Number Of Spectral Bands Research Articles

Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology.

Optimization of wavelength selection for multispectral image acquisition: a case study of atrial ablation lesions

In vivo autofluorescence hyperspectral imaging of moving objects can be challenging due to motion artifacts and to the limited amount of acquired photons. To address both limitations, we selectively reduced the number of spectral bands while maintaining accurate target identification. Several downsampling approaches were applied to data obtained from the atrial tissue of adult pigs with sites of radiofrequency ablation lesions. Standard image qualifiers such as the mean square error, the peak signal-to-noise ratio, the structural similarity index map, and an accuracy index of lesion component images were used to quantify the effects of spectral binning, an increased spectral distance between individual bands, as well as random combinations of spectral bands. Results point to several quantitative strategies for deriving combinations of a small number of spectral bands that can successfully detect target tissue. Insights from our studies can be applied to a wide range of applications.

Evaluating the performance of the wavelet transform in extracting spectral alteration features from hyperspectral images

ABSTRACTWith the large number of spectral bands in hyperspectral images, the conventional classification methods commonly used for multispectral images are not effectively applicable. To overcome such difficulty, feature extraction methods could be used to reduce the dimension of hyperspectral images. In this study, the performance of the principal component analysis (PCA) as a widely used technique in feature extraction and the wavelet transform as a powerful decomposition tool on hyperspectral data is compared. In wavelet transform, a non-linear wavelet feature extraction was employed to select efficient features for more classification accuracy. Shortwave infrared bands of Hyperion imagery were selected as input data. The study area includes two well-known porphyry copper deposits, Darrehzar and Sarcheshmeh, located in the Iranian copper belt. Neural networks (NN), Support Vector Machine (SVM), and Spectral Angle Mapper (SAM) were used for multi-class classification based on hydrothermal alteration zones and then trained by mineral spectral features related to typical porphyry copper deposits. In the NN set-up used in this study, one hidden layer was used, with the number of neurons equal to the number of features in the input layer. Conjugate gradient backpropagation was employed as the network training function. Then, the efficiency of feature extraction methods was compared through their classification accuracies. According to the results, although the highest classification accuracy for the PCA method occurs in lower numbers of extracted features compared to wavelet transform, the wavelet transform outperforms the PCA, based on confusion matrix classification. Moreover, NN is stronger than SVM and SAM in discriminating favourable alteration zones associated with porphyry copper mineralization using hyperspectral images.

Contextual Online Dictionary Learning for Hyperspectral Image Classification

Sparse representation (SR) has been successfully used in the classification of hyperspectral images (HSIs) by representing HSI pixels over a dictionary and yielding discriminative sparse coefficients. Most of SR-based classification methods construct the dictionary by directly using some labeled pixels as atoms. Such dictionary can lead to inefficient SR for large-sized HSIs, and may be incomplete when the number of labeled pixels is less than the number of spectral bands. This paper proposes a contextual online dictionary learning (DL) method for HSIs classification, which learns a dictionary over the whole image rather than few labeled pixels. The proposed method can effectively and efficiently improve the adaptive representation capability of different pixels with an online learning mechanism. Specifically, the contextual characteristics of the HSI are integrated with discriminative spectral information for online DL, i.e., pushing similar pixels in neighborhood to share similar sparse coefficients with respect to the well-learned dictionary. By this way, the obtained sparse coefficients are structured and discriminative. Finally, a traditional classifier, i.e., the linear support vector machine, is applied to the sparse coefficients, and the final classification results are obtained. Experimental results on real HSIs show the effectiveness of the proposed method.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

Sub-pixel classification of hydrothermal alteration zones using a kernel-based method and hyperspectral data; A case study of Sarcheshmeh Porphyry Copper Mine and surrounding area, Kerman, Iran

Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called training samples and end-members. The performance of algorithms using training samples can be negatively affected by the curse of dimensionality. This problem is usually observed in the hyperspectral image classification, especially when a low number of training samples, compared to the large number of spectral bands of hyperspectral data, are available. An unmixing method that is not highly impressed by the curse of dimensionality is a promising option. Among all the methods used, Support Vector Machine (SVM) is a more robust algorithm used to overcome this problem. In this work, our aim is to evaluate the capability of a regression mode of SVM, namely Support Vector Regression (SVR), for the sub-pixel classification of alteration zones. As a case study, the Hyperion data for the Sarcheshmeh, Darrehzar, and Sereidun districts is used. The main classification steps rely on 20 field samples taken from the Darrehzar area divided into 12 and 8 samples for training and validation, respectively. The accuracy of the sub-pixel maps obtained demonstrate that SVR can be successfully applied in the curse of dimensional conditions, where the size of the training samples (12) is very low compared to the number of spectral bands (165).

