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The very high spectral resolution in hyperspectral images (HSIs) offers an opportunity to detect subtle land-cover changes. However, the availability of HSIs acquired from different platforms requires the development of change detection (CD) methods capable of processing HSIs with different spatial resolutions. In this article, we propose a general end-to-end subpixel convolution-based residual network (SPCNet) to accomplish the CD task between high spatial resolution (HR) and low spatial resolution (LR) HSIs. To effectively tackle the resolution matching issue, a super-resolution (SR) block with an efficient subpixel convolution layer is introduced to upscale the LR feature maps into HR maps. The subpixel convolution layer can fully explore the subpixel context information by learning an array of upscaling filters. Moreover, the designed SPC module is embedded into the LR branch to generate more discriminative representations. More importantly, the SPC module as a plug-and-play unit has the potential to be embedded into other baseline networks to enhance the feature learning capability. Experimental results on four HSI datasets demonstrate the effectiveness of the proposed SPCNet. [ABSTRACT FROM AUTHOR]

In this article, in order to improve the power quality and decrease the consumed power of an induction motor (IM), which is supplied by an alternate battery-powered system, an algorithm that can be used to save its energy consumption has been proposed. The control mechanism is used to reduce the wasted energy caused by IMs start-up current based on bounding its optimal starting voltage. The proposed algorithm includes two parts: First, use branch-and-bound theory to derive the control rule of the IMs start voltage and give corresponding offline calculation procedure framework. Second, according to the control rule, adjust online voltage of the zinc–bromine flow battery, which is used to feed the IM. Due to the proposed approach, the minimum start-up voltage of the battery-powered system can be predicted and by which one the IMs surge current has been suppressed online. Moreover, the relationship of the output voltage of the battery and inverter supply to the load current has been derived by modeling the battery-powered system. The experiments demonstrate that compared with before optimization, the amount of energy consumption of the IM supplied by zinc–bromine flow battery decreases by 8.5% and corresponding indicator parameters of the power quality can reach the standard IEC TS62749-2015. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) always contain abundant spectral and spatial information. Most of the existing deep learning-based hyperspectral anomaly detection methods consider spectral differences between the background and anomalies, and the local spatial information is usually ignored. To make complete use of the spatial–spectral information, this article proposed a local spatial–spectral information-integrated semisupervised two-stream network (LS3T-Net) for hyperspectral anomaly detection. The two-stream network comprises an adaptive convolution and fully connected network and a variational autoencoder (VAE). The adaptive convolution and fully connected network is used to extract the local spatial features of patches, while VAE is trained to learn spectral information close to the background pixels. Furthermore, the detection maps from the two-stream network are incorporated through a process combining the benefits of spatial learning and spectral learning. This enhances the ability to separate the background and anomalies and suppress the false alarm. The experimental results for six real HSI datasets reveal that LS3T-Net can produce more accurate detection results than seven popular benchmark methods. [ABSTRACT FROM AUTHOR]

Deep learning-based methods (e.g., convolutional neural network (CNN)-based methods) have shown increasing potential in hyperspectral image (HSI) change detection (CD). However, the recent advances in CNN-based methods in HSI CD tasks are mostly devoted to designing more complex architectures or adding additional hand-designed blocks. This increases the number of parameters making model training difficult. In this article, we propose an end-to-end residual self-calibrated network (RSCNet) to increase the accuracy of HSI CD. To fully exploit the spatial information, the proposed RSCNet method adaptively builds interspatial and interspectral dependencies around each spatial location with fewer extra parameters and reduced complexity. Moreover, the introduced self-calibrated convolution (SCConv) helps to generate more discriminative representations by heterogeneously exploiting convolutional filters nested in the convolutional layer. The designed RSC module can explicitly incorporate richer information by introducing response calibration operation. The experiments on four bitemporal HSI datasets demonstrated that the proposed RSCNet method is more accurate than ten widely used benchmark methods. [ABSTRACT FROM AUTHOR]

