The construction of normalized enhanced water index and the extraction of supra-glacial water based on WorldView-2 imagery

As an important part of the mass balance of the Ice Sheet,Supra-glacial Water not only reflects the diversity of polar environmental changes,but also plays an important role in the study of global climate and environmental changes.In this paper,we chose northern Greenland as the research area,and constructed a Normalized Enhanced Water Index(NEWI)based on the high-precision WorldView-2 images of different phases during the ablation period in northern Greenland,followed by a statistical analysis on the spectral characteristics of the images were for the typical features in the study area.Then the fuzzy areas with similar gray values of thin sea ice and shallow ice water bodies were located,according to the distribution rules of ground objects and histogram graphic features of the images,so as to enhance the contrast of ground objects between the regions,and finally the extraction of the fine range of water bodies on the ice surface.Experimental results showed that the proposed index effectively highlighted the ice water with the water of the reflectivity difference,compared with the commonly used water index NDWI,etc.,especially in shallow water,which contributes to differentiation from other objects.The precision evaluation showed that the applied method of extraction has higher degree of refinement compared with other methods,by which the ice water can get complete ice water effectively.

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

A color image encryption scheme based on a 2D coupled chaotic system and diagonal scrambling algorithm

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.

Image Dehazing with Hybrid λ2-λ0 Penalty Mode

Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model for image dehazing. This model performs a weighted fusion of two distinct transmission maps, generated by imposing λ2 and λ0 norm penalties on the approximate regression coefficients of the transmission map. This approach effectively balances the sparsity and smoothness associated with the λ0 and λ2 norms, thereby optimizing the transmittance map. Specifically, when the λ2 norm is penalized in the model, an updated guided image is obtained after implementing λ0 penalty. The resulting optimization problem is effectively solved using the least square method and the alternating direction algorithm. The dehazing framework combines the advantages of λ2 and λ0 norms, enhancing sparse and smoothness, resulting in higher quality images with clearer details and preserved edges.

A novel water index for urban high-resolution eight-band WorldView-2 imagery

Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images still a challenging problem.This paper develops a novel water index named as NDWI-MSI,combining a new normalized difference water index(NDWI)and a recently developed morphological shadow index(MSI),to delineate water bodies from eight-band WorldView-2 imagery.The newly available bands(e.g.coastal,yellow,red-edge,and near-infrared 2)of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations.Through our testing,a new NDWI is defined in this study.In addition,MSI,a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas.The NDWI-MSI is created by combining NDWI and MSI,which is able to highlight water bodies and simultaneously suppress shadow areas.In experiments,it is shown that the new water index can achieve better performance than traditional NDWI,and even supervised classifiers,for example,maximum likelihood classifier,and support vector machine.

Development of a Generic Model for the Detection of Roof Materials Based on an Object-Based Approach Using WorldView-2 Satellite Imagery

The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.

MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用

目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。

基于Image 2和岭回归模型估测肉牛体尺、体重

为了建立快速、经济、准确、可行的预测肉牛体尺、体重性状指标的方法,试验利用Image 2图像识别技术和岭回归模型预测肉牛的体尺、体重,并与实测值进行比较,经过交叉验证该模型估测结果的准确性。结果表明:试验肉牛预测体尺与真实体尺的相关性>40%,其预测平均偏差小于0.04m。利用岭回归模型预测体重,通过3倍的交叉验证获得93%以上的准确率。说明可以利用图像识别技术与岭回归模型直接预测肉牛体尺、体重。

基于WorldView-2高分影像信息增强及提取在卡而却卡地区遥感调查中的应用

为进一步探讨WorldView-2高分遥感数据在遥感调查中的应用效果,笔者选择青海卡而却卡地区进行图像信息增强及信息提取方法研究。在分析研究区内不同岩性的光谱曲线特征的基础上,针对遥感解译中目视解译效果不好、图像模糊和对比度不够等问题,提出一系列图像增强处理的方法,这些方法显著增强了岩性、构造的识别效果,可以更好地辅助高分遥感解译工作。同时,通过基于纹理和光谱信息的影像分类方法对大理岩、岩浆岩信息提取研究,可以较准确地圈定大理岩、岩浆岩岩性界线,并通过与现有地质矿产资料进行对比,该方法形成的岩性-构造解译图反映的岩性、构造等信息更加详细、丰富。

IMAGE 2型糖尿病预防指南要点与点评

新近,欧洲糖尿病预防指南与培训标准工作组(Development and Implementation of a European Guideline and Training Standards for Diabetes Prevention,IMAGE)颁布了2型糖尿病(T2DM)预防指南,其要点摘译如下:

T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level

Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,ALB has caused a large number of deaths of a local tree species Populus gansuensis.The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate.Therefore,the monitoring of the ALB infestation at the individual tree level in the landscape is necessary.Moreover,the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.Methods:Multispectral WorldView-2(WV-2)images and 5 tree physiological factors were collected as experimental materials.One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees.The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model.Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy.Finally,three machine learning algorithms,i.e.,Random Forest(RF),Support Vector Machine(SVM),Classification And Regression Tree(CART),were applied and compared to find the best classifier for predicting the damage stage of individual P.gansuensis.Results:The confusion matrix of RF achieved the highest overall classification accuracy(86.2%)and the highest Kappa index value(0.804),indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees.In addition,the canopy color was found to be positively correlated with P.gansuensis’damage stages.Conclusions:A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P.gansuensis infested with ALB.The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree.These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province,China.

Infrared image segmentation method based on 2D histogram shape modification and optimal objective function

In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.

Defect detection method based on 2D entropy image segmentation

In order to improve the work efficiency of non-destructive testing(NDT)and the reliability of NDT results,an automatic method to detect defects in the ultrasonic image was researched.According to the characterization of ultrasonic D-scan image,clutter wave suppression and de-noising were presented firstly.Then,the image is processed by binaryzation using KSW 2 D entropy based on image segmentation method.The results showed that,the global threshold based segmentation method was somewhat ineffective for D-scan image because of under-segmentation.Especially,when the image is big in size,small targets which are composed by a small amount of pixels are often undetected.Whereas,local threshold based image segmentation method is effective in recognizing small defects because it takes local image character into account.

Sentiment Analysis Using E-Commerce Review Keyword-Generated Image with a Hybrid Machine Learning-Based Model

In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated images.For method validation,the data randomly comprised of 5000 reviews from Amazon have been analyzed.With superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works.Thus,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research.

2-D mini mumfuzzy entropy method of image thresholding based on genetic algorithm

A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

A Novel 2D Hyperchaotic with a Complex Dynamic Behavior for Color Image Encryption

The generation method of the key stream and the structure of the algorithm determine the security of the cryptosystem.The classical chaotic map has simple dynamic behavior and few control parameters,so it is not suitable for modern cryptography.In this paper,we design a new 2D hyperchaotic system called 2D simple structure and complex dynamic behavior map(2D-SSCDB).The 2D-SSCDB has a simple structure but has complex dynamic behavior.The Lyapunov exponent verifies that the 2D-SSCDB has hyperchaotic behavior,and the parameter space in the hyperchaotic state is extensive and continuous.Trajectory analysis and some randomness tests verify that the 2D-SSCDB can generate random sequences with good performance.Next,to verify the excellent performance of the 2D-SSCDB,we use the 2D-SSCDB to generate a keystream for color image encryption.In the encryption algorithm,the encryption algorithm scrambles and diffuses simultaneously,increasing the cryptographic system’s security.The horizontal correlation,vertical correlation,and diagonal correlation of ciphertext are−0.0004,−0.0004 and 0.0007,respectively.The average information entropy of the ciphertext is 7.9993.In addition,the designed encryption algorithm reduces the correlation between the three channels of the color image.Security analysis shows that the color image encryption algorithm designed using 2DSSCDB has good security,can resist standard attack methods,and has high efficiency.

Algorithm Development of Cloud Removal from Solar Images Based on Pix2Pix Network

Sky clouds affect solar observations significantly.Their shadows obscure the details of solar features in observed images.Cloud-covered solar images are difficult to be used for further research without pre-processing.In this paper,the solar image cloud removing problem is converted to an image-to-image translation problem,with a used algorithm of the Pixel to Pixel Network(Pix2Pix),which generates a cloudless solar image without relying on the physical scattering model.Pix2Pix is consists of a generator and a discriminator.The generator is a well-designed U-Net.The discriminator uses PatchGAN structure to improve the details of the generated solar image,which guides the generator to create a pseudo realistic solar image.The image generation model and the training process are optimized,and the generator is jointly trained with the discriminator.So the generation model which can stably generate cloudless solar image is obtained.Extensive experiment results on Huairou Solar Observing Station,National Astronomical Observatories,and Chinese Academy of Sciences(HSOS,NAOC and CAS)datasets show that Pix2Pix is superior to the traditional methods based on physical prior knowledge in peak signal-to-noise ratio,structural similarity,perceptual index,and subjective visual effect.The result of the PSNR,SSIM and PI are 27.2121 dB,0.8601 and 3.3341.