Hyperspectral Image Based Interpretable Feature Clustering Algorithm

Hyperspectral imagery encompasses spectral and spatial dimensions,reflecting the material properties of objects.Its application proves crucial in search and rescue,concealed target identification,and crop growth analysis.Clustering is an important method of hyperspectral analysis.The vast data volume of hyperspectral imagery,coupled with redundant information,poses significant challenges in swiftly and accurately extracting features for subsequent analysis.The current hyperspectral feature clustering methods,which are mostly studied from space or spectrum,do not have strong interpretability,resulting in poor comprehensibility of the algorithm.So,this research introduces a feature clustering algorithm for hyperspectral imagery from an interpretability perspective.It commences with a simulated perception process,proposing an interpretable band selection algorithm to reduce data dimensions.Following this,amulti-dimensional clustering algorithm,rooted in fuzzy and kernel clustering,is developed to highlight intra-class similarities and inter-class differences.An optimized P systemis then introduced to enhance computational efficiency.This system coordinates all cells within a mapping space to compute optimal cluster centers,facilitating parallel computation.This approach diminishes sensitivity to initial cluster centers and augments global search capabilities,thus preventing entrapment in local minima and enhancing clustering performance.Experiments conducted on 300 datasets,comprising both real and simulated data.The results show that the average accuracy(ACC)of the proposed algorithm is 0.86 and the combination measure(CM)is 0.81.

Mural Anomaly Region Detection Algorithm Based on Hyperspectral Multiscale Residual Attention Network

Mural paintings hold significant historical information and possess substantial artistic and cultural value.However,murals are inevitably damaged by natural environmental factors such as wind and sunlight,as well as by human activities.For this reason,the study of damaged areas is crucial for mural restoration.These damaged regions differ significantly from undamaged areas and can be considered abnormal targets.Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections.Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods.Thus,this study employs hyperspectral imaging to obtain mural information and proposes a mural anomaly detection algorithm based on a hyperspectral multi-scale residual attention network(HM-MRANet).The innovations of this paper include:(1)Constructing mural painting hyperspectral datasets.(2)Proposing a multi-scale residual spectral-spatial feature extraction module based on a 3D CNN(Convolutional Neural Networks)network to better capture multiscale information and improve performance on small-sample hyperspectral datasets.(3)Proposing the Enhanced Residual Attention Module(ERAM)to address the feature redundancy problem,enhance the network’s feature discrimination ability,and further improve abnormal area detection accuracy.The experimental results show that the AUC(Area Under Curve),Specificity,and Accuracy of this paper’s algorithm reach 85.42%,88.84%,and 87.65%,respectively,on this dataset.These results represent improvements of 3.07%,1.11%and 2.68%compared to the SSRN algorithm,demonstrating the effectiveness of this method for mural anomaly detection.

RGB-guided hyperspectral image super-resolution with deep progressive learning

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal resolution.Recently,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image guidance.Previous approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted priors.Recently,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing data.In this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image guidance.Specif-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image guidance.Then,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing scenes.The proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial constraint.It has a good general-isation capability,especially for blind HS image super-resolution.Comprehensive experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

Feasibility of hyperspectral analysis for discrimination of rabbit liver VX2 tumor

BACKGROUND Hepatocellular carcinoma is one of the most common malignant tumors worldwide. Currently, the most accurate diagnosis imaging modality for hepatocellular carcinoma is enhanced magnetic resonance imaging. However, it is still difficult to distinguish cirrhosis lesions, and novel diagnosis modalities are still needed.AIM To investigate the feasibility of hyperspectral analysis for discrimination of rabbit liver VX2 tumor.METHODS In this study, a rabbit liver VX2 tumor model was established. After laparotomy,under direct view, VX2 tumor tissue and normal liver tissue were subjected to hyperspectral analysis.RESULTS The spectral signature of the liver tumor was clearly distinguishable from that of the normal tissue, simply from the original spectral curves. Specifically, two absorption peaks at 600-900 nm wavelength in normal tissue disappeared but a new reflection peak appeared in the tumor. The average optical reflection at the whole waveband of 400-1800 nm in liver tumor was higher than that of the normal tissue.CONCLUSION Hyperspectral analysis can differentiate rabbit VX2 tumors. Further research will continue to perform hyperspectral imaging to obtain more information for differentiation of liver cancer from normal tissue.

Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion

Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history,science,culture,art and research.However,mainstream analytical methods are contacting and detrimental,which is unfavorable to the protection of cultural relics.This paper improves the accuracy of the extraction,location,and analysis of artifacts using hyperspectral methods.To improve the accuracy of cultural relic mining,positioning,and analysis,the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques.Firstly,region stitching algorithm based on the relative position of hyper spectrally collected data is proposed to improve stitching efficiency.Secondly,given the prominence of traditional HRNet(High-Resolution Net)models in high-resolution data processing,the spatial attention mechanism is put forward to obtain spatial dimension information.Thirdly,in view of the prominence of 3D networks in spectral information acquisition,the pyramid 3D residual network model is proposed to obtain internal spectral dimensional information.Fourthly,four kinds of fusion methods at the level of data and decision are presented to achieve cultural relic labeling.As shown by the experiment results,the proposed network adopts an integrated method of data-level and decision-level,which achieves the optimal average accuracy of identification 0.84,realizes shallow coverage of cultural relics labeling,and effectively supports the mining and protection of cultural relics.

Calibration and data restoration of light field modulated imaging spectrometer

A light field modulated imaging spectrometer （LFMIS） can acquire the spatial-spectral datacube of targets of interest or a scene in a single shot. The spectral information of a point target is imaged on the pixels covered by a microlens. The pixels receive spectral information from different spectral filters to the diffraction and misalignments of the optical components. In this paper, we present a linear spectral multiplexing model of the acquired target spectrum. A calibration method is proposed for calibrating the center wavelengths and bandwidths of channels of an LFMIS system based on the liner-variable filter （LVF） and for determining the spectral multiplexing matrix. In order to improve the accuracy of the restored spectral data, we introduce a reconstruction algorithm based on the total least square （TLS） approach. Simulation and experimental results confirm the performance of the spectrum reconstruction algorithm and validate the feasibility of the proposed calibrating scheme.

STRASS Dehazing:Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples

In this paper,we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS(Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples)dehazing,it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles.The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples.Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples.The final dehazed image is yielded after iterations of the high-pass filter.STRASS can be run directly without any machine learning.Extensive experimental results on datasets prove that STRASS surpass the state-of-the-arts.Image dehazing can be applied in the field of printing and packaging,our method is of great significance for image pre-processing before printing.

Design of Optical System for Small Long-Life Star Sensor

In order to realize a high-precision and continuous working function of a star sensor,we propose a new optical system design.Considering the difficulty of the manufacturing process,the entire optical system uses a complicated Petzval structure.In this paper,the key design elements of the optical system applied for star sensors are presented and the most important performance parameters are given.The ground test results show that the system can maintain excellent detection performance on a near-surface atmospheric platform.This study provides an optical system design scheme for a high-precision and continuous operating star sensor,as well as the theoretical basis for future in-atmosphere and continuous star detection technology.

A design of space robot multi-target capture algorithm based on DSP

To correctly capture spatial targets from cluttered and motive celestial background,a new Multi-Target Capture algorithm was proposed,which is a comparative difference algorithm based on the combination of centroid extraction and despun registration of efficient points.Moreover,this algorithm was applied in an image processing system based on the DSP featuring high speed and high performance.The procedures of image processing are as follows:first,label efficient points in the frame and extract their centroids;second,make appropriate despun registration,according to the reference rotation angles provided by Space Robot position system;third,translate and register centroid coordinates of efficient points in reference frames and get the registration points according to the principle that there are the most same centroid coordinates of efficient points when completely registered;finally,eliminate the same background points by using comparative difference method.The result shows that this image processing system can meet the needs of the whole system.

Abnormal event detection by a weakly supervised temporal attention network

Abnormal event detection aims to automatically identify unusual events that do not comply with expectation.Recently,many methods have been proposed to obtain the temporal locations of abnormal events under various determined thresholds.However,the specific categories of abnormal events are mostly neglect,which are important to help in monitoring agents to make decisions.In this study,a Temporal Attention Network(TANet)is proposed to capture both the specific categories and temporal locations of abnormal events in a weakly supervised manner.The TANet learns the anomaly score and specific category for each video segment with only video-level abnormal event labels.An event recognition module is exploited to predict the event scores for each video segment while a temporal attention module is proposed to learn a temporal attention value.Finally,to learn anomaly scores and specific categories,three constraints are considered:event category constraint,event separation constraint and temporal smoothness constraint.Experiments on the University of Central Florida Crime dataset demonstrate the effectiveness of the proposed method.