Due to the limitations of hardware technology and budget constraints, there always exists a tradeoff between spatial and spectral resolutions in a hyperspectral image (HSI). Because of the limited spatial resolution, mixed pixels are a common issue in HSIs, and consequently, some targets appear as subpixels. The effectiveness of hyperspectral target detection is affected greatly by the subpixel targets, especially when the size of the targets is small. In this article, we proposed a double dictionary-based nonlinear representation model for hyperspectral subpixel target detection (DDNRTD). DDNRTD represents HSIs with a nonlinear model based on background and target dictionaries, which fully considers the spatial property of background and targets and can separate background and targets reliably, especially for small-sized subpixel targets. In addition, we designed an over-completed background dictionary construction strategy to represent the background part more effectively, which integrates spectral angle distance (SAD) with sparse representation. Experiments on two simulated and five real HSI datasets showed that the proposed DDNRTD method produced more accurate detection results than six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep learning methods, especially convolutional neural network (CNN)-based methods, have shown promising performance for hyperspectral image (HSI) change detection (CD). It is acknowledged widely that different spectral channels and spatial locations in input image patches may contribute differently to CD. However, they are treated equally in existing CNN-based approaches. To increase the accuracy of HSI CD, we propose an end-to-end Siamese CNN (SiamNet) with a spectral–spatial-wise attention (SSA-SiamNet) mechanism. The proposed SSA-SiamNet method can emphasize informative channels and locations and suppress less informative ones to refine the spectral–spatial features adaptively. Moreover, in the network training phase, the weighted contrastive loss function is used for more reliable separation of changed and unchanged pixels and to accelerate the convergence of the network. SSA-SiamNet was validated using four groups of bitemporal HSIs. The accuracy of CD using the SSA-SiamNet was found to be consistently greater than for ten benchmark methods. [ABSTRACT FROM AUTHOR]

Government environment is the main determinant in attracting private investment into public–private partnership (PPP) projects, especially in developing countries. Red tape, an indicator of a government's efficiency, plays a critical role in private investment in PPP projects. Reasonable levels of red tape can enhance government transparency and promote private investment, while excessive red tape usually represents low governance efficiency and imposes further risk on private investors. This article explores how developing countries’ red tape affects private investment in PPP projects by examining the moderating effect of corruption. Analyzing a database of 308 PPP projects in 111 developing countries, the study reveals an inverted U-shaped relationship between red tape and private investment. Corruption weakens the positive relationship between red tape and private investment at low levels of red tape and mitigates their negative relationship at high levels. The study integrates the inconsistent results of previous research that postulated either a positive or negative relationship between red tape and private investment by proposing a nonlinear model. It also theorizes the moderating effect of corruption based on real management practice and illustrates its mechanism in absorbing private investment in PPP projects. [ABSTRACT FROM AUTHOR]

High-resolution gradient power amplifiers are required to generate high-fidelity gradient fields in magnetic resonance imaging (MRI) systems. Various aspects of gradient power amplifiers have been the subject of research in the past years, however, systematic analysis and design methods for its resolution improvement have not been sufficiently addressed. This article addresses this research gap with comprehensive analysis and design methods for resolution improvement. First, a method for systematic resolution characterization of the MRI gradient power amplifier with gradient coil is proposed. Second, noise modeling with respect to the critical aspects in the system resolution degradation is developed and discussed. Third, the resolution improvement methods of bandwidth optimization, pseudorandom code modulation, and utilizing silicon carbide (SiC) switching power devices are proposed and analyzed, achieving a resolution at the one part in ${10^6}$ level. Finally, the resolution improvement methods are experimentally validated in a 1000 V/400 A SiC gradient power amplifier. The experimental results show a 3.6 times reduction in the peak spurious signals and resolution improvement at all current levels and pulse lengths, compared to a conventional insulated gate bipolar transistor GPA. These results prove the validity of the proposed methods for resolution improvement in a high-power MRI gradient power amplifier, enabling future high-power and high-resolution amplifier designs. [ABSTRACT FROM AUTHOR]

