Extraction of Desertification Information in Hulun Buir Based on MODIS Image Data

[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.

Satellite Data Reduction Using Entropy-preserved Image Compression Technique

In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.

Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme

Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is increasing.Hiding secure data in these transmitted data has emerged broad security issue,such as authentication and copyright protection.On the other hand,considering the transmission efficiency issue,image transmission usually involves image compression in Internet-based applications.To address both issues,this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding(AMBTC).First,an image is divided into nonoverlapping blocks through AMBTC compression,the blocks are classified four types,namely smooth,semi-smooth,semi-complex,and complex.The secret data are embedded into the smooth blocks by using a simple replacement strategy.The proposed method respectively embeds nine bits(and five bits)of secret data into the bitmap of the semi-smooth blocks(and semicomplex blocks)through the exclusive-or(XOR)operation.The secret data are embedded into the complex blocks by using a hidden function.After the embedding phase,the direct binary search(DBS)method is performed to improve the image qualitywithout damaging the secret data.The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.

Some Peculiarities of the Preprocessing of Spectral Data and Images

Remotely sensed spectral data and images are acquired under significant additional effects accompanying their major formation process, which greatly determine measurement accuracy. In order to be used in subsequent quantitative analysis and assessment, this data should be subject to preliminary processing aiming to improve its accuracy and credibility. The paper considers some major problems related with preliminary processing of remotely sensed spectral data and images. The major factors are analyzed, which affect the occurrence of data noise or uncertainties and the methods for reduction or removal thereof. Assessment is made of the extent to which available equipment and technologies may help reduce measurement errors.

Review of large scale crop remote sensing monitoring based on MODIS data

China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.

Reversible data hiding in encrypted images based on additive secret sharing and additive joint coding using an intelligent predictor

Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block and a spatial attention module,showing superior pixel prediction performance compared to existing predictors.Additionally,we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space,outperforming single coding approaches.The image owner employs the presented intelligent predictor to forecast the original image,followed by encryption through additive secret sharing before conveying the encrypted image to data hiders.Subsequently,data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver.The receiver can extract secret data and recover the original image losslessly,with the processes of data extraction and image recovery being separable.Our innovative approach combines an intelligent predictor with additive secret sharing,achieving reversible data embedding and extraction while ensuring security and lossless recovery.Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity.For the Lena image,the number of prediction errors within the range of[-5,5]is as high as 242500 and our predictor achieves an embedding capacity of 4.39 bpp.

Analysis of the Design and Implementation of a GIS System Incorporating Intelligent Recognition Models

The rapid economic growth,urbanization,and industrialization have led to a scarcity of land resources in coastal areas,exacerbating the conflict between humans and the environment.In order to promote economic development,attention has turned to the sea,and various coastal engineering projects have been undertaken,sparking a wave of land reclamation.However,while these efforts bring economic and social benefits,they also have implications for ecological relationships.To respond to and plan for changes in the coastline and land cover in a timely manner,this paper proposes and constructs a GIS system that integrates remote sensing image recognition models.The system combines geographic information system development technology with image recognition technology,streamlining the processing and identification of image data.This approach is particularly advantageous for marine management departments in their long-term monitoring and dynamic management of coastal lines,ensuring a more effective and efficient response.

Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.

Contrast-enhanced ultrasound Liver Imaging Reporting and Data System:Lights and shadows in hepatocellular carcinoma and cholangiocellular carcinoma diagnosis