Design of the microlens arrays coupling with imagingfiber bundle

To ameliorate the disadvantages of imaging system coupled with imaging fiber bundle, a method by adding square aperture microlens arrays at both entrance and exit ends of the imaging fiber bundle is proposed to increase the system's coupling efficiency. The expressions for solving the parameters of both ends' microlens units are deducted particularly. The microlens arrays used for an infrared imaging fiber bundle with the single fiber diameter of 100 μm and core diameter of 70 μm are designed by this method. The simulation results show that compared with the system without microlens arrays, the fill factor of the imaging fiber bundle coupled microlens arrays system is increased from 44.4% to more than 90%, and the coupling efficiency is doubled too. So the design method is correct, and the introduction of microlens arrays into imaging fiber bundle coupled system is feasible and superior.

Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body.More and more people are suffering from lung diseases due to air pollution.These diseases are usually highly infectious.Such as lung tuberculosis,novel coronavirus COVID-19,etc.Lung nodule is a kind of high-density globular lesion in the lung.Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis,which is inefficient.For this reason,the use of computer-assisted diagnosis of lung nodules has become the current main trend.In the process of computer-aided diagnosis,how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research.To solve this problem,we propose a three-dimensional optimization model to achieve the extraction of suspected regions,improve the traditional deep belief network,and to modify the dispersion matrix between classes.We construct a multi-view model,fuse local three-dimensional information into two-dimensional images,and thereby to reduce the complexity of the algorithm.And alleviate the problem of unbalanced training caused by only a small number of positive samples.Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%,which is in line with clinical application standards.

Multi-Directional Reconstruction Algorithm for Panoramic Camera

A panorama can reflect the surrounding scenery because it is an image with a wide angle of view.It can be applied in virtual reality,smart homes and other fields as well.A multi-directional reconstruction algorithm for panoramic camera is proposed in this paper according to the imaging principle of dome camera,as the distortion inevitably exists in the captured panorama.First,parameters of a panoramic image are calculated.Then,a weighting operator with location information is introduced to solve the problem of rough edges by taking full advantage of pixels.Six directions of the mapping model are built,which include up,down,left,right,front and back,according to the correspondence between cylinder and spherical coordinates.Finally,multi-directional image reconstruction can be realized.Various experiments are performed in panoramas(1024×1024)with 30 different shooting scenes.Results show that the azimuth image can be reconstructed quickly and accurately.The fuzzy edge can be alleviated effectively.The rate of pixel utilization can reach 84%,and it is 33%higher than the direct mapping algorithm.Large scale distortion is also further studied.

A statistical analysis of conjugate equatorial plasma bubbles based on 630 nm airglow observations

Using the observations of the 630-nm all-sky imagers(ASIs)located in the geomagnetic conjugate points in the American sector from 2014 to 2017,this study statistically analyzed the features of conjugate equatorial plasma bubbles(EPBs),including their occurrence rate,zonal width,location and zonal drift velocity.The results show that the occurrence rate of the EPBs that occur simultaneously at geomagnetic conjugate points is~84%.The zonal widths of the EPBs are mainly~100 km,and the width differences of EPBs between the northern and southern hemispheres are mainly within±30 km.The zonal displacements of the center locations of the northern and southern EPBs are within±50 km.The zonal drift velocities of the northern and southern EPBs are nearly equal.However,it should be noted that the velocity of the EPBs in the northern hemisphere is 10%faster than that in the southern hemisphere.The results suggest that conjugate EPBs are common.Moreover,the non-conjugate EPBs in the northern and southern hemisphere can occur occasionally,which is probably associated with the different ionospheric backgrounds between the two hemispheres.The features of the conjugate EPBs as shown in this study provides support for the nowcasting of EPBs in the conjugate hemispheres.

Tetherless Optical Neuromodulation:Wavelength from Orange-red to Mid-infrared

Optogenetics,a technique that employs light for neuromodulation,has revolutionized the study of neural mechanisms and the treatment of neurological disorders due to its high spatiotemporal resolution and cell-type specificity.However,visible light,particularly blue and green light,commonly used in conventional optogenetics,has limited penetration in biological tissue.This limitation necessitates the implantation of optical fibers for light delivery,especially in deep brain regions,leading to tissue damage and experimental constraints.To overcome these challenges,the use of orange-red and infrared light with greater tissue penetration has emerged as a promising approach for tetherless optical neuromodulation.In this review,we provide an overview of the development and applications of tetherless optical neuromodulation methods with long wavelengths.We first discuss the exploration of orange-red wavelength-responsive rhodopsins and their performance in tetherless optical neuromodulation.Then,we summarize two novel tetherless neuromodulation methods using near-infrared light:upconversion nanoparticle-mediated optogenetics and photothermal neuromodulation.In addition,we discuss recent advances in mid-infrared optical neuromodulation.