In magnetic resonance imaging (MRI), high-power gradient power amplifiers (GPAs) are required to drive the gradient coils to generate strong and high-fidelity gradient fields. High precision is an essential requirement for the GPAs since precision directly impacts MRI imaging quality. Various aspects of the GPA have been the subject of research in the past years; however, high-precision GPA control that meets the stringent requirements of MRI applications is still a challenge. This article proposes a novel multi-rational-delay variables state space control method and an efficient out-of-band signal injection method to achieve GPA control accuracy at the level of one part per million at MVA output power levels. First, a systematic modeling and design method of the state space controller for high-power GPA is introduced utilizing state vectors with multi-rational-delay variables. This method improves the GPA dynamic performance significantly. Second, an efficient out-of-band signal injection method is presented to further improve the control precision at low output current, enabling the fulfillment of the challenging high-precision MRI requirements over the full GPA output range. Finally, the high-precision control method is validated in a GPA demonstrator with 500 A/1000 V output. Key results are a 30% improvement in current pulse reproducibility with respect to the conventional control method and a factor of 2.5 less noise at low currents. These experimental results validate the proposed novel method for the high-precision control of GPAs in MRI applications and prove its capability to contribute to significantly improved MRI image quality. [ABSTRACT FROM AUTHOR]

Studies on the classification of hyperspectral images (HSIs) based on deep learning are in full swing, especially the spectral–spatial dependent global learning (SSDGL) framework, which is both efficient and robust. However, the global convolutional long short-term memory (GCL) module under this framework fails to take full consideration of the spectral characteristics contained in HSIs, and the hierarchically balanced (H-B) sampling strategy introduced in this framework prevents the training process from converging smoothly. In this article, we develop a novel regularized spectral–spatial global learning (RSSGL) framework. Compared with SSDGL, the proposed framework mainly makes three improvements. Above all, aiming at the problem that the GCL module used in SSDGL cannot fully tap the local spectral dependence, we apply 3-D convolution to the gated units of long short-term memory (LSTM) as an alternative to the GCL module for adjacent and nonadjacent spectral dependencies learning. Furthermore, to extract the most discriminative features, an improved statistical loss regularization term is developed, in which we introduce a simple but effective diversity-promoting condition to make it more reasonable and suitable for deep metric learning in HSI classification. Finally, to effectively address the performance oscillation caused by the H-B sampling strategy, the proposed framework adopts an early stopping strategy to save and restore the optimal model parameters, making it more flexible and stable. Experiments conducted on three representative datasets show that the proposed RSSGL has superior classification performance compared with the existing relatively excellent research methods. The source code is released at https://github.com/swiftest/RSSGL. [ABSTRACT FROM AUTHOR]

In recent years, convolutional neural networks (CNNs) have achieved excellent performance in hyperspectral image classification and have been widely used. However, the convolution kernel used in traditional CNN has the limitation of single scale, which is not conducive to the improvement of hyperspectral classification performance. In addition, training a classification network of high-dimensional data based on limited labeled samples is still one of the challenges of hyperspectral image classification. To solve the above problems, a hyperspectral image classification method based on expansion convolution network (ECNet) is proposed. The expansion convolution injects holes into the standard convolution kernel to expand the receptive field (RF), so as to extract more context features. Because the shallow features of hyperspectral images contain more location and detail information, while the deep features contain stronger semantic information, in order to further enhance the correlation between deep and shallow information, inspired by ResNet, a similar feedback block (SFB) is introduced on the basis of ECNet, and the deep features and shallow features are fused through this feedback mechanism. Thus, an improved version of ECNet method is obtained, which is called feedback ECNet (FECNet). This study was tested on four commonly used hyperspectral datasets [i.e., Indian Pine (IP), Pavia University (UP), Kennedy Space Center (KSC), and Salinas Valley (SV)] and on a higher resolution and complexly distributed land cover dataset (University of Houston (HT). The experimental results show that the proposed method has better classification performance than some state-of-the-art methods, which shows that FECNet has a certain potential in hyperspectral image classification. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have exhibited extraordinary achievements in hyperspectral image (HSI) classification due to their detailed representation of features. However, the improvement of classification accuracy often leads to an evident increase in the complexity of the model, which makes it challenging for the model with the state-of-the-art performance to be applied in the actual scene. Considering MobileNetV3 as a lightweight feature extractor, this article proposes a model suitable for HSI classification based on MobileNetV3. To decrease the problem of massive redundant calculations in the existing spatial attention module, this article proposes a more concise and efficient spatial attention module based on the visual feature maps experiment. Besides, multiclass focal-loss is applied to solve the problem that the difficulty of classification varies for each sample. The experimental results demonstrate that in the case of using very few training sets, the proposed model can tremendously reduce the number of calculations and parameters while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