BACKGROUND Contrast-enhanced ultrasound(CEUS)is considered a secondary examination compared to computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of hepatocellular carcinoma(HCC),due to the risk of misdiagnosing intrahepatic cholangiocarcinoma(ICC).The introduction of CEUS Liver Imaging Reporting and Data System(CEUS LI-RADS)might overcome this limitation.Even though data from the literature seems promising,its reliability in real-life context has not been well-established yet.AIM To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis.METHODS CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis.The diagnostic standard for all nodules was either biopsy(102 nodules)or CT/MRI(409 nodules).Common diagnostic accuracy indexes such as sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)were assessed for the following associations:CEUS LR-5 and HCC;CEUS LR-4 and 5 merged class and HCC;CEUS LR-M and ICC;and CEUS LR-3 and malignancy.The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined.Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated.RESULTS CEUS LR-5 predicted HCC with a 67.6%sensitivity,97.7%specificity,and 99.3%PPV(P<0.001).The merging of LR-4 and 5 offered an improved 93.9%sensitivity in HCC diagnosis with a 94.3%specificity and 98.8%PPV(P<0.001).CEUS LR-M predicted ICC with a 91.3%sensitivity,96.7%specificity,and 99.6%NPV(P<0.001).CEUS LR-3 predominantly included benign lesions(only 28.8%of malignancies).In this class,the hypo-hypo pattern showed a much higher rate of malignant lesions(73.3%)than the iso-iso pattern(2.6%).Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect(n=511,k=0.94,P<0.001),while the agreement among raters from separate centres was substantial(n=50,k=0.67,P<0.001).Agreement was stronger for arterial phase hyperenhancement(internal k=0.86,P<2.7×10-214;external k=0.8,P<0.001)than washout(internal k=0.79,P<1.6×10-202;external k=0.71,P<0.001).CONCLUSION CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose HCC and by splitting LR-3 into two subgroups to differentiate iso-iso nodules from other patterns.

AN IMPROVED ALGORITHM FOR SUPERVISED FUZZY C-MEANS CLUSTERING OF REMOTELY SENSED DATA

This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de- creased as comparing with that by a conventional algorithm: that is, the classification accura- cy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzy c-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.

Assimilation of GPM Microwave Imager Radiance for Track Prediction of Typhoon Cases with the WRF Hybrid En3DVAR System

The impact of assimilating radiance data from the advanced satellite sensor GMI(GPM microwave imager)for typhoon analyses and forecasts was investigated using both a three-dimensional variational(3DVAR)and a hybrid ensemble-3DVAR method.The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting(WRF)model.The GMI radiance data are assimilated for Typhoon Matmo(2014),Typhoon Chan-hom(2015),Typhoon Meranti(2016),and Typhoon Mangkhut(2018)in the Pacific before their landing.The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method,the wind,temperature,and humidity fields are effectively adjusted,leading to improved forecast skills of the typhoon track with GMI radiance assimilation.The hybrid DA method is able to further adjust the location of the typhoon systematically.The improvement of the track forecast is even more obvious for later forecast periods.In addition,water vapor and hydrometeors are enhanced to some extent,especially with the hybrid method.

Interobserver agreement for contrast-enhanced ultrasound of liver imaging reporting and data system:A systematic review and metaanalysis

BACKGROUND Hepatocellular carcinoma is the most common primary liver malignancy.From the results of previous studies,Liver Imaging Reporting and Data System(LIRADS)on contrast-enhanced ultrasound(CEUS)has shown satisfactory diagnostic value.However,a unified conclusion on the interobserver stability of this innovative ultrasound imaging has not been determined.The present metaanalysis examined the interobserver agreement of CEUS LI-RADS to provide some reference for subsequent related research.AIM To evaluate the interobserver agreement of LI-RADS on CEUS and analyze the sources of heterogeneity between studies.METHODS Relevant papers on the subject of interobserver agreement on CEUS LI-RADS published before March 1,2020 in China and other countries were analyzed.The studies were filtered,and the diagnostic criteria were evaluated.The selected references were analyzed using the“meta”and“metafor”packages of R software version 3.6.2.RESULTS Eight studies were ultimately included in the present analysis.Meta-analysis results revealed that the summary Kappa value of included studies was 0.76[95%confidence interval,0.67-0.83],which shows substantial agreement.Higgins I2 statistics also confirmed the substantial heterogeneity(I2=91.30%,95%confidence interval,85.3%-94.9%,P<0.01).Meta-regression identified the variables,including the method of patient enrollment,method of consistency testing,and patient race,which explained the substantial study heterogeneity.CONCLUSION CEUS LI-RADS demonstrated overall substantial interobserver agreement,but heterogeneous results between studies were also obvious.Further clinical investigations should consider a modified recommendation about the experimental design.

