HyMap hyperspectral remote sensing to detect hydrocarbons

The ability of airborne hyperspectral remote sensing methods to detect hydrocarbons was investigated by the Federal Institute of Geosciences and Natural Resources. Reference areas of defined geometry and chemical properties were prepared, e.g. sandy soil, oil-contaminated soil, grass, plastic tarpaulins. The aim of the study was to collect hyperspectral data from these areas and simultaneously determine their spectra with the infrared intelligent spectroradiometer GER Mark V IRIS. The data corrections and further processing were based on data provided by a field spectrometer. This study showed that airborne hyperspectral remote sensing can be used to detect hydrocarbons efficiently. Hydrocarbon-bearing substances are characterized by typical absorption features in the spectra. The availability of the high signal-to-noise-ratio HyMap hyperspectral imaging system permits these features to be recognized in the pixel spectra even if they are not very pronounced. Oil contaminated soil and other materials containing hydrocarbons can be detected and located directly and unambiguously by image processing focused on the spectral characteristics of hydrocarbons. By this procedure, atmospheric correction of the HyMap data is not necessary.     

Detecting the responses of Masson pine to acid stress using hyperspectral and multispectral remote sensing

In this study, we detected the spectrum and vegetation index responses of Masson pine to acid stress using ground-based hyperspectral radiometry and satellite-based multispectral remote sensing. From the hyperspectral detection, we found that the spectral reflectance of stressed Masson pine increased with increase in acidity in the visible region, while an opposite result was found in the near-infrared (NIR) region. The simulated normalized difference vegetation index (NDVI) derived from hyperspectral data of Masson pine presented higher values under weaker acid stress in general. Similar results were observed by satellite-derived NDVI across four acidity gradient transects in subtropical China. Both annual average NDVI and inter-annual NDVI trends of Masson pine forest increased with increase in annual average precipitation acidity (pH), indicating that acid stress would inhibit the growth of Masson pine under certain environmental conditions. However, there are limitations and uncertainties in our present work, and the integration of long-term ground-based and satellite-based observations of vegetation growth and acidity deposition is urgently needed.     

Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress

Chlorophyll content can be used as an indicator to monitor crop diseases. In this article, an experiment on winter wheat stressed by stripe rust was carried out. The canopy reflectance spectra were collected when visible symptoms of stripe rust in wheat leaves were seen, and canopy chlorophyll content was measured simultaneously in laboratory. Continuous wavelet transform (CWT) was applied to process the smoothed spectral and derivative spectral data of winter wheat, and the wavelet coefficient features obtained by CWT were regarded as the independent variable to establish estimation models of chlorophyll content. The hyperspectral vegetation indices were also regarded as the independent variable to build estimation models. Then, two types of models above-mentioned were compared to ascertain which type of model is better. The cross-validation method was used to determine the model accuracies. The results indicated that the estimation model of chlorophyll content, which is a multivariate linear model constructed using wavelet coefficient features extracted by Mexican Hat wavelet function processing the smoothed spectrum (WSMH1 and WSMH2), is the best model. It has the highest estimation accuracy with modelled coefficient of determination (R²) of 0.905, validated R² of 0.913, and root mean square error (RMSE) of 0.288 mg fg^?1. The univariate linear model built by wavelet coefficient feature of WSMH1 is secondary and the modelled R² is 0.797, validated R² is 0.795, and RMSE is 0.397 mg fg^?1. Both estimation models are better than those of all hyperspectral vegetation indices. The research shows that the feature information of canopy chlorophyll content of winter wheat can be captured by wavelet coefficient features which are extracted by the method of CWT processing canopy reflectance spectrum data. Therefore, it could provide theoretical support on detecting diseases of crop by remote sensing quantitatively estimating chlorophyll content.     

Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data

DNA microarrays make it possible to study simultaneously the expression of thousands of genes in a biological sample. Univariate clustering techniques have been used to discover target genes with differential expression between two experimental conditions. Because of possible loss of information due to use of univariate summary statistics, it may be more effective to use multivariate statistics. We present multivariate normal mixture model based clustering analyses to detect differential gene expression between two conditions.Deviating from the general mixture model and model-based clustering, we propose mixture models with specific mean and covariance structures that account for special features of two-condition microarray experiments. Explicit updating formulas in the EM algorithm for three such models are derived. The methods are applied to a real dataset to compare the expression levels of 1176 genes of rats with and without pneumococcal middle-ear infection to illustrate the performance and usefulness of this approach. About 10 genes and 20 genes are found to be differentially expressed in a six-dimensional modeling and a bivariate modeling, respectively. Two simulation studies are conducted to compare the performance of univariate and multivariate methods. Depending on data, neither method can always dominate the other. The results suggest that multivariate normal mixture models can be useful alternatives to univariate methods to detect differential gene expression in exploratory data analysis.     