Due to the influences of imaging conditions, spectral imagery can be coarse and contain a large number of mixed pixels. These mixed pixels can lead to inaccuracies in the land-cover class (LC) mapping. Super-resolution mapping (SRM) can be used to analyze such mixed pixels and obtain the LC mapping information at the subpixel level. However, traditional SRM methods mostly rely on spatial correlation based on linear distance, which ignores the influences of nonlinear imaging conditions. In addition, spectral unmixing errors affect the accuracy of utilized spectral properties. In order to overcome the influence of linear and nonlinear imaging conditions and utilize more accurate spectral properties, the SRM based on spatial–spectral correlation (SSC) is proposed in this work. Spatial correlation is obtained using the mixed spatial attraction model (MSAM) based on the linear Euclidean distance. Besides, a spectral correlation that utilizes spectral properties based on the nonlinear Kullback–Leibler distance (KLD) is proposed. Spatial and spectral correlations are combined to reduce the influences of linear and nonlinear imaging conditions, which results in an improved mapping result. The utilized spectral properties are extracted directly by spectral imagery, thus avoiding the spectral unmixing errors. Experimental results on the three spectral images show that the proposed SSC yields better mapping results than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The subpixel mapping (SPM) technique can handle coarse fractional images derived by unmixing coarse original hyperspectral (HS) image to produce a fine land cover map at the subpixel scale. A popular SPM approach is a two-step model. It first increases the spatial resolution of coarse fractional images by subpixel sharpening to produce fine fractional images and then assigns class labels to each subpixel by the class allocation method. However, there is only a single processing path of the current SPM algorithm, and the information type of the fine fractional images is not rich. To enrich the information type, SPM based on dual processing paths (DPP) is proposed. DPP contains two processing paths, namely spatial–spectral path and multiscale path. First, the coarse original HS image and the high spatial resolution multispectral image are fused by component substitution to produce the fine fractional images with more spatial–spectral information in the spatial–spectral path. At the same time, deep Laplacian pyramid networks are used to obtain the fine fractional images with multiscale information in the multiscale path. The fine fractional images from the two paths are then integrated to generate the improved fraction images with multiscale spatial–spectral information. Finally, the multiscale spatial–spectral information is utilized to allocate class labels by the class allocation method. Experimental results on three real HS remote sensing data show that the proposed DPP outperforms the other SPM methods, demonstrating the effectiveness of the use of DPP in enriching the information type of the fine fractional images. [ABSTRACT FROM AUTHOR]

Remote sensing images provide a wealth of information for a variety of applications, but it is at the expense of huge data. In this paper, we present a novel compression method based on optimum adaptive directional lifting (OADL) with content-dependent binary tree codec. First, the OADL model is designed, which calculates the optimal prediction direction of each image block and performs the weighted directional adaptive interpolation during the process of lifting. The former aims to reduce the edge and texture energy of the non-horizontal and non-vertical directions in the high-frequency subbands, and the latter focuses on preserving the directional characteristics of remote sensing images as much as possible. Second, a binary tree codec with content-based adaptive scanning is introduced, which can provide different scanning orders and scanning manners among and within subbands, respectively. In addition, it can encode more significant coefficients at the same bit rate. Experimental results show that, compared with other scan-based compression methods, the proposed compression method can always provide better coding performance in terms of some evaluation indexes. [ABSTRACT FROM AUTHOR]

