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Convolutional neural networks (CNNs) have a strong capacity to extract deep-level features from data. However, the standard convolution (SC) only considers the intensity information and ignores the spatial gradient information. Since spatial difference features are more robust to illumination invariance, this letter proposes a multiscale central difference convolutional (MSCDC) network for hyperspectral anomaly detection. Specifically, we use central difference convolution (CDC) to combine intensity and gradient information. This solution improves the representation ability of hyperspectral images (HSIs) and enhances the difference between the background and the anomalies. Furthermore, to fully utilize local spatial information and adapt to targets with different sizes, CDC kernels of three different sizes are used to capture high-, mid-, and low-level features, respectively. Finally, an SC is used to fuse multiscale features and obtain more reliable spatial information. Compared with five popular hyperspectral anomaly detection methods on four real-world HSI datasets, the proposed MSCDC exhibits excellent performance. [ABSTRACT FROM AUTHOR]

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]

Deep learning techniques have shown increasing potential in change detection (CD) in hyperspectral images (HSIs). However, most deep learning-based existing methods for HSI CD follow a patch-based local learning framework and concentrate on binary CD. In this letter, we propose an end-to-end semantic feature fused global learning (SFFGL) framework for HSI multiclass CD (MCD). In SFFGL, a global spatialwise fully convolutional network (FCN), which introduces a spatial attention mechanism (PAM) between encoder and decoder, is designed to effectively exploit the global spatial information from the whole HSIs and achieve patch-free inference. PAM can adaptively extract global spatialwise feature representation. In the model training stage, a global hierarchical (GH) sampling strategy is introduced to obtain diverse gradients during backpropagation for more robust performance. The semantic–spatial feature fusion ($\text{S}^{2}\text{F}^{2}$) unit is designed to effectively fuse the enhanced spatial context information in the encoder and the semantic information in the decoder. More importantly, a semantic feature enhancement module (SEM) is proposed to weaken the influence of the unchanged regional background on the change regional foreground, thus further improving the accuracy. Experimental results on two benchmark HSI datasets demonstrate the effectiveness of the proposed SFFGL. [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]

Subpixel mapping based on the Hopfield neural network (HNN) is a technique to handle mixed pixels for obtaining the spatial distribution information of land cover. However, the original low-resolution remote sensing image may contain some uncertainties, such as the diversity of the land cover classes and the limitation of the resolution of the satellite sensor, the existing HNN is unable to fully utilize the prior information of the original image. In order to resolve this problem, an improved HNN (I-HNN) is proposed in this letter. In the proposed I-HNN, additional prior information of the original image is supplied by adding a new processing path to the existing HNN. To validate the effectiveness of the proposed method, two experiments are conducted on real hyperspectral images. The obtained results demonstrate that the proposed I-HNN outperforms the existing HNN. Moreover, the I-HNN does not require any auxiliary data. [ABSTRACT FROM AUTHOR]

In this letter, a novel subpixel sharpening for soft-then-hard subpixel mapping (SPM) is proposed. First, the fractional images for each class are, respectively, derived by spectral unmixing followed by spatial interpolation and by spectral interpolation followed by spectral unmixing. Bilinear and bicubic interpolation is used as the spatial and spectral interpolation methods. The fractional images for each class are then integrated together using the appropriate weighting parameter. Finally, the integrated finer fractional images are used to allocate hard class labels to subpixels. The proposed method is fast and does not need any prior spatial structure information. Experiments on two actual hyperspectral images show that the proposed method produces higher accuracy results than the existing algorithms. Moreover, both the spatial and spectral information is fully utilized to improve the accuracy of the SPM results. [ABSTRACT FROM PUBLISHER]

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]