Using origin analysis to detect merging and splitting of source code entities

Merging and splitting source code entities is a common activity during the lifespan of a software system; as developers rethink the essential structure of a system or plan for a new evolutionary direction, so must they be able to reorganize the design artifacts at various abstraction levels as seems appropriate. However, while the raw effects of such changes may be plainly evident in the new artifacts, the original context of the design changes is often lost. That is, it may be obvious which characters of which files have changed, but it may not be obvious where or why moving, renaming, merging, and/or splitting of design elements has occurred. In this paper, we discuss how we have extended origin analysis (Q. Tu et al., 2002), (M.W. Godfrey et al., 2002) to aid in the detection of merging and splitting of files and functions in procedural code; in particular, we show how reasoning about how call relationships have changed can aid a developer in locating where merges and splits have occurred, thereby helping to recover some information about the context of the design change. We also describe a case study of these techniques (as implemented in the Beagle tool) using the PostgreSQL database system as the subject.     

基于EXCEL的高光谱影像光谱响应分析

高光谱遥感侧重于从光谱维角度对影像信息进行分析与处理。由于目前高光谱数据的处理技术跟不上数据获取技术,而已有的成熟的多光谱影像处理技术并不适合于处理高光谱数据,因此利用EXCEL软件展开了高光谱影像的地物光谱重建、光谱特征及其相关性分析、光谱微分计算、光谱向量相似性度量和信息提取等研究,并基于PHI（Pushbroom Hyperspectral Imager）航空高光谱影像像元光谱维矢量进行了光谱响应分析,实现信息监测和识别。     

Land transitions from multivariate time series: using seasonal trend analysis and segmentation to detect land-cover changes

The detection of land-cover change over large areas using short time series is a challenging and important task in global change studies. This paper introduces a novel method, called the Segment-based Detection of Trends and Change (SDTC), to determine areas that are undergoing land-cover changes. The method is illustrated in the State of Alaska over the 2001–2009 time period using three time series derived from Moderate Resolution Spectroradiometer (MODIS) imagery: the normalized difference vegetation index (NDVI), albedo, and land surface temperature (LST). SDTC extends seasonal trend analysis (STA) by using segmentation in the detection process to combine multiple variables and to incorporate the spatial context. Segments labelled as ‘change’ correspond to groups of adjacent pixels with a majority of pixels undergoing significant temporal trends as defined by the Mann–Kendall (MK) procedure and STA. Segments correspond to landscape units in a less arbitrary manner than pixels because they represent groupings of adjacent areas undergoing similar temporal behaviour. Findings indicate that the use of MK in conjunction with segmentation exploits more fully the richness of the spatial context in the process of detection. Results suggest that SDTC is a useful method for detecting and characterizing land-cover change. The technique is conservative and eliminates the ‘salt and pepper’ effect by filtering noise and identifying areas of change. Using SDTC, we found that most areas of change in Alaska undergo between one and three significant changes and that increasing LST seasonality constitutes the most widespread type of change.     

Linking physiological responses,chlorophyll fluorescence and hyperspectral imagery to detect salinity stress using the physiological reflectance index in the coastal shrub,Myrica cerifera

Measurements of physiology, chlorophyll fluorescence and hyperspectral reflectance were used to detect salinity stress in the evergreen coastal shrub, Myrica cerifera on Hog Island, Virginia. Two experimental sites were used in our study, the oceanside of a M. cerifera thicket, which is exposed to sea spray, and the protected, leeside of the thicket. Using the physiological reflectance index (PRI), we were able to detect stress at both the canopy and landscape level. Monthly variations in stomatal conductance, photosynthesis, and relative water content indicated a strong summer drought response that was not apparent in chlorophyll fluorescence or in the water band index (WBI) derived from canopy and airborne reflectance measurements. In contrast, there were significant differences in both physiological measurements and tissue chlorides between the two sites used in the study, indicating salinity stress. This was reflected in measurements of PRI. There was a positive relationship between PRI measured at the canopy-level and light-adapted fluorescence (ΔF/F′_m; r² = 0.69). PRI was significantly lower on the oceanside of the Myrica cerifera thicket. PRI was not significantly related to NDVI (r² = 0.01) at the canopy-level and only weakly related (r² = 0.04) at the landscape-level, suggesting that the indices are independent. The chlorophyll index (CI) did not show any significant changes between the two sites. Frequency histograms of pixels sampled from airborne hyperspectral imagery revealed that the distribution of PRI was shifted to the right on the backside of the thicket relative to the oceanside and there was a significant difference between sites. These results suggest that PRI may be used for early identification of salt-stress and to identify areas across the landscape where community structure may change due to sea-level rise.     