An approach for saving energy consumption of the electric submersible motor (ESM) has been proposed based on combining the thermal field model with electrical control. For decreasing the high energy consumption that has become increasingly serious in oil fields, one of our works is to increase the identification accuracy of the ESM temperature by overcoming the influence of the long cable. The thermal field model of the long cable has been derived and used to correct the equivalent circuit of the ESM first; then the procedure that can be used to identify the rotor temperature is given based on improving the calculation accuracy and efficiency of the rotor slot harmonics. The control rule for modulating the inverter frequency to match the actual oil production was simulated by summarizing the experiment results. Theory analysis and experiments denote that the average identification error of the rotor temperature has been controlled within 1° C and the average power consumption of the ESM may be decreased for about 12% by the proposed approach. This work can effectively reduce the power consumption and provide overheat protection of the ESM. [ABSTRACT FROM AUTHOR]

Multiple subpixel shifted images (MSIs) from the same area can be incorporated to improve the accuracy of soft-then-hard subpixel mapping (STHSPM). In this paper, a novel method that derives higher resolution MSIs with more spatial–spectral information (MSI-SS) is proposed. First, coarse MSIs produce two high-resolution MSIs for each class respectively by two paths at the same time. The spatial path produces the high-resolution MSIs by soft classification followed by interpolation. And, the other high-resolution MSIs are derived from the spectral path by interpolation followed by soft classification. Then the higher resolution MSIs with more spatial–spectral information for each class are derived by integrating the aforementioned two kinds of high-resolution MSIs by the appropriate weight. Finally, the integrated higher resolution MSIs for each class are used to allocate hard class labels to subpixels. The proposed method is fast and takes more spatial–spectral information of the original MSIs into account. Experiments on three real hyperspectral remote sensing images show that the proposed method produce higher SPM accuracy result. [ABSTRACT FROM PUBLISHER]

Subpixel mapping (SPM) of hyperspectral remote sensing imagery is a promising technique for deriving fine mapping result by classification at fine spatial resolution. There is a type of algorithm for SPM, namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at subpixel level and then allocates classes for subpixel according to the soft attribute values. However, the fraction images derived from spectral unmixing are of less prior information of original hyperspectral remote sensing imagery and there are lots of errors in SPM result due to the limitation of spectral unmixing technology currently available. In this paper, a framework based on subpixel resolution thematic map, namely, super-resolution then classification (STC) is proposed to improve mapping result. In the proposed framework, a maximum a posteriori (MAP) model associated with the endmembers of interest (EOI), namely, T-MAP-SR is applied to the original coarse imagery to derive a high-resolution imagery with generous prior information. Then fine mapping result can be derived from the high-spatial resolution imagery by the available classification methods. Experiments show that the proposed framework can produce higher mapping accuracy result and protect the classes of interest (COI). [ABSTRACT FROM PUBLISHER]

Due to the limited spatial resolution of multispectral/hyperspectral data, mixed pixels widely exist and various spectral unmixing techniques have been developed for information extraction at the subpixel level in recent years. One of the challenging problems in spectral mixture analysis is how to model the data of a primary class. Given that the within-class spectral variability (WSV) is inevitable, it is more realistic to associate a group of representative spectra with a pure class. The unmixing method using the extended support vector machines (eSVMs) has handled this problem effectively. However, it has simplified WSV in the mixed cases. In this paper, a further development of eSVMs is presented to address two problems in multiple-endmember spectral mixture analysis: 1) one mixed pixel may be unmixed into different fractions (model overlap); and 2) one fraction may correspond to a group of mixed pixels (fraction overlap). Then, spectral unmixing resolution (SUR) is introduced to characterize how finely the mixture in a mixed pixel can be quantified. The quantitative relationship between SUR and WSV of endmembers is derived via a geometry analysis in support vector machine feature space. Thus, the possible SUR can be estimated when multiple endmembers for each class are given. Moreover, if the requirement of SUR is fixed, the acceptance level of WSV is then limited, which can be used as a guide to remove outliers and purify endmembers for each primary class. Experiments are presented to illustrate model and fraction overlap problems and the application of SUR in uncertainty analysis of spectral unmixing. [ABSTRACT FROM PUBLISHER]

