A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method

The position of the inflexion point in the red edge region (680 to 780 nm) of the spectral reflectance signature, termed the red edge position (REP), is affected by biochemical and biophysical parameters and has been used as a means to estimate foliar chlorophyll or nitrogen content. In this paper, we report on a new technique for extracting the REP from hyperspectral data that aims to mitigate the discontinuity in the relationship between the REP and the nitrogen content caused by the existence of a double-peak feature on the derivative spectrum. It is based on a linear extrapolation of straight lines on the far-red (680 to 700 nm) and NIR (725 to 760 nm) flanks of the first derivative reflectance spectrum. The REP is then defined by the wavelength value at the intersection of the two lines. The output is a REP equation, REP = − (c₁ − c₂) / (m₁ − m₂), where c₁ and c₂, and m₁ and m₂ represent the intercepts and slopes of the far-red and NIR lines, respectively. Far-red wavebands at 679.65 and 694.30 nm in combination with NIR wavebands at 732.46 and 760.41 nm or at 723.64 and 760.41 nm were identified as the optimal combinations for calculating nitrogen-sensitive REPs for three spectral data sets (rye canopy, and maize leaf and mixed grass/herb leaf stack spectra). REPs extracted using this new technique (linear extrapolation method) showed high correlations with a wide range of foliar nitrogen concentrations for both narrow and wider bandwidth spectra, being comparable with results obtained using the traditional linear interpolation, polynomial and inverted Gaussian fitting techniques. In addition, the new technique is simple as is the case with the linear interpolation method, but performed better than the latter method in the case of maize leaves at different developmental stages and mixed grass/herb leaves with a low nitrogen concentration.     

Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance

Accurate detection of heavy metal-induced stress on the growth of crops is essential for agricultural ecological environment and food security. This study focuses on exploring singularity parameters as indicators for a crop's Zn stress level assessment by applying wavelet analysis to the hyperspectral reflectance. The field in which the experiment was conducted is located in the Changchun City, Jilin Province, China. The hyperspectral and biochemistry data from four crops growing in Zn contaminated soils: rice, maize, soybean and cabbage were collected. We performed a wavelet transform to the hyperspectral reflectance (350-1300 nm), and explored three categories of singularity parameters as indicators of crop Zn stress, including singularity range (S_R), singularity amplitude (S_A) and a Lipschitz exponent (α). The results indicated that (i) the wavelet coefficient of the fifth decomposition level by applying Daubechies 5 (db5) mother wavelets proved successful for identifying crop Zn stress; the S_R of crop concentrated on the region was around 550-850 nm of the spectral signal under Zn stress; (ii) the S_R stabilized, but S_A and α had developed some variations at the growth stages of the crop; (iii) the S_R, S_A and α were found among four crop species differentially; and moreover the S_A increased in relation to an increase in the S_R of crop species; (iv) the α had a strong non-linear relationship with the Zn concentration (R²:0.7601-0.9451); the S_A had a strong linear relationship with Zn concentration (R²:0.5141-0.8281). Singularity parameters can be used as indicators for a crop's Zn stress level as well as offer a quantitative analysis of the singularity of spectrum signal. The wavelet transform technique has been shown to be very promising in detecting crops with heavy metal stress.     

Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data

Crop residues left on agricultural lands after harvest play an important role in controlling and protecting soil against water and wind erosion. One challenge of remote sensing is to differentiate crop residues from bare soil and crop cover, especially when the residues have been weathered and/or when the crop cover phenology is more advanced. Several techniques for mapping and estimating crop residues exist in the literature. However, these methods are time consuming and not suited for quantitative evaluation. They have the disadvantage of being less rigorous and accurate because they do not consider the spectral mixture of different materials in the same pixel. In this study, the potential of hyperspectral (Probe-1) and multispectral high spatial resolution (IKONOS) data were compared for estimating and mapping crop residues on agricultural lands using the constrained linear spectral mixture analysis approach. Image data were spectrally and radiometrically calibrated, atmospherically corrected, as well as geometrically rectified. Pure spectral signatures of residues, bare soil and crop cover were manually extracted from image data based on prior knowledge of the fields. Percent (fraction) cover for each sampling point was extracted using unmixing and validated against ground reference measurements. The best results were achieved for the crop cover (index of agreement (D) = 0.92 and root mean square error (RMSE) = 0.09) adjusted for the impurity of the endmembers canola, pea and wheat, followed by the wheat residues (D = 0.76 and RMSE = 0.12). Considering only the wheat residues in fields with a canola crop, D increases to 0.86. The soil fractions were generally underestimated with D = 0.72, and no significant improvements could be made after adjusting for the shadow effect. The estimations from the IKONOS data were poorer for the same cover types (residues: D = 0.40 and RMSE = 0.24; crop: D = 0.51 and RMSE = 0.38; soil: D = 0.58 and RMSE = 0.29). Relative to the IKONOS data, the better performance of the hyperspectral data is mainly due to the improved spectral band characteristics, especially in the SWIR, which is sensitive to the residues (lignin and cellulose absorption features), soil and crop cover.     