Using CSP to detect errors in the TMN protocol

We use FDR (Failures Divergence Refinement), a model checker for CSP, to detect errors in the TMN protocol (M. Tatebayashi et al., 1990). We model the protocol and a very general intruder as CSP processes, and use the model checker to test whether the intruder can successfully attack the protocol. We consider three variants on the protocol, and discover a total of 10 different attacks leading to breaches of security     

Using hierarchical statistical analysis and deep neural networks to detect covert timing channels

Covert timing channels provide a mechanism to leak data across different entities. Manipulating the timing between packet arrivals is a well-known example of such approach. The time based property makes the detection of the hidden messages impossible by traditional security protecting mechanisms such as proxies and firewalls. This paper introduces a new generic hierarchical-based model to detect covert timing channels. The detection process consists of the analysis of a set of statistical metrics at consecutive hierarchical levels of the inter-arrival times flows. The statistical metrics considered are: mean, median, standard deviation, entropy, Root of Average Mean Error (RAME). A real statistical metrics timing channel dataset of covert and overt channel instances is created. The generated dataset is set to be either flat where the statistical metrics are calculated on all flows of data or hierarchal (5 levels of hierarchy were considered) where the statistical metrics are computed on sub parts of the flow as well. Following this method, 5 different datasets were generated, and used to train/test a deep neural network based model. Performance results about accuracy and model training time showed that the hierarchical approach outperforms the flat one by 4 to 10 percent (in terms of accuracy) and was able to achieve short model training time (in terms of seconds). When compared to the Support Vector Machine (SVM) classifier, the deep neural network achieved a better accuracy level (about 2.3% to 12% depends on the used kernel) and significantly shorter model training time (few seconds versus few 100’s of seconds). This paper also explores the importance of the used metrics in each level of the detection process.     

Using string matching to detect video transitions

The detection of shot boundaries in videos captures the structure of the image sequences by the identification of transitional effects. This task is important in the video indexing and retrieval domain. The video slice or visual rhythm is a single two-dimensional image sampling that has been used to detect several types of video events, including transitions. We use the longest common subsequence (LCS) between two strings to transform the video slice into one-dimensional signals obtaining a highly simplified representation of the video content. We also developed a chain of mathematical morphology operations over these signals leading to the detection of the most frequent video transitions, namely, cut, fade, and wipe. The algorithms are tested with success with various genres of videos.     

Using information flow analysis to detect implicit information leaks for web service composition

Information leak, which can undermine the compliance of web-service-composition business processes for some policies, is one of the major concerns in web service composition. We present an automated and effective approach for the detection of implicit information leaks in business process execution language (BPEL) based on information flow analysis. We introduce an adequate meta-model for BPEL representation based on a Petri net for transformation and analysis. Building on the concept of Petri net place-based noninterference, the core contribution of this paper is the application of a Petri net reachability graph to estimate Petri net interference and thereby to detect implicit information leaks in web service composition. In addition, a case study illustrates the application of the approach on a concrete workflow in BPEL notation.     

Using hyperspectral remote sensing to detect and quantify southeastern pine senescence effects in red-cockaded woodpecker (Picoides borealis) habitat

Conservation of threatened and endangered species requires maintenance of critical habitat. The red-cockaded woodpecker Picoides borealis (RCW) is a threatened bird species, endemic to the mixed conifer forests of the southeastern United States. RCW nests and forages preferentially in mature longleaf pine Pinus palustris, but also uses mature loblolly pine Pinus taeda and shortleaf pine Pinus echinata forests. In the last century, the extent of mature pine forests has been greatly reduced by logging. The RCW, in contrast to other woodpeckers, excavates nest cavities in living trees and senescence symptoms (year round leaf chlorosis and leaf mortality) have been observed in mature pine stands across the southeast. Widespread mortality of the mature pine forests would threaten the long-term survival of the RCW. We used airborne hyperspectral data across a portion of Ft. Benning Military Installation, Georgia, U.S.A., to determine if senescent trees can be identified and mapped and assess the likely persistence of mature pines in the RCW habitat. Univariate analysis of variance showed good separation between asymptomatic, senesced and dead physiological conditions with asymptomatic trees having significantly higher reflectance for all bands in the wavelength range between 0.719 and 1.1676 µm, senescent trees having significantly lower reflectance for bands in the range between 1.1927 and 1.3122 µm, and dead trees having significantly higher reflectance for bands in the range between 1.8151 and 1.9471 µm. Classification and Regression Tree (CART) models achieved correct classification rates and kappa statistics above 70%. CART models selected information from wavelength regions similar to those identified from the ANOVA, which likely explains their performance. Our aggregated CART model of tree senescence estimated that 141.4 ha (3%) of the study area is affected. RCW nests occurred in areas with significantly higher tree cover, and trees within foraging and home ranges did not have significantly more senescence than areas without RCW. These results indicate that although tree senescence is widespread, mortality is yet to significantly affect RCW habitat. Results and analysis of critical habitat similar to those exemplified in this study can extend our knowledge about the stressors of these important and imperiled components of biodiversity.     