While a single spectrum is often used to present a pure class, it is more realistic to consider intra-class spectral variation and model a pure class using a group of its representative spectra. In line with this consideration, crisp unmixing accuracy assessment, where unmixing performance is assessed using a mean squared error of the estimated endmember fractions, can be misleading. In this paper, alterative spectral unmixing assessment methods are introduced to account for the uncertainty contained in the spectral measurements and during the ground truth data collection. Two fuzzy measures are developed to assess unmixing performance. One is fuzzy unmixing fraction error for a realistic assessment and the other is pixel level unmixing accuracy to provide a good quantitative understanding of the unmixing success rates spatially. To demonstrate and illustrate how they work, the two fuzzy measures are applied to evaluate the performance of several spectral unmixing methods including both single spectrum based and multiple spectra based algorithms. Crisp assessments and fuzzy results at various tolerance levels are presented and compared. Based on the realistic measures proposed, it is found the recent developed unmixing method with extended Support Vector Machines outperforms other algorithms tested. [ABSTRACT FROM PUBLISHER]

Subpixel mapping (SPM) is a technique for predicting the spatial distribution of land cover classes in remote sensing images at a finer spatial resolution level than those of the input images. Indicator cokriging (ICK) has been found to be an effective and efficient SPM method. The accuracy of this model, however, is limited by insufficient constraints. In this paper, the accuracy of the ICK-based SPM model is enhanced by using additional information gained from multiple shifted images (MSIs). First, each shifted image is utilized to compute the conditional probability of class occurrence at any fine spatial resolution pixel (i.e., subpixel) using ICK, and a set of conditional probability maps for all classes are generated for each image. The multiple ICK-derived conditional probability maps are then integrated, according to the estimated subpixel shifts of MSI. Lastly, class allocation at the subpixel scale is implemented to produce SPM results. The proposed algorithm was tested on two synthetic coarse spatial resolution remote sensing images and a set of real Moderate Resolution Imaging Spectroradiometer (MODIS) data. It was evaluated both visually and quantitatively. The experimental results showed that more accurate SPM results can be generated with MSI than with a single observed coarse image in conventional ICK-based SPM. In addition, the accuracy of the proposed method is higher than that of the existing Hopfield neural network (HNN)-based SPM and the HNN with MSI. [ABSTRACT FROM PUBLISHER]

Spectral unmixing is one of the important techniques for hyperspectral data processing. The analysis of spectral mixing is often based on a linear, fully constrained (FC) (i.e., nonnegative and sum-to-one mixture proportions), and least squares criterion. However, the traditional iterative processing of FC least squares (FCLS) linear spectral mixture analysis (LSMA) (FCLS-LSMA) is of heavy computational burden. Recently developed geometric LSMA methods decreased the complexity to some degree, but how to further reduce the computational burden and completely meet the FCLS criterion of minimizing the unmixing residual needs to be explored. In this paper, a simple distance measure is proposed, and then, a new geometric FCLS-LSMA method is constructed based on the distance measure. The method is in line with the FCLS criterion, free of iteration and dimension reduction, and with very low complexity. Experimental results show that the proposed method can obtain the same optimal FCLS solution as the traditional iteration-based FCLS-LSMA, and it is much faster than the existing spectral unmixing methods, particularly the traditional iteration-based method. [ABSTRACT FROM AUTHOR]

Endmember extraction (EE) is a prerequisite task for spectral analysis of hyperspectral imagery. In all kinds of EE algorithms, maximum simplex volume-based ones, such as simplex growing algorithm (SGA) and N-FINDR algorithm, have been widely used for their fully automated and efficient performance. However, implementation of the algorithms needs dimension reduction of original data, and the algorithms include innumerable volume calculation. This leads to a low speed of the algorithms and thus becomes a limitation to their applications. In this paper, a simple distance measure is presented, and then, fast SGA and fast N-FINDR algorithm are constructed based on a proposed distance measure, which is free of dimension reduction and makes use of distance measure instead of volume evaluation to speed up the algorithm. The complexity of the proposed methods is compared with the original algorithms by theoretical analysis. Experiments show that the implementation of the two improved EE algorithms is much faster than that of the two original maximum simplex volume-based EE algorithms. [ABSTRACT FROM PUBLISHER]