification accuracy to the model built with full wavelength region, which demonstrates the efficiency of the GA variable selection procedure. However, due to the stochastic nature of the GA, the efficiency of using these wavebands in a multispectral vision system needs to be verified in future work.     

Challenges and solutions of optical-based nondestructive quality inspection for robotic fruit and vegetable grading systems: A technical review

BackgroundOptical techniques, including computer vision, spectral imaging, near-infrared technology and other emerging imaging and spectroscopy techniques, have been rapidly developing and widely applied in fruit and vegetable grading systems for nondestructive quality inspecting and grading over the past decades. However, automatic detection of quality and grading is still difficult due to some still existing challenges, which are the key of blocking their commercialization in robotic fruit and vegetable grading systems. The challenges include the following aspects: the influence of physical and biological variability, whole surface detection, discrimination between defects and stems/calyxes, unobvious defect detection, robustness of the features and algorithms, as well as rapid optical detection system development. These challenges can reduce the fruit or vegetable quality inspection accuracy, thus greatly reducing automatic level of the quality inspecting and grading machines.Scope and approachAs agricultural engineers with about eight years of technical experience in fruit grading systems, we believe the ultimate goal of each scientific research should seek its task in serving the engineering. So, we have made many attempts to solve the challenges and increase the automation of the grading machines.Key findings and conclusionsThe review gives a detailed summary about the challenges and solutions of optical-based nondestructive quality inspection for fruit or vegetable grading systems from the perspective of engineering. Particular attention has been paid to the techniques that can improve the automation degree of the grading robot in this review. The advantages and disadvantages of the solutions are compared and discussed. Additionally, the remaining engineering challenges and future trends are also discussed.     

基于主分量和独立成分分析的多光谱目标检测

不同材料的物体具有不同的光谱特性, 基于这一原理, 可以利用多光谱图像数据对不同的目标进行检测。对于具有相似或相同外形特征( 颜色和形状) 的物体, 利用全色图像一般达不到检测与识别的目的; 利用传统的多光谱目标检测方法, 则因计算量较大, 识别精度低等, 达不到满意的效果。提出了一种基于主分量与独立成分分析相结合的多光谱目标检测的新方法。通过对多光谱图像数据进行主分量分析, 可以降低多光谱的维数, 去掉冗余成份, 保留其主要信息; 对降维后的数据再进行独立成分分析, 提取各种目标的光谱特性, 实现目标的检测与识别。将这两种方法有机的结合起来, 发挥各自的优点, 实现对多光谱图像目标快速的检测与识别。以真假树叶( 真树叶和塑料树叶) 为例, 验证了该方法的有效性和正确性。     

基于小波变换和分段DPCM混合编码的多光谱遥感图像压缩算法

多光谱遥感图像的压缩要利用图像谱内及谱间的相关特性。该文在分析多光谱图像谱内和谱间相关特性的基础上,提出了对多光谱遥感图像进行压缩的分段 DPCM和 SPIHT相结合的混合压缩算法,即首先利用分段 DPCM算法去除谱间冗余,再利用高效的 SPIHT小波压缩算法对预测误差图像进行编码。实验取得了令人满意的效果,证明了该算法的有效性。     

A multispectral imaging technique to determine concentration profiles of myoglobin derivatives during meat oxygenation

We have designed a multispectral imaging acquisition system to measure the relative concentration values of myoglobin forms inside meat during oxygenation. Images at 474, 525, 572 and 610 nm are used to compute the concentration of reduced myoglobin, oxymyoglobin and metmyoglobin with a spatial resolution of 0.0125 mm/pixel. From these images, pigment concentration profiles as a function of oxygenation time and depth beneath the surface were obtained. A model describing the diffusion of oxygen and the consumption of reduced myoglobin is numerically tested versus the measured concentration profiles. The model accurately fits the data with a Mean Root Squared Error equal to 0.253%. Precise definitions for position and width of pigment layers based in concentration profiles are given. The results suggest that multispectral imaging techniques combined with precise control and measurement of sample temperature and oxygen partial pressure will permit detailed studies of the myoglobin chemistry during oxygenation.     

Image Quality Assessment for Visual and Infrared Multis-pectral Imager of Gaofen-5

Gaofen-5(GF-5) satellite was successfully launched on May 29, 2018. The Visual and Infrared Multispectral Imager(VIMI) developed independently by China is a multi-spectral imager in the range of visible band to long-wave infrared band, which has broad application prospects. The quality assessment of satellite image is not only the verification of whether the remote sensing satellite meets the design criteria, but also the reference for image processing and application. In this paper, the quality assessment for VIMI is provided, which provides reference for the processing and application of the image. Four indicators, named the Signal-to-Noise Ratio (SNR), clarity, information content and radiation heterogeneity, were used for quality assessment, and were compared with Landsat 8 images. The results show that the SNR of the shortwave infrared band (320.44~388.42) is slightly higher than that of the visible near-infrared band(208.24~238.03). The clarity (0.82~0.91) in the near-shortwave infrared band is higher than that in the other bands, especially in the long-wave infrared band (0.01~0.21). The information content of short-wave infrared band (9.01~9.97) is higher than that of medium-long-wave infrared band (5.71~8.31). The radiation heterogeneity of all 12 bands is less than 2%. The results of comparison with Landsat 8 show that ①the clarity of B1~B4 is better than Landsat 8 and this of other bands are close to Landsat 8,②for information content, B1~B10 of VIMI is close to Landsat 8, while this of B11 and B12 is less than Landsat 8 with 5.23 for B11 and 5.61 for B12,(3)for SNR, GF-5 VIMI still needs to be further improved, with 280.41、226.84 and 151.92 less than Landsat 8 for B1,B2 and B6.     

高光谱数据融合研究

多传感器的遥感数据融合在城市规划、土地利用、矿产探测、军事侦察等领域有着非常广泛的应用前景。本文高光谱图像光谱分辨率高、空间分辨率低、多光谱图像空间分辨率高、光谱分辨率高的特点,阐述了其数据融合。特别介绍了CRISP锐化算法。     

Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes.