Close-range hyperspectral imaging for geological field studies: workflow and methods

Close-range hyperspectral imaging is a new method for geological research, in which imaging spectrometry is applied from the ground, allowing the mineralogy and lithology in near-vertical cliff sections to be studied in detail. Contemporary outcrop studies often make use of photorealistic three-dimensional (3D) models, derived from terrestrial laser scanning (lidar), that facilitate geological interpretation of geometric features. Hyperspectral imaging provides complementary geochemical information that can be combined with lidar models, enhancing quantitative and qualitative analyses. This article describes a complete workflow for applying close-range hyperspectral imaging, from planning the optimal scan conditions and data acquisition, through pre-processing the hyperspectral imagery and spectral mapping, integration with lidar photorealistic 3D models, and analysis of the geological results. Pre-processing of the hyperspectral images involves the reduction of scanner artefacts and image discontinuities, as well as relative reflectance calibration using empirical line correction, based on two calibrated reflection targets. Signal-to-noise ratios better than 70:1 are achieved for materials with 50% reflectance. The lidar-based models are textured with products such as hyperspectral classification maps. Examples from carbonate and siliciclastic geological environments are presented, with results showing that spectrally similar material, such as different dolomite types or sandstone and siltstone, can be distinguished and spectrally mapped. This workflow offers a novel and flexible technique for applications, in which a close-range instrument setup is required and the spatial distribution of minerals or chemical variations is valuable.     

Evaluation of hyperspectral data for pasture estimate in the Brazilian Amazon using field and imaging spectrometers

We used two hyperspectral sensors at two different scales to test their potential to estimate biophysical properties of grazed pastures in Rondônia in the Brazilian Amazon. Using a field spectrometer, ten remotely sensed measurements (i.e., two vegetation indices, four fractions of spectral mixture analysis, and four spectral absorption features) were generated for two grass species, Brachiaria brizantha and Brachiaria decumbens. These measures were compared to above ground biomass, live and senesced biomass, and grass canopy water content. The sample size was 69 samples for field grass biophysical data and grass canopy reflectance. Water absorption measures between 1100 and 1250 nm had the highest correlations with above ground biomass, live biomass and canopy water content, while ligno-cellulose absorption measures between 2045 and 2218 nm were the best for estimating senesced biomass. These results suggest possible improvements on estimating grass measures using spectral absorption features derived from hyperspectral sensors. However, relationships were highly influenced by grass species architecture. B. decumbens, a more homogeneous, low growing species, had higher correlations between remotely sensed measures and biomass than B. brizantha, a more heterogeneous, vertically oriented species. The potential of using the Earth Observing-1 Hyperion data for pasture characterization was assessed and validated using field spectrometer and CCD camera data. Hyperion-derived NPV fraction provided better estimates of grass surface fraction compared to fractions generated from convolved ETM+/Landsat 7 data and minimized the problem of spectral ambiguity between NPV and Soil. The results suggest possible improvement of the quality of land-cover maps compared to maps made using multispectral sensors for the Amazon region.     

An immersive multi-agent system for interactive applications

This paper presents an interactive multi-agent system based on a fully immersive virtual environment. A user can interact with the virtual characters in real time via an avatar by changing their moving behavior. Moreover, the user is allowed to select any character as the avatar to be controlled. A path planning algorithm is proposed to address the problem of dynamic navigation of individual and groups of characters in the multi-agent system. A natural interface is designed for the interaction between the user and the virtual characters, as well as the virtual environment, based on gesture recognition. To evaluate the efficiency of the dynamic navigation method, performance results are provided. The presented system has the potential to be used in the training and evaluation of emergency evacuation and other real-time applications of crowd simulation with interaction.     

Unsupervised and supervised classification of hyperspectral imaging data using projection pursuit and Markov random field segmentation

This work presents a classification technique for hyperspectral image analysis when concurrent ground truth is either unavailable or available. The method adopts a principal component analysis (PCA)-based projection pursuit (PP) procedure with an entropy index for dimensionality reduction, followed by a Markov random field (MRF) model-based segmentation. An ordinal optimization approach to PP determines a set of ‘good enough projections’ with high probability, the best among which is chosen with the help of MRF model-based segmentation. When ground-truth is absent, the segmented output obtained is labelled with the desired number of classes so that it resembles the natural scene closely. When the land-cover classes are in detailed level, some special reflectance characteristics based on the classes of the study area are determined and incorporated in the segmentation stage. Segments are evaluated with training samples so as to yield a classified image with respect to the type of ground-truth data. Two illustrations are presented: (i) an AVIRIS-92AV3C image with concurrent ground truth – for both supervised and unsupervised cases and (ii) an EO-1 Hyperion sensor image with concurrent ground-truth at detailed level classes. Provided with the illustrations are comparisons of classification accuracies and computational times of other approaches with those of the proposed methodology. Experimental results demonstrate that the proposed method provides high classification accuracy and is not computationally intensive.     

高斯过程及其在高光谱图像分类中的应用

高光谱遥感图像分类是高光谱成像信息处理的研究热点,高光谱成像的内在特点对于分类器设计具有直接影响.高斯过程是近年来发展迅速的一种新的机器学习方法,具备容易实现、超参数可自适应获取以及预测输出具有概率意义等优点,比较适合于处理图像分类问题.首先对高斯过程的基本概念及其主要的分类算法进行了简要介绍,然后在对高光谱图像分类的特点和高光谱图像分类的研究现状的分析基础上,讨论了基于高斯过程的高光谱图像分类的基本思想,提出了基于空间约束的高斯过程分类和基于半监督高斯过程分类等适合高光谱图像分类的新方法.最后对基于高斯过程的高光谱图像分类研究的发展趋势进行了展望.     

An efficient motion magnification system for real-time applications

The human eye cannot see subtle motion signals that fall outside human visual limits, due to either limited resolution of intensity variations or lack of sensitivity to lower spatial and temporal frequencies. Yet, these invisible signals can be highly informative when amplified to be observable by a human operator or an automatic machine vision system. Many video magnification techniques have recently been proposed to magnify and reveal these signals in videos and image sequences. Limitations, including noise level, video quality and long execution time, are associated with the existing video magnification techniques. Therefore, there is value in developing a new magnification method where these issues are the main consideration. This study presents a new magnification method that outperforms other magnification techniques in terms of noise removal, video quality at large magnification factor and execution time. The proposed method is compared with four methods, including Eulerian video magnification, phase-based video magnification, Riesz pyramid for fast phase-based video magnification and enhanced Eulerian video magnification. The experimental results demonstrate the superior performance of the proposed magnification method regarding all video quality metrics used. Our method is also 60–70% faster than Eulerian video magnification, whereas other competing methods take longer to execute than Eulerian video magnification.     

An innovative degraded adhesion model for multibody applications in the railway field

The simulation of the braking maneuver of a railway vehicle under degraded adhesion conditions is very important concerning the safety of railway operation. However, the implementation of a realistic friction law is comparatively difficult because of the complex and nonlinear behavior of the wheel-rail contact. Particularly under degraded adhesion conditions, very high creepages occur, which cause sliding in the contact. This sliding produces a high dissipation of energy, which has a cleaning effect on the rolling surfaces, and thereby strengthens the influence of the adhesion. In this work, this energetic criterion has been studied. The authors suggest implementing an innovative friction law to the simulation of railway multibody models with 3D multi-point contact detection algorithms. As a benchmark case, the braking of a coach equipped with a Wheel Slide Protection (WSP) system is simulated. The results are compared with experimental data available from previous testing activities by Trenitalia. The new friction law provides to match the experimental reference results and to carry out simulated braking tests, including the working WSP system, which comply with the current regulations (Railway applications, braking, wheel slide protection, UNI EN 15595, 2009).     

Optimal field sampling for targeting minerals using hyperspectral data

This paper presents a statistical method for deriving optimal spatial sampling schemes. It focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixel in the hyperspectral image to a reference spectrum. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized through a process of simulated annealing. A HyMAP 126-channel airborne hyperspectral data acquired in 2003 over the Rodalquilar area in Spain serves as an application to target those pixels with the highest likelihood of occurrence of a specific mineral and as a collection the location of these sampling points selected represent the distribution of that particular mineral. In this area, alunite being a predominant mineral in the alteration zones was chosen as the target mineral. Three weight functions are defined to intensively sample areas where a high probability and abundance of alunite occurs. Weight function I uses binary weights derived from the SAM classification image, leading to an even distribution of sampling points over the region of interest. Weight function II uses scaled weights derived from the SAM rule image. Sample points are arranged more intensely in areas of abundance of alunite. Weight function III combines information from several different rule image classifications. Sampling points are distributed more intensely in regions of high probable alunite as classified by both SAM and SFF, thus representing the purest of pixels. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective.     

An open framework for agent based modelling of agricultural land use change

There is growing interest in creating empirically grounded agent based models (ABMs) to simulate land use change at a variety of spatio-temporal scales. The development of land use change models is challenging, as there is a need to connect representations of human behavioural processes to simulations of the biophysical environment. This paper presents a new agent-based modelling framework (Aporia) that has the goal of reducing the complexity and difficulty of constructing high-fidelity land use models. Building on earlier conceptual developments for modelling land use change and the provision of ecosystem services, Aporia was designed to be modular, flexible and open, using a declarative, compositional approach to create complex models from subcomponents. The framework can be tightly or loosely coupled with multiple vegetation models, it can be set up to evaluate a range of ecosystem service indicators, and it can be calibrated for a range of different landscape-scale case studies and modelling styles. The framework is released under an Open Source licence, and can be freely re-used and modified to form the basis of new models. We illustrate this with two case studies implemented using Aporia, exploring different socio-economic scenarios and behavioural characteristics on the land use decisions of Swiss and Scottish farmers. We also discuss the benefits of frameworks in terms of their flexibility, expandability, verification and transparency.     

Compressive sensing and adaptive direct sampling in hyperspectral imaging

Hyperspectral imaging (HSI) is an emerging technique, which provides the continuous acquisition of electro-magnetic waves, usually covering the visible as well as the infrared light range. Many materials can be easily discriminated by means of their spectra rendering HSI an interesting method for the reliable classification of contents in a scene. Due to the high amount of data generated by HSI, effective compression algorithms are required. The computational complexity as well as the potentially high number of sensors render HSI an expensive technology. It is thus of practical interest to reduce the number of required sensor elements as well as computational complexity – either for cost or for energy reasons. In this paper, we present two different systems that acquire hyperspectral images with less samples than the actual number of pixels, i.e. in a low dimensional representation. First, a design based on compressive sensing (CS) is explained. Second, adaptive direct sampling (ADS) is utilized to obtain coefficients of hyperspectral images in the 3D (Haar) wavelet domain, simplifying the reconstruction process significantly. Both approaches are compared with conventionally captured images with respect to image quality and classification accuracy. Our results based on real data show that in most cases only 40% of the samples suffice to obtain high quality images. Using ADS, the rate can be reduced even to a greater extent. Further results confirm that, although the number of acquired samples is dramatically reduced, we can still obtain high classification rates.     

An exact wide field digital imaging algorithm

Abstract

A new imaging algorithm is presented for Synthetic Aperture Radar (SAR) that is exact in the sense that it is capable of producing a complex image with excellent geometrical, radiometrical and phase fidelity. No interpolations or significant approximations are required, yet the method accomplishes range curvature correction over the complete range swath. The key to the approach is a quadratic phase perturbation of the range linearly frequency modulated signals while in the range signal, azimuth frequency transform (Doppler) domain. Range curvature correction is completed by a phase multiply in the two-dimensional frequency domain. Other operations required are relatively conventional. The method is generalizable to imaging geometries encountered in squint and spotlight SAR, inverse SAR, seismics, sonar, and tomography.     

Light field imaging: models,calibrations, reconstructions,and applications

Light field imaging is an emerging technology in computational photography areas. Based on innovative designs of the imaging model and the optical path, light field cameras not only record the spatial intensity of threedimensional (3D) objects, but also capture the angular information of the physical world, which provides new ways to address various problems in computer vision, such as 3D reconstruction, saliency detection, and object recognition. In this paper, three key aspects of light field cameras, i.e., model, calibration, and reconstruction, are reviewed extensively. Furthermore, light field based applications on informatics, physics, medicine, and biology are exhibited. Finally, open issues in light field imaging and long-term application prospects in other natural sciences are discussed.     

An intelligent agent-based architecture for strategic information system applications

Strategic information systems (SIS) focus on the use of information system (IS) and information technology (IT) in the strategic management process in business organizations. The emphasis is on the strategic view of IS and IT and their impact on organizational strategy. Increased competition and advances in information technologies push for considerable structural changes in SIS. Agents, as autonomous entities which either work on their own or cooperate with others, and agent architectures have enormous potentials to be applied in such critical systems. In this article, first we investigate the very fundamental concepts of strategic information systems and intelligent agent technology. Then, the discussion continues on the specification of the characteristics and implementation issues of a typical SIS. Afterwards, we make use of these concepts and integrate them into a state-of-the-art, intelligent architecture for strategic information systems, called intelligent agent-based SIS. This is a comprehensive framework for a SIS in IT era which may be put into practice by a team of professionals in the near future. The graphical representation of this model is intended to help the reader understand the concept much better. After explaining the suggested model in full details, we introduce some support agents and specify their corresponding roles in an intelligent agent-based SIS architecture. Discussions and concluding remarks regarding the proposed system are provided at the end of the paper.     

An iris biometric system for public and personal use

Much work in the emerging field of biometrics has focused on identification applications. Biometrics offers the means to identify individuals without requiring that they carry ID cards and badges or memorize passwords. A leading concern in the development of such applications, however, is how to avoid rejecting valid users or approving imposters. The iris of the eye may provide a solution by offering a much more discriminating biometric than fingerprint or face recognition. The authors have designed and implemented an iris biometric system for personal electronic identification. Further, their system solves problems associated with public use devices such as automated teller machines, where habituated use is not the norm. The system also addresses personal-use arenas, such as home banking, and other Internet and network applications, such as secure business logons. The article describes the public- and personal-use systems, as well as relating statistical analysis and field trials to gauge the effectiveness of their system     

An integrated strategy to systematically understand and manage quality in use for web applications

The main goal in evaluating software quality is to ultimately improve its quality. In this work, we discuss SIQinU (Strategy for Improving Quality in Use), a six-phased evaluation-driven strategy for understanding and improving software quality requirements in a systematic way. Starting with quality in use (QinU), we design specific user tasks and context of use, and through identifying problems in QinU, we determine external quality (EQ) attributes that could be related to these QinU weakly performing indicators. Then, after deriving EQ attributes related to the QinU problems, we evaluate EQ and derive a benchmark to be used as a basis to make improvements. Once improvement recommendations are made based on poorly performing EQ indicators, a new version of the software application is completed and evaluated again for its EQ to establish a delta from the initial benchmark. Then, we re-evaluate QinU to determine the improvements resulting in QinU from the improvements made at the EQ level, thus leading to a cyclic strategy for improvement and development of relationships. SIQinU is a repeatable and consistent strategy which relies on: a conceptual framework (with ontological base), a process, and specific methods. In order to illustrate SIQinU, a real case study is conducted.     

An opinion on imaging challenges in phenotyping field crops

Almost all the world’s food is grown in open fields, where plant phenotypes can be very different from those observed in greenhouses. Geneticists and agronomists studying food crops routinely detect, measure, and classify a wide variety of phenotypes in fields that contain many visually distinct types of a single crop. Augmenting humans in these tasks by automatically interpreting images raises some important and nontrivial challenges for research in computer vision. Nonetheless, the rewards for overcoming these obstacles could be exceptionally high for today’s 7 billion people, let alone the 9.6 billion projected by 2050 (United Nations Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2012 Revision). To stimulate dialog between researchers in computer vision and those in genetics and agronomy, we offer our views on three computational challenges that are central to many phenotyping tasks. These are disambiguating one plant from another; assigning an individual plant’s organs to it; and identifying field phenotypes from those shown in archival images. We illustrate these challenges with annotated photographs of maize highlighting the regions of interest. We also describe some of the experimental, logistical, and photographic constraints on image collection and processing. While collecting the data sets needed for algorithmic experiments requires sustained collaboration and funding, the images we show and have posted should allow one to consider the problems, think of possible approaches, and decide on the next steps.     

Lens-free imaging for biological applications

Lens-free (or lensless) imaging is emerging as a cost-effective, compact, and lightweight detection method that can serve numerous biological applications. Lens-free imaging can generate high-resolution images within a field-portable platform, which is ideal for affordable point-of-care devices aiming at resource-limited settings. In this mini-review, we first describe different modes of operation for lens-free imaging and then highlight several recent biological applications of this emerging platform technology.     

An efficient compensatory neuro-fuzzy system and its applications

In this study, a compensatory neuro-fuzzy system (CNFS) is proposed. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of a neuro-fuzzy system to make the fuzzy logic system more adaptive and effective. Furthermore, an online learning algorithm that consists of structure learning and parameter learning is proposed to automatically construct the CNFS. The structure learning is based on the fuzzy similarity measure to determine the number of fuzzy rules, and the parameter learning is based on backpropagation algorithm to adjust the parameters. The simulation results have shown that (1) the CNFS model converges quickly and (2) the CNFS model has a lower root mean square (RMS) error than other models.