A novel approach of steganography by using strong edge detection and chaos theory

Multimedia Tools and Applications - The growth of the Internet and rapid development of digital devices and network technology led to increase the risks and threats of attackers, therefore the...     

A new approach for thresholding spectral change detection using multispectral and hyperspectral image data,a case study over Sokolov,Czech republic

Change detection and multitemporal analyses aim to detect changes occurring over a specific geographical area using two or more images acquired at two or more different times. In this article, we present a new thresholding approach for unsupervised change detection. This approach focuses on determining the threshold that discriminates between change and no-change pixels. The differences between pixels in the two images are associated with real changes or noise. We propose a thresholding scheme that separates the threshold into two parts: (1) a spectral domain threshold that accounts for errors related to sensor stability, atmospheric conditions, and data-processing variations, and (2) a spatial domain threshold associated with georectification errors. We demonstrate our method using both multispectral Landsat images and airborne imaging spectroscopy HyMap images. The results show that the spectral domain threshold gives high detection capabilities with moderate false-alarm rate. Adding the spatial domain threshold to the spectral domain threshold reduces the false-alarm rates while maintaining good detection capabilities.     

Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion

In this paper, we explored fusion of structural metrics from the Laser Vegetation Imaging Sensor (LVIS) and spectral characteristics from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) for biomass estimation in the Sierra Nevada. In addition, we combined the two sensors to map species-specific biomass and stress at landscape scale. Multiple endmember spectral mixture analysis (MESMA) was used to classify vegetation from AVIRIS images and obtain sub-pixel fractions of green vegetation, non-photosynthetic vegetation, soil, and shade. LVIS metrics, AVIRIS spectral indices, and MESMA fractions were compared with field measures of biomass using linear and stepwise regressions at stand (1 ha) level. AVIRIS metrics such as water band indices and shade fractions showed strong correlation with LVIS canopy height (r² = 0.69, RMSE = 5.2 m) and explained around 60% variability in biomass. LVIS variables were found to be consistently good predictors of total and species specific biomass (r² = 0.77, RMSE = 70.12 Mg/ha). Prediction by LVIS after species stratification of field data reduced errors by 12% (r² = 0.84, RMSE = 58.78 Mg/ha) over using LVIS metrics alone. Species-specific biomass maps and associated errors created from fusion were different from those produced without fusion, particularly for hardwoods and pines, although mean biomass differences between the two techniques were not statistically significant. A combined analysis of spatial maps from LVIS and AVIRIS showed increased water and chlorophyll stress in several high biomass stands in the study area. This study provides further evidence that lidar is better suited for biomass estimation, per se, while the best use of hyperspectral data may be to refine biomass predictions through a priori species stratification, while also providing information on canopy state, such as stress. Together, the two sensors have many potential applications in carbon dynamics, ecological and habitat studies.     

Technical Note: Crop stress detection using AVIRIS hyperspectral imagery and artificial neural networks

The objectives of this study were to compare the results of artificial neural network (ANN) and standard vegetation algorithm processing to distinguish nutrient stress from in-field controls, and determine whether nutrient stress might be distinguished from water stress in the same test field. The test site was the US Department of Agriculture's Variable Rate Application (VRAT) site, Shelton, Nebraska. The VRAT field was planted in corn with test plots that were differentially treated with nitrogen (N). The field contained four replicates, each with N treatments ranging from 0 kg ha^?1 to 200 kg ha^?1 in 50 kg ha^?1 increments. Low-altitude (3 m pixel) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery (224 bands) was collected over the site. Ground data were collected to support image interpretation. An ANN was applied to the AVIRIS image data for detection of crop and water stress. Known vegetation indices were used as a baseline for comparison against ANN-based stress detection. The resulting comparison found that ANN methods provided a heightened capability to separate stressed crops from in-field, non-stressed controls and was sensitive to differences in nutrient- and water-stressed field regions.     

Solving sparse linear systems of equations using the modified digraph approach

We propose two different algorithms which depend on the modified digraph approach for solving a sparse system of linear equations. The main feature of the algorithms is that the solution of a sparse system of linear equations can be expressed exactly if all the non-zero entries, including the right-hand side, are integers and if none of the products exceeds the size of the largest integer that can be represented in the arithmetic of the computer used. The implementation of the algorithms is tested on five problems. The results are compared with those obtained using an algorithm proposed earlier. It is shown that the efficiency with which a sparse system of linear equations can be analysed by a digital computer using the proposed modified digraph approach as a tool depends mainly on the efficiency with which semifactors and k-semifactors are generated. Finally, in our implementation of the proposed algorithms, the input sparse matrix is stored using a row-ordered list of a modified uncompressed storage scheme.     