Spectrum Section Preallocation for Cooperative Sensing and Transmission in Cognitive Radio Ad Hoc Networks

A mobile device with embedded cognitive radios (CRs) is integrated into a novel low-cost massive antenna configuration multihop or device-to-device (D2D) wireless network that temporarily uses any portion of the spectrum for transmissions. However, the number of spectral bands (SBs) that a CR can sense within a specific time is limited. Furthermore, CR node transmissions interfere with one another. This paper proposes a method both for cooperative sensing and transmission with preallocated disjoining of spectrum sections (SSs) from neighboring links to increase the likelihood of matching available SBs. This minimizes sensing and negotiating overheads and reduces interference from a viewpoint of high-level graph model and mathematical formulation. The multiple-constraint routing with the resource allocation for cooperative sensing problem is proven to be NP-hard. The resource allocation method is compared with existing SB postallocation methods (i.e., random SB allocation and fixed SB allocation) and preallocation methods (i.e., link-based SS allocation and multiple parallel SB allocation). The proposed methods handle the resource preallocation mechanism with a centralized solution but handle the sensed results with a decentralized solution to reduce the negotiating and selecting time. We evaluate the performance of the system capacity, end-to-end transmission delay, fairness index, and standard deviation, thereby verifying the quality of the proposed method in terms of interferences, the fading effect, and other environment-dependent conditions. The results of the proposed method are more favorable than those of other methods.

Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction

Hyperspectral data classification has shown potential in many applications. However, a large number of spectral bands cause overfitting. Methods for reducing spectral bands, e.g., linear discriminant analysis, require matrix inversion. We propose a semidefinite programming for linear discriminants regularized difference (SLRD) criterion approach that does not require matrix inversion. The paper establishes a classification error bound and provides experimental results with ten methods over six hyperspectral datasets demonstrating the efficacy of the proposed SLRD technique.

A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water ( γ w ) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of γ w . Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the γ w extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals.

Penalized logistic regression for classification and feature selection with its application to detection of two official species of Ganoderma

Two species of Ganoderma, Ganoderma lucidum (G. lucidum) and Ganoderma sinense (G. sinense) have been widely used as traditional Chinese herbal medicine for their high medicinal value. Recent studies show that the two species differ in levels of their main active compounds triterpenoids though both have antitumoral effects. An effective and simple analytical method using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to discriminate between the two species is of essential importance for its quality assurance and medicinal value estimation. In this study three penalized logistic regression models, weighted least absolute shrinkage and selection operator (Lasso), elastic net and weighted fusion, using ATR-FTIR spectroscopy have been explored for the purpose of classification and interpretation. The weighted fusion model incorporating spectral correlation structure allowed an automatic selection of a small number of spectral bands and achieved an excellent overall classification accuracy of 99% in discriminating spectra of G. lucidum from that of G. sinense. Its classification performance was superior to that of the weighted Lasso model and elastic net model. The automatic selection of informative spectral features results in substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of Ganoderma regarding its anti-cancer effects.

HYBRID OPTIMIZATION OF OBJECT-BASED CLASSIFICATION IN HIGH-RESOLUTION IMAGES USING CONTINOUS ANT COLONY ALGORITHM WITH EMPHASIS ON BUILDING DETECTION

Abstract. Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and Support Vector Machine classifiers

ABSTRACTNitrogen (N) is a crucial element in sustaining oil palm production. However, assessing N status of tall perennial crops such as oil palm is complex and not as straightforward as assessing annual crops, due to complex N partitioning, age, and larger amounts of respiratory loads. Hence, the objectives of this study were to evaluate the potential of spectral measurements obtained from leaf scale and machine learning approaches as a rapid tool for quantifying oil palm N status. This study involved assessing the performance of discriminant analysis (DA) and Support Vector Machine (SVM) classifiers for discriminating spectral bands sensitive to N sufficiency levels and comparing the predictive accuracy of DA and SVM for classifying N status of immature and mature oil palms. The experiment was conducted on immature Tenera seedlings (13 months old) and mature Tenera palm stands (9 and 12 years old) that were arranged in Randomized Complete Block Design with treatments varied from 0 to 2 kg N. Generally, the discriminant function of both classifiers was age-dependent. A clear trade-off between the classifiers’ number of spectral bands and their accuracies was observed; the DA with a larger number of optimal spectral bands could discriminate N sufficiency levels of all maturity classes with higher accuracies compared to the SVM, yet the latter could produce reasonable accuracies with a lesser number of spectral bands. N status of all maturity classes could be classified satisfactorily with SVM (71–88%) via the satellite-simulated blue and green bands, signifying the possibility to develop spectral index or an N-sensitive sensor for oil palm.

Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

Generalized Aggregation of Sparse Coded Multi-Spectra for Satellite Scene Classification

Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of spectral bands than a general image, thereby permitting the multi-spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-spectral analysis and explores the middle-level statistics of spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-spectral analysis firstly learns the multi-spectral prototypes (codebook) for representing any pixel-wise spectral data, and then, based on the learned codebook, a sparse coded spectral vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded spectral vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods, but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is very impressive compared with the state-of-the-art methods for satellite scene classification.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images

BackgroundHyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison.ResultsInstead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to 0.998pm 0.003 for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR.ConclusionsAn advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.

The Impact of the Spectral Band Number and Width on the Oil Pollution Diagnostics on Earth Surface by Laser Fluorescence Method

Using the remote sensing methods is the most promising for day-to-day control of oil pollution. The laser-induced fluorescence method provides efficient detection and classification of oil pollutions. To monitor oil pollutions on the earth surface is more complicated than on the water one because of lower fluorescence intensity and interfering fluorescence of natural objects available on the earth surface. Properties of oil pollution classifiers depend largely on the number and positions of spectral bands of fluorescence registration. Reducing the number of spectral bands allows us to diminish computation complexity and cost of equipment. In some cases the reduction increases classification accuracy. The number of spectral bands can be reduced through increasing their width. The paper presents mathematical modeling of oil pollution detection and classification. The experimentally obtained fluorescence spectra of oil pollutions on different substrates were used as input data. The k-nearest neighbors algorithm was used to detect and classify oil pollutions. Cross validation was applied in mathematical modeling. The mathematical modeling results have shown that for oil pollutions detection using over 8 spectral bands (band width less than 50 nm) a classification error rate does not depend on the further increasing number of the spectral bands. As to the type classification of oil pollutions (4 classes), an increasing width of the spectral bands up to 60 nm (the number of spectral bands reduced up 7) does not lead to a significantly decreasing overall classification accuracy. In the case of the sort classification of oil pollutions (8 classes) a local maximum of the overall accuracy has been observed at 25-30 nm width of the spectral band (14-16 spectral bands). The spectral resolution improvement (increasing the number of bands) does give an essentially increasing accuracy. The paper has shown that to detect and classify oil pollutions on the earth surface there is no need to acquire fluorescence with high spectral resolution and numerous spectral bands.

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

ABSTRACTNew 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.

Covariance-based band selection and its application to near-real-time hyperspectral target detection

The matched filter (MF) and adaptive coherence estimator (ACE) show great effectiveness in hyperspectral target detection applications. Practical applications in which on-board processing is generally required demand real-time or near-real-time implementation of these detectors. However, a vast amount of hyperspectral data may make real-time or near-real-time implementation of the detection algorithms almost impossible. Band selection can be one of the solutions to this problem by reducing the number of spectral bands. We propose a new band selection method that prioritizes spectral bands based on their influence on the detection performance of the MF and ACE and discards the least influential bands. We validate the performance of our method using real hyperspectral images. We also demonstrate our technique on near-real-time detection tasks and show it to be a feasible approach to the tasks.

The Computational Studies of Plasmon Interaction

In this paper, an interaction of metal nanoparticles that appears in the extinction spectra was investigated. The mutual coupling between the nanoparticles, the effect of size difference, and the interparticle separation in silver nanoparticle dimers are studied by computer discrete dipole approximation methods. The obtained results show that nanoparticle interaction results in the distinct collective modes, known as the low-energy bonding modes and the higher-energy antibounding modes. The spectral position of the modes is analyzed as a function of the ratio of interparticle distance to particle size that reduces the dependency on the particle size itself. The optical spectra of nanoparticles that form the fractal cluster were investigated. It was found that the number of spectral bands increase with the growth of the number of nanoparticles in the fractal cluster, which are described within the plasmon hybridization model.

Modeling and analysis for the image mapping spectrometer**Project supported by the National Natural Science Foundation of China (Grant Nos. 61635002 and 61307020) and the Changjiang Scholars and Innovative Research Team in University (PCSIRT) Program, China.

The snapshot image mapping spectrometer (IMS) has advantages such as high temporal resolution, high throughput, compact structure and simple reconstructed algorithm. In recent years, it has been utilized in biomedicine, remote sensing, etc. However, the system errors and various factors can cause cross talk, image degradation and spectral distortion in the system. In this research, a theoretical model is presented along with the point response function (PRF) for the IMS, and the influence of the mirror tilt angle error of the image mapper and the prism apex angle error are analyzed based on the model. The results indicate that the tilt angle error causes loss of light throughput and the prism apex angle error causes spectral mixing between adjacent sub-images. The light intensity on the image plane is reduced to 95% when the mirror tilt angle error is increased to (). The prism apex error should be controlled within the range of 0–36 (0.01 ) to ensure the designed number of spectral bands, and avoid spectral mixing between adjacent images.