A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Development status of novel spectral imaging techniques and application to traditional Chinese medicine

Traditional Chinese medicine(TCM)is a treasure of the Chinese nation,providing effective solutions to current medical requisites.Various spectral techniques are undergoing continuous development and provide new and reliable means for evaluating the efficacy and quality of TCM.Because spectral techniques are noninvasive,convenient,and sensitive,they have been widely applied to in vitro and in vivo TCM evaluation systems.In this paper,previous achievements and current progress in the research on spectral technologies(including fluorescence spectroscopy,photoacoustic imaging,infrared thermal imaging,laser-induced breakdown spectroscopy,hyperspectral imaging,and surface enhanced Raman spectroscopy)are discussed.The advantages and disadvantages of each technology are also presented.Moreover,the future applications of spectral imaging to identify the origins,components,and pesticide residues of TCM in vitro are elucidated.Subsequently,the evaluation of the efficacy of TCM in vivo is presented.Identifying future applications of spectral imaging is anticipated to promote medical research as well as scientific and technological explorations.

A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images.Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental conditions.Here,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions.Accordingly,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images.The divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize endmembers.The endmember with the minimum error is chosen as the final endmember in each specific category.The proposed method is simple and automatically considers endmember variability in hyperspectral images.The efficiency of the proposed method is evaluated using two real hyperspectral datasets.The average spectral angle and abundance angle are used to analyze the performance measures.

Research on the detection of early caries based on hyperspectral imaging

Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.

Hypergraph Regularized Deep Autoencoder for Unsupervised Unmixing Hyperspectral Images

Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data reconstruction.However,most existing AE-based unmixing algorithms usually ignore the spatial information of HSIs.To solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for unmixing.Firstly,the traditional AE architecture is specifically improved as an unsupervised unmixing framework.Secondly,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their abundances.Moreover,L_(1/2)norm is further used to enhance abundances sparsity.Finally,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.

DiriNet:An Estimation Network for Spectral Response Function and Point Spread Function

Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few works have been payed on the estimation of the two degra-dation functions.To learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly constrained.Specifically,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread function.To the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation functions.Both image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.

Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution.The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit,which will improve the timeliness of oil spill emergency monitoring.At the same time,the combination of spectral and spatial features can improve the accuracy of oil spill monitoring.Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions,for which the multiscale superpixel level group clustering framework(MSGCF)was used to select spectral feature bands with strong separability.In addition,the double-branch dual-attention(DBDA)model was applied to identify crude oil and its emulsions.Compared with the recognition results based on original hyperspectral images,using the feature bands determined by MSGCF improved the recognition accuracy,and greatly shortened the running time.Moreover,the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined,and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010.This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration,laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit.

Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction

To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.

Application of gemstone spectral imaging for efficacy evaluation in hepatocellular carcinoma after transarterial chemoembolization

AIM: To assess the value of gemstone spectral imaging (GSI) in efficacy evaluation in hepatocellular cancer (HCC) after transcatheter arterial chemoembolization (TACE) treatment.METHODS: Thirty patients with HCC underwent GSI, including nonenhanced, arterial, portalvenous and delayed phase scans, after TACE treatment. Arterial phase images were acquired with GSI for reconstruction of virtual nonenhanced images and color overlay images. Digital subtraction angiography (DSA) was performed in all these patients. Two blinded and independent readers evaluated the data in two reading sessions; standard nonenhanced, arterial, portalvenous, and delayed phase images were read in session A, and the optimal monochromatic images, iodine/water based images and spectrum features were read in session B. Sensitivity and specificity were calculated with the DSA data as the reference standard. The sensitivity and specificity were compared using the χ2 test.RESULTS: DSA revealed 154 lesions in 30 patients, and 100 of them had blood supply. Overall sensitivity and specificity were 72% (72/100) and 77.8% (42/54) for session A, and 97% (97/100) and 94.4% (51/54) for session B, respectively. The sensitivity and specificity of the two reading sessions were significantly different (χ2 = 23.04, χ2 = 7.11, P < 0.05).CONCLUSION: Compared with conventional CT, GSI could significantly improve the detection of small and multiple lesions without increasing the radiation dose. Based on spectrum features, GSI could assess tumor homogeneity and more accurately identify residual tumors and recurrent or metastatic lesions during efficacy evaluation and follow-up in HCC after TACE treatment.