Hepatocellular carcinoma Liver Imaging Reporting and Data Systems treatment response assessment: Lessons learned and future directions

Hepatocellular carcinoma(HCC)is a leading cause of morbidity and mortality worldwide,with rising clinical and economic burden as incidence increases.There are a multitude of evolving treatment options,including locoregional therapies which can be used alone,in combination with each other,or in combination with systemic therapy.These treatment options have shown to be effective in achieving remission,controlling tumor progression,improving disease free and overall survival in patients who cannot undergo resection and providing a bridge to transplant by debulking tumor burden to downstage patients.Following locoregional therapy(LRT),it is crucial to provide treatment response assessment to guide management and liver transplant candidacy.Therefore,Liver Imaging Reporting and Data Systems(LI-RADS)Treatment Response Algorithm(TRA)was created to provide a standardized assessment of HCC following LRT.LIRADS TRA provides a step by step approach to evaluate each lesion independently for accurate tumor assessment.In this review,we provide an overview of different locoregional therapies for HCC,describe the expected post treatment imaging appearance following treatment,and review the LI-RADS TRA with guidance for its application in clinical practice.Unique to other publications,we will also review emerging literature supporting the use of LI-RADS for assessment of HCC treatment response after LRT.

Validation of Vesical Imaging Reporting and Data System score for the diagnosis of muscle-invasive bladder cancer: A prospective cross-sectional study

Objective:Vesical Imaging Reporting and Data System(VIRADS)score was developed to standardize the reporting and staging of bladder tumors on pre-operative multiparametric magnetic resonance imaging.It helps in avoiding unnecessary repeat transurethral resection of bladder tumor in high-risk non-muscle-invasive bladder cancer patients.This study was done to determine the validity of VIRADS score prospectively for the diagnosis of muscleinvasive bladder cancer.Methods:This study was conducted from March 2019 to March 2020 at Sawai Man Singh Medical College and Hospital,Jaipur,Rajasthan,India.Patients admitted with the provisional diagnosis of bladder tumor were included as participants.All these patients underwent a 3 Tesla mpMRI to obtain a VIRADS score before they underwent transurethral resection of bladder tumor and these data were analyzed to evaluate the correlation of pre-operative VIRADS score with mus-cle invasiveness of the tumor in final biopsy report.Results:A cut-off of VIRADS≥4 for prediction of detrusor muscle invasion yielded a sensitivity of 79.4%,specificity of 94.2%,positive predictive value of 90.0%,negative predictive value of 87.5%,and diagnostic accuracy of 86.4%.A cut off of VIRADS≥3 for prediction of detrusor muscle invasion yielded a sensitivity of 91.2%,specificity of 78.8%,positive predictive value of 73.8%,negative predictive value of 93.2%,and accuracy of 83.7%.The receiver operating curve showed the area under the curve to be 0.922(95%confidence interval:0.862e0.983).Conclusion:VIRADS score appears to be an excellent and effective pre-operative radiological tool for the prediction of detrusor muscle invasion in bladder cancer.

Analysis of Spectral Characteristics Based on Optical Remote Sensing and SAR Image Fusion

Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model （IBSM）, the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.

Liver Imaging Reporting and Data System criteria for the diagnosis of hepatocellular carcinoma in clinical practice: A pictorial minireview

Hepatocellular carcinoma(HCC)is the sixth most common cancer.The main risk factors associated with HCC development include hepatitis B virus,hepatitis C virus,alcohol consumption,aflatoxin B1,and nonalcoholic fatty liver disease.However,hepatocarcinogenesis is a complex multistep process.Various factors lead to hepatocyte malignant transformation and HCC development.Diagnosis and surveillance of HCC can be made with the use of liver ultrasound(US)every 6 mo.However,the sensitivity of this imaging method to detect HCC in a cirrhotic liver is limited,due to the abnormal liver parenchyma.Computed tomography(CT)and magnetic resonance imaging(MRI)are considered to be most useful tools for at-risk patients or patients with inadequate US.Liver biopsy is still used for diagnosis and prognosis of HCC in specific nodules that cannot be definitely characterized as HCC by imaging.Recently the American College of Radiology designed the Liver Imaging Reporting and Data System(LI-RADS),which is a comprehensive system for standardized interpretation of CT and MRI liver examinations that was first proposed in 2011.In 2018,it was integrated into the American Association for the Study of Liver Diseases guidance statement for HCC.LI-RADS is designed to ensure high sensitivity,precise categorization,and high positive predictive value for the diagnosis of HCC and is applied to“highrisk populations”according to specific criteria.Most importantly LI-RADS criteria achieved international collaboration and consensus among liver experts around the world on the best practices for caring for patients with or at risk for HCC.

