Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.     

Data handling in hyperspectral image analysis

Hyperspectral imaging (HSI) combines spectroscopy and imaging resulting in three dimensional multivariate data structures (‘hypercubes’). Each pixel in a hypercube contains a spectrum representing its light absorbing and scattering properties. This spectrum can be used to estimate chemical composition and/or physical properties of the spatial region represented by that pixel. One of the advantages of HSI is the large volume of data available in each hypercube with which to create calibration and training sets. This is also known as the curse of dimensionality, due to the resultant high computational load of high dimensional data. It is desirable to decrease the computational burden implied in hyperspectral imaging; this is especially relevant in the development of real time applications. This paper gives an overview of some pertinent issues for the handling of HSI data. Computational considerations involved in acquiring and managing HSI data are discussed and an overview of the multivariate analysis methods available for reducing the considerable data load encountered in HSI data is presented.     

基于喷射效应的太赫兹高分辨成像研究与进展

太赫兹(THz)成像技术,因其具有能量低、透射率高、波谱范围宽等独特的分析能力,已经在生物医学、安全检查、航空航天等领域展现出巨大的优势及潜在的应用价值,但是较低的空间分辨率制约了太赫兹成像技术的进一步应用。太赫兹波通过具有适当折射率的介质结构产生的“太喷射”效应调控亚波长尺寸太赫兹光场,突破衍射极限对显微系统空间分辨率的限制,同时不损失光场能量和光谱信息,实现高通量、超宽谱的远场太赫兹高分辨成像。本文首先介绍基于纳米喷射的微球透镜显微技术,接着介绍基于太喷射的太赫兹显微技术,最后对基于喷射效应的太赫兹高分辨成像技术的前景做了展望。     

Digital micromirror device as a spatial illuminator for fluorescence lifetime and hyperspectral imaging

Time-domain fluorescence lifetime imaging (FLIM) and hyper-spectral imaging (HSI) are two advanced microscopy techniques widely used in biological studies. Typically both FLIM and HSI are performed with either a whole-field or raster-scanning approach, which often prove to be technically complex and expensive, requiring the user to accept a compromise among precision, speed, and spatial resolution. We propose the use of a digital micromirror device (DMD) as a spatial illuminator for time-domain FLIM and HSI with a laser diode excitation source. The rather unique features of the DMD allow both random and parallel access to regions of interest (ROIs) on the sample, in a very rapid and repeatable fashion. As a consequence both spectral and lifetime images can be acquired with a precision normally associated with single-point systems but with a high degree of flexibility in their spatial construction. In addition, the DMD system offers a very efficient way of implementing a global analysis approach for FLIM, where average fluorescence decay parameters are first acquired for a ROI and then used as initial estimates in determining their spatial distribution within the ROI. Experimental results obtained on phantoms employing fluorescent dyes clearly show how the DMD method supports both spectral and temporal separation for target identification in HSI and FLIM, respectively.     

Noncontact optical imaging in mice with full angular coverage and automatic surface extraction

During the past decade, optical imaging combined with tomographic approaches has proved its potential in offering quantitative three-dimensional spatial maps of chromophore or fluorophore concentration in vivo. Due to its direct application in biology and biomedicine, diffuse optical tomography (DOT) and its fluorescence counterpart, fluorescence molecular tomography (FMT), have benefited from an increase in devoted research and new experimental and theoretical developments, giving rise to a new imaging modality. The most recent advances in FMT and DOT are based on the capability of collecting large data sets by using CCDs as detectors, and on the ability to include multiple projections through recently developed noncontact approaches. For these to be implemented, we have developed an imaging setup that enables three-dimensional imaging of arbitrary shapes in fluorescence or absorption mode that is appropriate for small animal imaging. This is achieved by implementing a noncontact approach both for sources and detectors and coregistering surface geometry measurements using the same CCD camera. A thresholded shadowgrammetry approach is applied to the geometry measurements to retrieve the surface mesh. We present the evaluation of the system and method in recovering three-dimensional surfaces from phantom data and live mice. The approach is used to map the measured in vivo fluorescence data onto the tissue surface by making use of the free-space propagation equations, as well as to reconstruct fluorescence concentrations inside highly scattering tissuelike phantom samples. Finally, the potential use of this setup for in vivo small animal imaging and its impact on biomedical research is discussed.     

Molecular Engineering of NIR-II/IIb Emitting AIEgen for Multimodal Imaging-Guided Photo-Immunotherapy

In view of the great challenges related to the complexity and heterogeneity of tumors, efficient combination therapy is an ideal strategy for eliminating primary tumors and inhibiting distant tumors. A novel aggregation-induced emission (AIE) phototherapeutic agent called T-TBBTD is developed, which features a donor–acceptor–donor (D–A–D) structure, enhanced twisted molecule conformation, and prolonged second near-infrared window (NIR-II) emission. The multimodal imaging function of the molecule has significance for its treatment time window and excellent photothermal/photodynamic performance for multimode therapy. The precise molecular structure and versatility provide prospects for molecular therapy for anti-tumor applications. Fluorescence imaging in the NIR-II window offers advantages with enhanced spatial resolution, temporal resolution, and penetration depth. The prepared AIE@R837 NPs also have controllable performance for antitumor photo-immunotherapy. Following local photo-irradiation, AIE@R837 NPs generate abundant heat, and ¹O₂ directly kills tumor cells, induces immunogenic cell death (ICD) as a photo-therapeutic effect, and releases R837, which enhances the synergistic effect of antigen presentation and contributes to the long-lasting protective antitumor immunity. A bilateral 4T1 tumor model revealed that this photo-immunotherapy can eliminate primary tumors. More importantly, it has a significant inhibitory effect on distant tumor growth. Therefore, this method can provide a new strategy for tumor therapy.     

Multivariate image analysis: A review with applications

Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction.     

Materials Chemistry of Nanoultrasonic Biomedicine

As a special cross‐disciplinary research frontier, nanoultrasonic biomedicine refers to the design and synthesis of nanomaterials to solve some critical issues of ultrasound (US)‐based biomedicine. The concept of nanoultrasonic biomedicine can also overcome the drawbacks of traditional microbubbles and promote the generation of novel US‐based contrast agents or synergistic agents for US theranostics. Here, we discuss the recent developments of material chemistry in advancing the nanoultrasonic biomedicine for diverse US‐based bio‐applications. We initially introduce the design principles of novel nanoplatforms for serving the nanoultrasonic biomedicine, from the viewpoint of synthetic material chemistry. Based on these principles and diverse US‐based bio‐application backgrounds, the representative proof‐of‐concept paradigms on this topic are clarified in detail, including nanodroplet vaporization for intelligent/responsive US imaging, multifunctional nano‐contrast agents for US‐based multi‐modality imaging, activatable synergistic agents for US‐based therapy, US‐triggered on‐demand drug releasing, US‐enhanced gene transfection, US‐based synergistic therapy on combating the cancer and potential toxicity issue of screening various nanosystems suitable for nanoultrasonic biomedicine. It is highly expected that this novel nanoultrasonic biomedicine and corresponding high performance in US imaging and therapy can significantly promote the generation of new sub‐discipline of US‐based biomedicine by rationally integrating material chemistry and theranostic nanomedicine with clinical US‐based biomedicine.     

Scene-based method for spatial misregistration detection in hyperspectral imagery

Hyperspectral imaging (HSI) sensors suffer from spatial misregistration, an artifact that prevents the accurate acquisition of the spectra. Physical considerations let us assume that the influence of the spatial misregistration on the acquired data depends both on the wavelength and on the across-track position. A scene-based method, based on edge detection, is therefore proposed. Such a procedure measures the variation on the spatial location of an edge between its various monochromatic projections, giving an estimation for spatial misregistration, and also allowing identification of misalignments. The method has been applied to several hyperspectral sensors, either prism, or grating-based designs. The results confirm the dependence assumptions on lambda and theta, spectral wavelength and across-track pixel, respectively. Suggestions are also given to correct for spatial misregistration.     

Quantitative analysis of piroxicam polymorphs pharmaceutical mixtures by hyperspectral imaging and chemometrics

It has been recognized that crystal polymorphism is an important factor related to the physicochemical and biological properties of drug substances and formulations. In this work, the piroxicam polymorphic forms 1 and 2 were studied using near-infrared chemical imaging (NIR-CI) technology to map the distribution of both species in pharmaceutical formulations. In this direction, the partial least squares (PLS) method was used to construct calibration models of concentrations per pixel of the sample. The RMSEP results for both models of the polymorphic forms remained below 4% (w/w). It was also possible to distinguish local and global information of the constituents through this method. These results seem to be a suitable tool for quality process control and final product quality assurance.     

An epistemological approach to human systems integration

Can we contribute to developing a consistent terminology and, to some extent, an acceptable ontology in the rapidly expanding field of human systems integration (HSI)? We often define HSI as a process and a product at the confluence of several areas, such as systems engineering, human factors and ergonomics, information technology, and specific sectors, such as aerospace, health, and energy. It is a broader transdisciplinary field in our increasingly complex human-machine world that focuses on integrating technology, organizations, and people within a complex sociotechnical system throughout its life cycle. Therefore, HSI is no longer a question of usability and user interface design once a complex machine is technologically developed, but also about considering people and organizations early on in the design and development processes. Indeed, rooted in industrial engineering research and operational worlds, HSI requires a deeper foundation based on an epistemological approach. This assertion is even more crucial today as technology has become predominantly digital, and, more specifically, the concept of the digital twin is emphasized because it has become essential to support model-based HSI. In other words, software-based assistant systems are replacing traditional tools. Therefore, appropriate social-cognitive (multi-agent) models and methods are helpful throughout the life cycle of contemporary sociotechnical designs to account for the complexity and tangibility of their human-centered context-sensitive architectures, combining procedural and declarative knowledge. By considering these reasons, this article provides a set of fundamental axioms, some theoretical abstractions, and valuable practical models, which are presented and illustrated through the lens of an evolutionary HSI ontology.     

Background-free three-dimensional selective imaging of anisotropic plasmonic nanoparticles

There is an increasing demand for advanced optical imaging techniques that can detect and resolve nanosize objects at a spatial resolution below the optical diffraction limit,especially in three-dimensional (3D) cellular environments.In this study,using a polarization-activated localization scheme based on the orientation-dependent properties of anisotropic plasmonic metal nanoparticles (MNPs),"photoswitchable" imaging of single gold nanorods (AuNRs) was accomplished not only in two dimensions but also in three dimensions.Moreover,the Rayleigh scattering background arising from the congested subcellular structures was efficiently suppressed.Thus,we obtained the 3D distributions of both the position and the orientation of the AuNRs inside the cells and investigated their internalization kinetics.To our knowledge,this is the first demonstration of the confocal-like 3D imaging of non-fluorescence nanoparticles with a high resolution and almost zero background.This technique is easy to implement and should greatly facilitate MNP studies and applications in biomedicine and biology.     

Signal-to-noise ratio for astronomical imaging by deconvolution from wave-front sensing

One method for improving the quality of astronomical images measured through a atmospheric turbulence uses simultaneous short-exposure measurements of both an image and the output of a wave-front sensor exposed to an image of the telescope pupil. The wave-front sensor measurements are used to reconstruct an estimate of the instantaneous generalized pupil function of the telescope, which is used to compute an estimate of the instantaneous optical transfer function, which is then used in a deconvolution procedure. This imaging method has been called both deconvolution from wave-front sensor (DWFS) measurements and self-referenced speckle holography. We analyze the signal-to-noise ratio (SNR) behavior of this imaging method in the spatial frequency domain. The analysis includes effects arising from differences in the correlation properties of the incident and the estimated pupil phases and the fact that the object-spectrum estimator is a randomly filtered doubly stochastic Poisson random process. SNR resultsobtained for the DWFS method are compared with the speckle-imaging powerspectrum SNR for equivalent seeing conditions and light levels. It is shown that for unresolved stars the power-spectrum SNR is superior to the DWFS SNR. However, for extended objects the power-spectrum SNR and the DWFS SNR are similar. Since speckle imaging uses a separate Fourier phasereconstruction process not required by the DWFS method, the DWFS method provides an alternative to speckle imaging that uses simple postprocessing at the cost of a wave-front sensor measurement but with no loss of SNR performance for extended objects.     

过氧化氢探针的研究进展

作为活性氧家族中的重要成员,过氧化氢不仅是生命体内重要的氧代谢产物和信号传导介质,而且也是氧化胁迫的主要来源和活性氧相关疾病的重要标志物。因此,开发快速、简便、灵敏的过氧化氢探针材料在生物医学等领域具有重要的意义。过氧化氢探针可分为荧光探针、生物发光探针、电极探针等,其中荧光探针因具有高灵敏度、高选择性等特点而受到广泛的关注。以荧光探针为中心,根据分子的大小和工作原理的不同进行分类,介绍了过氧化氢探针的研究进展,并对过氧化氢探针的发展前景进行了展望。     

Characterisation of biological material with ToF-SIMS: a review

Abstract

Time of flight secondary ion mass spectrometry (ToF-SIMS) has the unique ability to simultaneously obtain chemical information (elemental and molecular) with its spatial distribution on a subcellular scale. Recent progress in instrumentation, in particular the developments of cluster ion beam sources, has resulted in a growing interest in applying ToF-SIMS to a range of biological samples. In this review, the instrumental and methodological approaches responsible for this interest are presented along with some examples where the technique has been successfully applied.     

Theoretical error analysis of the sampling moiré method and phase compensation methodology for single-shot phase analysis

Recently, a rapid and accurate single-shot phase measurement technique called the sampling moiré method has been developed for small-displacement distribution measurements. In this study, the theoretical phase error of the sampling moiré method caused by linear intensity interpolation in the case of a mismatch between the sampling pitch and the original grating pitch is analyzed. The periodic phase error is proportional to the square of the spatial angular frequency of the moiré fringe. Moreover, an effective phase compensation methodology is developed to reduce the periodic phase error. Single-shot phase analysis can perform accurately even when the sampling pitch is not matched to the original grating pitch exactly. The primary simulation results demonstrate the effectiveness of the proposed phase compensation methodology.     

A spatial variable selection method for monitoring product surface

Two-dimensional (2-D) data maps are generated in certain advanced manufacturing processes. Such maps contain rich information about process variation and product quality status. As a proven effective quality control technique, statistical process control (SPC) has been widely used in different processes for shift detection and assignable cause identification. However, charting algorithms for 2-D data maps are still vacant. This paper proposes a variable selection-based SPC method for monitoring 2-D wafer surface. The fused LASSO algorithm is firstly employed to identify potentially shifted sites on the surface; a charting statistic is then developed to detect statistically significant shifts. As the variable selection algorithm can nicely preserve shift patterns in spatial clusters, the newly proposed chart is proved to be both effective in detecting shifts and capable of providing diagnostic information for process improvement. Extensive Monte Carlo simulations and a real example have been used to demonstrate the effectiveness and usage of the proposed method.     

An algebraic multigrid approach to solve extended finite element method based fracture problems

This article proposes an algebraic multigrid (AMG) approach to solve linear systems arising from applications where strong discontinuities are modeled by the extended finite element method. The application of AMG methods promises optimal scalability for solving large linear systems. However, the straightforward (or ‘black‐box’) use of existing AMG techniques for extended finite element method problems is often problematic. In this paper, we highlight the reasons for this behavior and propose a relatively simple adaptation that allows one to leverage existing AMG software mostly unchanged. Numerical tests demonstrate that optimal iterative convergence rates can be attained that are comparable with AMG convergence rates associated with linear systems for standard finite element approximations without discontinuities. Published 2012. This article is a US Government work and is in the public domain in the USA.     

Airborne system for fast measurements of upwelling and downwelling spectral actinic flux densities

An airborne system for fast measurements of spectral actinic flux densities in the wavelength range 305-700 nm is introduced. The system is called the Actinic Flux Density Meter (AFDM). The AFDM utilizes the diode array technique and measures downwelling and upwelling spectral actinic flux densities separately with a time resolution of less than 1 s. For airborne measurements this means a spatial resolution of approximately 60 m, assuming an average aircraft velocity of 60 m/s. Thus the AFDM resolves fast changes in the actinic radiation field, which are of special importance for conditions of inhomogeneous clouds or surface reflection. Laboratory characterization measurements of the AFDM are presented, and a method to correct the nonideal angular response of the optical inlets is introduced. Furthermore, exemplar field data sampled simultaneously with spectral irradiance measurements are shown. The horizontal variability of the measured spectra of actinic flux density is quantified, and profile measurements for overcast situations are presented. Finally, the effects of clouds on the spectral actinic flux density are discussed.     

Conductive Silk‐Based Composites Using Biobased Carbon Materials

There is great interest in developing conductive biomaterials for the manufacturing of sensors or flexible electronics with applications in healthcare, tracking human motion, or in situ strain measurements. These biomaterials aim to overcome the mismatch in mechanical properties at the interface between typical rigid semiconductor sensors and soft, often uneven biological surfaces or tissues for in vivo and ex vivo applications. Here, the use of biobased carbons to fabricate conductive, highly stretchable, flexible, and biocompatible silk‐based composite biomaterials is demonstrated. Biobased carbons are synthesized via hydrothermal processing, an aqueous thermochemical method that converts biomass into a carbonaceous material that can be applied upon activation as conductive filler in composite biomaterials. Experimental synthesis and full‐atomistic molecular dynamics modeling are combined to synthesize and characterize these conductive composite biomaterials, made entirely from renewable sources and with promising applications in fields like biomedicine, energy, and electronics.