Diagnosis of extent of early gastric cancer using flexible spectral imaging color enhancement

The demarcation line between the cancerous lesion and the surrounding area could be easily recognized with flexible spectral imaging color enhancement (FICE) system compared with conventional white light images. The characteristic f inding of depressed-type early gastric cancer (EGC) in most cases was revealed as reddish lesions distinct from the surrounding yellowish non-cancerous area without magnification. Conventional endoscopic images provide little information regarding depressed lesions located in the tangential line, but FICE produces higher color contrast of such cancers. Histological f indings in depressed area with reddish col- or changes show a high density of glandular structure and an apparently irregular microvessel in intervening parts between crypts, resulting in the higher color con- trast of FICE image between cancer and surrounding area. Some depressed cancers are shown as whitish lesion by conventional endoscopy. FICE also can pro- duce higher color contrast between whitish cancerous lesions and surrounding atrophic mucosa. For nearly flat cancer, FICE can produce an irregular structuralpattern of cancer distinct from that of the surrounding mucosa, leading to a clear demarcation. Most elevated-type EGCs are detected easily as yellowish lesions with clearly contrasting demarcation. In some cases, a partially reddish change is accompanied on the tumor surface similar to depressed type cancer. In addition, the FICE system is quite useful for the detection of minute gastric cancer, even without magnif ication. These new contrasting images with the FICE system may have the potential to increase the rate of detection of gastric cancers and screen for them more effectively as well as to determine the extent of EGC.

Ultraslim endoscopy with flexible spectral imaging color enhancement for upper gastrointestinal neoplasms

AIM:To conduct a preliminary study on the effect of flexible spectral imaging color enhancement (FICE) used in combination with ultraslim endoscopy by focusing on the enhanced contrast between tumor and non-tumor lesions. METHODS: We examined 50 lesions of 40 patients with epithelial tumors of the upper gastrointestinal tract before endoscopic submucosal dissection using ultraslim endoscopy with conventional natural color imag ing and with FICE imaging. We retrospectively invest igated the effect of the use of FICE on endoscopic diagn osis in comparison with normal light. RESULTS: Visibility of the epithelial tumors of the upper gastrointestinal tract with FICE was superior to normal light in 54% of the observations and comparable to normal light in 46% of the observations. There was no lesion for which visibility with FICE was inferior to that with normal light. FICE visualized 69.6% of hyperemic lesions and 58.8% of discolored lesions better than conventional endoscopy with natural color imaging. FICE sign if icantly improved the visibility of lesions with hyp ere mia or discoloration compared with normocolored lesions. CONCLUSION: This study suggests that the use of FICE would improve the ability of ultraslim endoscopy to detect epithelial tumors of the upper gastrointestinal tract.

Application of Spectral Angle Mapper Classification to Discriminate Hydrothermal Alteration in Southwest Birjand, Iran, Using Advanced Spaceborne Thermal Emission and Reflection Radiometer Image Processing

The purpose of this study is to evaluate the Spectral Angle Mapper （SAM） classification method for determining the optimum threshold （maximum spectral angle） to unveil the hydrothermal mineral assemblages related to mineral deposits. The study area indicates good potential for Cu-Au porphyry, epithermal gold deposits and hydrothermal alteration well developed in arid and semiarid climates, which makes this region significant for Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） image processing analysis. Given that achieving an acceptable mineral mapping requires knowing the alteration patterns, petrochemistry and petrogenesis of the igneous rocks while considering the effect of weathering, overprinting of supergene alteration, overprinting of hypogene alteration and host rock spectral mixing, SAM classification was implemented for argillic, sericitic, propylitic, alunitization, silicification and iron oxide zones of six previously known mineral deposits： Maherabad, a Cu-Au porphyry system; Sheikhabad, an upper part of Cu-Au porphyry system; Khoonik, an Intrusion related Au system; Barmazid, a low sulfidation epithermal system; Khopik, a Cu-Au porphyry system; and Hanish, an epithermal Au system. Thus, the investigation showed that although the whole alteration zones are affected by mixing, it is also possible to produce a favorable hydrothermal mineral map by such complementary data as petrology, petrochemistry and alteration patterns.

Simulated annealing spectral clustering algorithm for image segmentation

The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.

A Study of Estimation Model for the Chlorophyll Content of Wheat Leaf Based on Hyperspectral Imaging

In order to explore the spectral features and sensitive wave band of wheat leaf,we establish a quantitative relationship model between wheat chlorophyll content and spectral features to promote the application of hyperspectral technology in precise wheat fertilization and fast,non-destructive growth monitoring.Using the relational analysis,we analyze the relationship between chlorophyll content and spectral reflectance or the first derivative,and establish the chlorophyll content monitoring model.By selection and verification,the best estimation models for wheat chlorophyll content are as follows:SPAD = 36.75 + 188.168R387,SPAD =2094.242R'7153+ 112646.744 R'7152-1.561E7 R'715+42.991.The two models can well estimate the SPAD value of wheat leaf,and comparatively speaking,the SPAD estimation model based on wave band R387 has greater accuracy.