Reconstructing the landing trajectory of the CE-3 lunar probe by using images from the landing camera

An accurate determination of the landing trajectory of Chang＇e-3 （CE-3） is significant for verifying orbital control strategy, optimizing orbital planning, accu- rately determining the landing site of CE-3 and analyzing the geological background of the landing site. Due to complexities involved in the landing process, there are some differences between the planned trajectory and the actual trajectory of CE-3. The land- ing camera on CE-3 recorded a sequence of the landing process with a frequency of 10 frames per second. These images recorded by the landing camera and high-resolution images of the lunar surface are utilized to calculate the position of the probe, so as to reconstruct its precise trajectory. This paper proposes using the method of trajectory reconstruction by Single Image Space Resection to make a detailed study of the hov- ering stage at a height of 100 m above the lunar surface. Analysis of the data shows that the closer CE-3 came to the lunar surface, the higher the spatial resolution of im- ages that were acquired became, and the more accurately the horizontal and vertical position of CE-3 could be determined. The horizontal and vertical accuracies were 7.09 m and 4.27 m respectively during the hovering stage at a height of 100.02 m. The reconstructed trajectory can reflect the change in CE-3＇s position during the powered descent process. A slight movement in CE-3 during the hovering stage is also clearly demonstrated. These results will provide a basis for analysis of orbit control strategy, and it will be conducive to adjustment and optimization of orbit control strategy in follow-up missions.

Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China

Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.

A new segmentation algorithm for lunar surface terrain based on CCD images

Terrain classification is one of the critical steps used in lunar geomorphologic analysis and landing site selection. Most of the published works have focused on a Digital Elevation Model （DEM） to distinguish different regions of lunar terrain. This paper presents an algorithm that can be applied to lunar CCD images by blocking and clustering according to image features, which can accurately distinguish between lunar highland and lunar mare. The new algorithm, compared with the traditional algo- rithm, can improve classification accuracy. The new algorithm incorporates two new features and one Tamura texture feature. The new features are generating an enhanced image histogram and modeling the properties of light reflection, which can represent the geological characteristics based on CCD gray level images. These features are ap- plied to identify texture in order to perform image clustering and segmentation by a weighted Euclidean distance to distinguish between lunar mare and lunar highlands. The new algorithm has been tested on Chang＇e-1 CCD data and the testing result has been compared with geological data published by the U.S. Geological Survey. The result has shown that the algorithm can effectively distinguish the lunar mare from highlands in CCD images. The overall accuracy of the proposed algorithm is satisfactory, and the Kappa coefficient is 0.802, which is higher than the result of combining the DEM with CCD images.

A new lunar absolute control point: established by images from the landing camera on Chang'e-3

The establishment of a lunar control network is one of the core tasks in selenodesy, in which defining an absolute control point on the Moon is the most im- portant step. However, up to now, the number of absolute control points has been very sparse. These absolute control points have mainly been lunar laser ranging retrore- flectors, whose geographical location can be observed by observations on Earth and also identified in high resolution lunar satellite images. The Chang＇e-3 （CE-3） probe successfully landed on the Moon, and its geographical location has been monitored by an observing station on Earth. Since its positional accuracy is expected to reach the meter level, the CE-3 landing site can become a new high precision absolute control point. We use a sequence of images taken from the landing camera, as well as satellite images taken by CE-1 and CE-2, to identify the location of the CE-3 lander. With its geographical location known, the CE-3 landing site can be established as a new abso- lute control point, which will effectively expand the current area of the lunar absolute control network by 22%, and can greatly facilitate future research in the field of lunar surveying and mapping, as well as selenodesy.