Overall scheme and on-orbit images of Chang’E-2 lunar satellite CCD stereo camera

The goals of engineering and scientific missions for Chang＇E-2 lunar satellite require high detection sensitivity and large imaging dynamic range for the onboard CCD cameras. The TDI CCD image sensor was adopted for the two linear CCD stereo cameras for the first time in the lunar reconnaissance of the world. The design argumentation is described in this paper. The analysis shows that the imagers meet the mission requirements. The satellite was launched on 1 October 2010 at zero window. The cameras obtained images of 7 m resolution on the 100 km orbit for the first time on 24 October 2010, and operated once again on 27 October 2010 to take stereo images of the Sinus Iridum with the resolution better than 1.5 m. On the near-moon-arc of 15 kmxl00 km elliptical orbit, the images are very clear and rich of grey scales, indicating successful completion of the Chang＇E-2 engineering mission. At the present the cameras are acquiring the full lunar surface stereo images with 7 m resolution on the 100 km circular orbit to complete their scientific mission.

Design of a novel LED collimating element based on freeform surface

A novel element for collimating LED light is designed based on non-imaging optics. It is composed of a refraction lens and a reflector. The upper surface of the lens is freeform and calculated by geometrical optics and iterative process. The lens makes the rays in the range of 0°-45°from the optical axis collimated. The rays in the range of 45°-90°from the optical axis are collimated by the reflector. The inner surface of the reflector is parabolic with its focus located in the LED chip. The designed element is applicable to LED source of any emitting type. For a certain application, the simulation results of the designed element in Tracepro show that it has a very compact structure and good collimating performance. Just investigating the loss in the lens surfaces, this element has high light output efficiency of nearly 99%. Most lighting area radii are no more than 20 mm when the illuminated plane is 5 m away from the LED source.

Auto-compensation of velocity-height ratio for Chang’E-2 satellite CCD stereo camera

The TDI-CCD imaging method using auto-compensation of velocity-height ratio (VHR) was applied to Chang’E-2 satellite CCD stereo camera.Factors that influence the image quality of the camera were discussed,among which the mismatch error in VHR was found to be the main cause.An auto-compensation scheme for VHR was developed.The validity and effectiveness were proved by the on-orbit high quality images.

A Review of Functional Near-Infrared Spectroscopy Studies of Motor and Cognitive Function in Preterm Infants

Preterm infants are vulnerable to brain injuries,and have a greater chance of experiencing neurodevelopmental disorders throughout development. Early screening for motor and cognitive functions is critical to assessing the developmental trajectory in preterm infants, especially those who may have motor or cognitive deficits. The brain imaging technology functional near-infrared spectroscopy(fNIRS) is a portable and low-cost method of assessing cerebral hemodynamics, making it suitable for large-scale use even in remote and underdeveloped areas. In this article, we review peer-reviewed, scientific f NIRS studies of motor performance, speech perception, and facial recognition in preterm infants. f NIRS provides a link between hemodynamic activity and the development of brain functions in preterm infants. Research using fNIRS has shown different patterns of hemoglobin change during some behavioral tasks in early infancy. fNIRS helps to promote our understanding of the developmental mechanisms of brain function in preterm infants when performing motor or cognitive tasks in a less-restricted environment.

Development of space-based diffractive telescopes

Membrane diffractive optical elements formed by fabricating microstructures on the substrates have two important characteristics,ultra-light mass(surface mass density<0.1 kg/m2)and loose surface shape tolerances(surface accuracy requirements are on the order of magnitude of centimeter).Large-aperture telescopes using a membrane diffractive optical element as the primary lens have super large aperture,light weight,and low cost at launch.In this paper,the research and development on space-based diffractive telescopes are classified and summarized.First,the imaging theory and the configuration of diffractive-optics telescopes are discussed.Then,the developments in diffractive telescopes are introduced.Finally,the development prospects for this technology used as a high-resolution space reconnaissance system in the future are summarized,and the critical and relevant work that China should carry out is put forward.