Using hyperspectral radiometry to predict the green leaf area index of turfgrass

The green leaf area index (LAI) is an important indicator of the photosynthetic capacity of turfgrass canopies. The measurement of LAI is typically destructive and requires large plots to allow for multiple sampling dates. Hyperspectral radiometry may provide a rapid, non-destructive means for estimating LAI. Our objectives were to: (1) evaluate the utility of hyperspectral radiometry to predict the LAI of Kentucky bluegrass (Poa Pratensis L.); and (2) determine regions of the spectrum that provide the best LAI predictions. An empirical prediction model of spectral data for LAI was conducted with partial least squares regression (PLSR). The PLSR method created viable, first-iteration models for five of 11 sampling dates (the coefficient of determination (R²) is 0.52–0.85). Each model had its own set of factors that were analysed to determine their ‘weights’, or specific regions of the spectrum by which they were most strongly influenced. Second iterations of each model were then created using only those regions most strongly influenced, centred on 600, 690, 761, 960, 1330, and 1420 nm (±10 nm). Four of the five second-iteration models had LAI estimation capabilities greater than or similar to the first-iteration models (R² = 0.72–0.86), indicating that the information contained in all other wavelengths was redundant or irrelevant in regard to predictions of LAI. The robustness of prediction models varied over the growing season, possibly related to changes in canopy properties with environmental conditions. Results suggest hyperspectral radiometry has a significant potential to predict LAI in turfgrass, although different models may be required throughout the growing season.     

Using physiological signals to detect natural interactive behavior

Many researchers in the Human Robot Interaction (HRI) and Embodied Conversational Agents (ECA) domains try to build robots and agents that exhibit human-like behavior in real-world close encounter situations. One major requirement for comparing such robots and agents is to have an objective quantitative metric for measuring naturalness in various kinds of interactions. Some researchers have already suggested techniques for measuring stress level, awareness etc using physiological signals like Galvanic Skin Response (GSR) and Blood Volume Pulse (BVP). One problem of available techniques is that they are only tested with extreme situations and cannot according to the analysis provided in this paper distinguish the response of human subjects in natural interaction situations. One other problem of the available techniques is that most of them require calibration and some times ad-hoc adjustment for every subject. This paper explores the usefulness of various kinds of physiological signals and statistics in distinguishing natural and unnatural partner behavior in a close encounter situation. The paper also explores the usefulness of these statistics in various time slots of the interaction. Based on this analysis a regressor was designed to measure naturalness in close encounter situations and was evaluated using human-human and human-robot interactions and shown to achieve statistically significant distinction between natural and unnatural situations.     

Using compiler optimization techniques to detect equivalent mutants

Mutation analysis is a software testing technique that requires the tester to generate test data that will find specific, well-defined errors. Mutation testing executes many slightly differing versions, called mutants, of the same program to evaluate the quality of the data used to test the program. Although these mutants are generated and executed efficiently by automated methods, many of the mutants are functionally equivalent to the original program and are not useful for testing. Recognizing and eliminating equivalent mutants is currently done by hand, a time-consuming and arduous task. This problem is currently a major obstacle to the practical application of mutation testing. This paper presents extensions to previous work in detecting equivalent mutants; specifically, algorithms for determining several classes of equivalent mutants are presented, an implementation of these algorithms is discussed, and results from using this implementation are presented. These algorithms are based on data flow analysis and six compiler optimization techniques. Each of these techniques is described together with how they are used to detect equivalent mutants. The design of the tool and some experimental results using it are also presented.     

Using static symbolic execution to detect buffer overflows

Buffer overrun remains one of the main sources of errors and vulnerabilities in the C/C++ source code. To detect such kind of defects, static analysis is widely used. In this paper, we propose a path-sensitive static analysis based on symbolic execution with state merging. For buffers with compile-time-known sizes, we present an interprocedural path- and context-sensitive overrun detection algorithm that finds program points satisfying a proposed error definition. The described approach was implemented in the Svace static analyzer without significant loss of performance. On Android 5.0.2, these detectors generated 351 warnings, 64% of which were true positives. In addition, we describe a prototype of an intraprocedural heap buffer overflow detector and present an example of a defect found by this detector.