Dust storm detection and monitoring using multi-temporal INSAT-3A-CCD data

Sand and dust on being agitated by winds often trigger huge dust storms which are characterized by high aerosol optical depth (AOD), τ_a, lower Angström exponent (α) and near-zero visibility condition. Geostationary platforms are most suitable for studying the dynamic behaviour of such events. In the present study we have used multi-temporal Indian National Satellite System (INSAT-3A-CCD) data on an hourly basis to characterize a massive dust storm that occurred during 15–16 October 2008 over the northern Arabian Sea. An algorithm has been developed to estimate AOD and the Angström exponent using the near-infrared (NIR) channel of INSAT-3A-CCD. AOD at 550 nm and the Angström exponent, α(810, 550 nm) were computed during 14–18 October 2008. The mean value of τ_a(550 nm) was found to be 1.03 during the dust event, almost two to three times higher than in dust-free conditions. Similarly, the mean α(810, 550 nm) value reduced almost by half during the dust event, indicating the presence of larger particles. In general an increasing trend of AOD values was noticed till early afternoon and a decreasing trend was observed thereafter. INSAT-derived atmospheric parameters were compared with Moderate Resolution Imaging Spectroradiometer-derived AOD and α for similar observation period. A good correlation for τ_a was found with R ²?=?0.92 and root mean square error (RMSE)?=?0.054. However, a R ² value of 0.74 with RMSE of 0.13 was obtained for α. Analysis of air mass back-trajectory indicates the source of this dust event originates from deserts of Afghanistan and Pakistan.     

Joint approach using satellite techniques for slope instability detection and monitoring

A joint approach using satellite techniques was applied to two different regions (Sellas and Chalkeio villages) of Peloponissos (Greece) in order to detect and monitor slope instability. In the context of the research effort, a GPS campaign network, along with one permanent GPS station and a corner reflector (CR) network, was established at each region. From the two GPS campaigns that were carried out, ground displacements in the north and east components for Sellas region reached a magnitude of 9 and 8 mm, respectively, whereas for Chalkeio they were of the order of 1 cm and 8 mm, respectively. These results, however, are still preliminary and need validation from additional GPS campaigns that are planned to be carried out in future. The temporal resolution provided by the position time series of the permanent GPS stations highlighted the main features of both instability phenomena, that is, sensitivity at both horizontal components of motion for the Sellas region and slow linear trends for the Chalkeio region. The achieved precision of the daily solutions for both permanent GPS stations was found to be 1–3 mm for the horizontal components and 5–8 mm for the vertical components. Regarding the preliminary study of differential synthetic aperture radar (SAR) interferometry (DInSAR) in CR network, each reflector has been identified in SAR imagery, but at present the volume of SAR acquisitions is not adequate for providing safe deformation and error estimations. On the other hand, the permanent scatterers interferometry and small baselines subset (SBAS) techniques revealed a discontinuity in retrospective deformation rate along the observed rupture of Chalkeio village of almost 6 mm year^?1.     

A data constrained approach for brain tumour detection using fused deep features and SVM

Multimedia Tools and Applications - The identification of MR images of the brain with tumours is one of the most critical tasks of any brain tumour (BT) detection system. Interestingly, because of...     

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.     

A soft multi-core architecture for edge detection and data analysis of microarray images

As configurable processing advances, elements from the traditional approaches of both hardware and software development can be combined by incorporating customized, application-specific computational resources into the processor’s architecture, especially in the case of field-programmable-gate-array-based systems with soft-processors, so as to enhance the performance of embedded applications. This paper explores the use of several different microarchitectural alternatives to increase the performance of edge detection algorithms, which are of fundamental importance for the analysis of DNA microarray images. Optimized application-specific hardware modules are combined with efficient parallelized software in an embedded soft-core-based multi-processor. It is demonstrated that the performance of one common edge detection algorithm, namely Sobel, can be boosted remarkably. By exploiting the architectural extensions offered by the soft-processor, in conjunction with the execution of carefully selected application-specific instruction-set extensions on a custom-made accelerating co-processor connected to the processor core, we introduce a new approach that makes this methodology noticeably more efficient across various applications from the same domain, which are often similar in structure. With flexibility to update the processing algorithms, an improvement reaching one order of magnitude over all-software solutions could be obtained. In support of this flexibility, an effective adaptation of this approach is demonstrated which performs real-time analysis of extracted microarray data; the proposed reconfigurable multi-core prototype has been exploited with minor changes to achieve almost 5× speedup.     