Incoherent digital holographic spectral imaging with high accuracy of image pixel registration

Fresnel incoherent correlation holography(FINCH) is a unique three-dimensional(3D) imaging technique which has the advantages of scanning-free,high resolution,and easy matching with existing mature optical systems.In this article,an incoherent digital holographic spectral imaging method with high accuracy of spectral reconstruction based on liquid crystal tunable filter(LCTF) and FINCH is proposed.Using the programmable characteristics of spatial light modulator(SLM),a series of phase masks,none of whose focal lengths changes with wavelength,is designed and made.For each wavelength of LCTF output,SLM calls three phase masks with different phase constants at the corresponding wavelength,and CCD records three holograms.The spectral images obtained by this method have a constant magnification,which can achieve pixel-level image registration,restrain image registration errors,and improve spectral reconstruction accuracy.The results show that this method can not only obtain the 3D spatial information and spectral information of the object simultaneously,but also have high accuracy of spectral reconstruction and excellent color reproducibility.

Feasibility Study for Applying Spectral Imaging for Wheat Grain Authenticity Testing in Pasta

Authentication of pasta is currently determined using molecular biology-based techniques focusing on DNA as the target analyte. Whilst proven to be effective, these approaches can be criticised as being destructive, time consuming, and requiring specialist instrument training. Advances in the field of multispectral imaging (MSI) and hyperspectral imaging (HSI) have facilitated the development of compact imaging platforms with the capability to rapidly differentiate a range of materials (inclusive of grains and seeds) based on surface colour, texture and chemical composition. This preliminary investigation evaluated the applicability of spectral imaging for identification and quantitation of durum wheat grain samples in relation to pasta authenticity. MSI and HSI were capable of rapidly distinguishing between durum wheat and adulterant common wheat cultivars and assigning percentage adulteration levels characterised by low biases and good repeatability estimates. The results demonstrated the potential for spectral imaging based seed/grain adulteration testing to augment existing standard molecular approaches for food authenticity testing.

Optimized energy thresholds in a spectral computed tomography scan for contrast agent imaging

Spectral computed tomography(CT) based on photon counting detectors(PCDs) is a well-researched topic in the field of X-ray imaging. When PCD is applied in a spectral CT system, the PCD energy thresholds must be carefully selected, especially for K-edge imaging, which is an important spectral CT application. This paper presents a threshold selection method that yields better-quality images in K-edge imaging. The main idea is to optimize the energy thresholds ray-by-ray according to the targeted component coefficients, followed by obtaining an overall optimal energy threshold by frequency voting. A low-dose pre-scan is used in practical implementations to estimate the line integrals of the component coefficients for the basis functions. The variance of the decomposed component coefficients is then minimized using the Cramer–Rao lower bound method with respect to the energy thresholds. The optimal energy thresholds are then used to take a full scan and gain better image reconstruction with less noise than would be given by a full scan using the non-optimal energy thresholds. Simulations and practical experiments on imaging iodine and gadolinium solutions, which are commonly used as contrast agents in medical applications, were used to validate the method. The noise was significantly reduced with the same dose relative to the non-optimal energy thresholds in both simulations and in practical experiments.

Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

Tea plant stresses threaten the quality of tea seriously.The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation.In recent years,hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases,pests and some other stresses at the leaf level.However,the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale.In this study,based on the canopy-level hyperspectral imaging data,the methods for identifying and differentiating the three commonly occurred tea stresses(i.e.,the tea leafhopper,anthrax and sun burn)were studied.To account for the complexity of the canopy scenario,a stepwise detecting strategy was proposed that includes the process of background removal,identification of damaged areas and discrimination of stresses.Firstly,combining the successive projection algorithm(SPA)spectral analysis and K-means cluster analysis,the background and overexposed non-plant regions were removed from the image.Then,a rigorous sensitivity analysis and optimization were performed on various forms of spectral features,which yielded optimal features for detecting damaged areas(i.e.,YSV,Area,GI,CARI and NBNDVI)and optimal features for stresses discrimination(i.e.,MCARI,CI,LCI,RARS,TCI and VOG).Based on this information,the models for identifying damaged areas and those models for discriminating different stresses were established using K-nearest neighbor(KNN),Random Forest(RF)and Fisher discriminant analysis.The identification model achieved an accuracy over 95%,and the discrimination model achieved an accuracy over 93%for all stresses.The results suggested the feasibility of stress detection and differentiation using canopy-level hyperspectral imaging techniques,and indicated the potential for its extension over large areas.

PHISHING WEB IMAGE SEGMENTATION BASED ON IMPROVING SPECTRAL CLUSTERING

This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.

Detection of illegally added drugs in dietary supplements by near-infrared spectral imaging

The application to detect ilally added drugs in dietary supplerments by near-infrared spectral imaging was studied with the focus on nifedipine,diclofenac and metformin.The method is based on near-infrared spectral images correlation cofficient to detect ilally added drugs.The results comply 100%with HPLC methods test results with no false positive results.