Tracking rigid bodies using only position data: A shadowing filter approach based on newtonian dynamics

Tracking a moving object like a ship, vehicle, aircraft or even an animal or human is challenging especially when given only noisy observations of the path. To successfully track such an object the method used needs to infer the unknown information, for example the acceleration, from noisy position data. Here we present such a method based on a shadowing filter algorithm. In comparison with other filters such as Kalman and Particle filters the shadowing filter solves the tracking problem based on deterministic dynamics instead of statistics. The algorithm presented shows how to track rigid bodies having an unknown moment of inertia and we validate the performance of the filter and explore how the two important parameters of the filter impact on its performance.     

Retrieval of crop chlorophyll content and leaf area index from decompressed hyperspectral data: the effects of data compression

The objective of this study is to evaluate whether the retrieval of the leaf chlorophyll content and leaf area index (LAI) for precision agriculture application from hyperspectral data is significantly affected by data compression. This analysis was carried out using the hyperspectral data sets acquired by Compact Airborne Spectrographic Imager (CASI) over corn fields at L'Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000 and over corn, soybean and wheat fields at the former Greenbelt farm (Agriculture and Agri-Food Canada) in three intensive field campaigns during the summer of 2001. Leaf chlorophyll content and LAI were retrieved from the original data and the reconstructed data compressed/decompressed by the compression algorithm called Successive approximation multi-stage vector quantization (SAMVQ) at compression ratios of 20:1, 30:1, and 50:1. The retrieved products were evaluated against the ground-truth.In the retrieval of leaf chlorophyll content (the first data set), the spatial patterns were examined in all of the images created from the original and reconstructed data and were proven to be visually unchanged, as expected. The data measures R², absolute RMSE, and relative RMSE between the leaf chlorophyll content derived from the original and reconstructed data cubes, and the laboratory-measured values were calculated as well. The results show the retrieval accuracy of crop chlorophyll content is not significantly affected by SAMVQ at the compression ratios of 20:1, 30:1, and 50:1, relative to the observed uncertainties in ground truth values. In the retrieval of LAI (the second data set), qualitative and quantitative analyses were performed. The results show that the spatial and temporal patterns of the LAI images are not significantly affected by SAMVQ and the retrieval accuracies measured by the R², absolute RMSE, and relative RMSE between the ground-measured LAI and the estimated LAI are not significantly affected by the data compression either.     

Detecting anomalies and de-noising monitoring data from sensors: A smart data approach

When monitoring safety levels in deep pit foundations using sensors, anomalies (e.g., highly correlated variables) and noise (e.g., high dimensionality) exist in the extracted time series data, impacting the ability to assess risks. Our research aims to address the following question: How can we detect anomalies and de-noise monitoring data from sensors in real time to improve its quality and use it to assess geotechnical safety risks? In addressing this research question, we develop a hybrid smart data approach that integrates Extended Isolation Forest and Variational Mode Decomposition models to detect anomalies and de-noise data effectively. We use real-life data obtained from sensors to validate our smart data approach while constructing a deep pit foundation. Our smart data approach can detect anomalies with a root mean square error and signal-to-noise ratio of 0.0389 and 24.09, respectively. To this end, our smart data approach can effectively pre-process data enabling improved decision-making and the management of safety risks.     

A probabilistic SVM approach for hyperspectral image classification using spectral and texture features

New hyperspectral sensors can collect a large number of spectral bands, which provide a capability to distinguish various objects and materials on the earth. However, the accurate classification of these images is still a big challenge. Previous studies demonstrate the effectiveness of combination of spectral data and spatial information for better classification of hyperspectral images. In this article, this approach is followed to propose a novel three-step spectral–spatial method for classification of hyperspectral images. In the first step, Gabor filters are applied for texture feature extraction. In the second step, spectral and texture features are separately classified by a probabilistic Support Vector Machine (SVM) pixel-wise classifier to estimate per-pixel probability. Therefore, two probabilities are obtained for each pixel of the image. In the third step, the total probability is calculated by a linear combination of the previous probabilities on which a control parameter determines the efficacy of each one. As a result, one pixel is assigned to one class which has the highest total probability. This method is performed in multivariate analysis framework (MAF) on which one pixel is represented by a d-dimensional vector, d is the number of spectral or texture features, and in functional data analysis (FDA) on which one pixel is considered as a continuous function. The proposed method is evaluated with different training samples on two hyperspectral data. The combination parameter is experimentally obtained for each hyperspectral data set as well as for each training samples. This parameter adjusts the efficacy of the spectral versus texture information in various areas such as forest, agricultural or urban area to get the best classification accuracy. Experimental results show high performance of the proposed method for hyperspectral image classification. In addition, these results confirm that the proposed method achieves better results in FDA than in MAF. Comparison with some state-of-the-art spectral–spatial classification methods demonstrates that the proposed method can significantly improve classification accuracies.