3D Reconstruction with Coronary Artery Based on Curve Descriptor and Projection Geometry-Constrained Vasculature Matching

This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg–Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter adjusting and the trust region method, the error between 3D reconstructed vessel projection and the actual vessel can be minimized. Then, CBOCD (curvature and brightness order curve descriptor) is proposed to indicate the degree of the self-occlusion of blood vessels during angiography. Next, the error matrix constructed from the error of epipolar matching is used in point pairs matching of the vascular through dynamic programming. Finally, the recorded radius of vessels helps to construct ellipse cross-sections and samples on it to get a point set around the centerline and the point set is converted to mesh for reconstructing the surface of vessels. The validity and applicability of the proposed methods have been verified through experiments that result in the significant improvement of 3D reconstruction accuracy in terms of average back-projection errors. Simultaneously, due to precise point-pair matching, the smoothness of the reconstructed 3D coronary artery is guaranteed.

Interference Suppression in Heterogeneous Massive MIMO Systems with Imperfect CSI

Interference management is of paramount importance in heterogeneous massive mimo networks (HetNet). In this paper, an algorithm has been suggested to suppress the interference in large-MIMO HetNets with imperfect channel state information(CSI). The proposed technique controls both the intra-tier and cross-tier interference of the macrocell as well as the small cells. The intra-tier interference of the macrocell as well as the cross-tier interference have been minimized under maximum transmission power and minimum signal to interference and noise ratio (SINR) constraint. The channel estimation error matrix has also been modeled using the joint sparsity property. The precoding algorithm is thus achieved through the application of semi-definite relaxation and block coordinate descent techniques. The intra-tier interference of the small cells are addressed with the aid of the zero forcing scheme. The proposed method has been validated through various simulations which confirm the superiority of the algorithm over its counterparts.

Bivariate Based Susceptibility Mapping for Gully Erosion in Wanjoga River Catchment Upper Tana Basin, Kenya

Gullies occur in semi-arid regions characterized by rainfall variability and seasonality, increased overland flow, affecting ecological fragility of an area. In most gully prone areas, extent of land affected by gullies is increasing. Thus, predicting susceptibility to gully erosion in semi-arid environment is an important step towards effectively rehabilitating and prevention against gully erosion. Proneness to gully occurrence was assessed against; Land cover/land use, slope, soil characteristics, rainfall variability and elevation, and modelled using geographical information system (GIS)-based bivariate statistical approach. Objectives of the study were; a) to assess influence of geomorphological factors on gully erosion, b) analyze and develop gully erosion susceptibility map, c) verify gully susceptibility images using error matrix of class labels in classified map against ground truth reference data. Total of 66 gullied areas (width and depth ≥ ranging 0.5), were mapped using 15m resolution Landsat images for 2018 and field surveys to estimate susceptibility to gully erosion by Global Mapper software in GIS. The images were verified using 120 pixels of known 15 gully presence or absence to produce an error matrix based on comparison of actual outcomes to predicted outcomes. Influence of conditioning factors to gully erosion showed a significant positive relationship between gully susceptibility and gully conditioning factors with consistency value; CR =0.097; value< 0.1, indicating, individual conditioning factors had an importance in influencing gully erosion. Slope (43%) and soil lithotype (25%), most influenced gully susceptibility, while land cover/land use (12%) and rainfall (12%) had least impact. Verification results showed satisfactory agreement between susceptibility map and field data on gullied areas at approximately 76.2%, an error of positive value of 4% and a negative value of 7%. Thus, production of susceptibility map by bivariate statistical method represents a useful tool for ending long and short-term gully emergencies by planning conservation of semi-arid regions.

Application of Spectral Index-Based Logistic Regression to Detect Inland Water in the South Caucasus

The Caucasus is a diverse region with many climate zones that range from subtropical lowlands to mountainous alpine areas. The region is marked by irrigated croplands fed by irrigation canals, heavily vegetated wetlands, lakes, and reservoirs. In this study, we demonstrate the development of an improved surface water map based on a global water dataset to get a better understanding of the spatial distribution of small water bodies. First, we used the global water product from the European Commission Joint Research Center (JRC) to generate training data points by stratified random sampling. Next, we applied the optimal probability cut-off logistic regression model to develop surface water datasets for the entire Caucasus region, covering 19 Landsat tiles from May to October 2019. Finally, we used 6745 manually classified points (3261 non-water, 3484 water) to validate both the newly developed water dataset and the JRC global surface water dataset using an estimated proportion of area error matrix to evaluate accuracy. Our approach produced surface water extent maps with higher accuracy (89.2%) and detected 392 km2 more water than the global product (86.7% accuracy). We demonstrate that the newly developed method enables surface water detection of small ponds and lakes, flooded agricultural fields, and narrow irrigation channels, which are particularly important for mosquito-borne diseases.

Unoccupied aerial vehicle-assisted monitoring of benthic vegetation in the coastal zone enhances the quality of ecological data

Traditional monitoring (e.g., in-water based surveys) of eelgrass meadows and perennial macroalgae in coastal areas is time and labor intensive, requires extensive equipment, and the collected data has a low temporal resolution. Further, divers and Remotely Operated Vehicles (ROVs) have a low spatial extent that cover small fractions of full systems. The inherent heterogeneity of eelgrass meadows and macroalgae assemblages in these coastal systems makes interpolation and extrapolation of observations complicated and, as such, methods to collect data on larger spatial scales whilst retaining high spatial resolution is required to guide management. Recently, the utilization of Unoccupied Aerial Vehicles (UAVs) has gained popularity in ecological sciences due to their ability to rapidly collect large amounts of area-based and georeferenced data, making it possible to monitor the spatial extent and status of SAV communities with limited equipment requirements compared to ROVs or diver surveys. This paper is focused on the increased value provided by UAV-based, data collection (visual/Red Green Blue imagery) and Object Based Image Analysis for gaining an improved understanding of eelgrass recovery. It is demonstrated that delineation and classification of two species of SAV ( Fucus vesiculosus and Zostera marina) is possible; with an error matrix indicating 86–92% accuracy. Classified maps also highlighted the increasing biomass and areal coverage of F. vesiculosus as a potential stressor to eelgrass meadows. Further, authors derive a statistically significant conversion of percentage cover to biomass ( R2 = 0.96 for Fucus vesiculosus, R2 = 0.89 for Zostera marina total biomass, and R2 = 0.94 for AGB alone, p < 0.001). Results here provide an example of mapping cover and biomass of SAV and provide a tool to undertake spatio-temporal analyses to enhance the understanding of eelgrass ecosystem dynamics.

Time Series Analysis Using Digital Classification of Data on Land Use and Land Cover in Dohuk Governorate

This study deals with the analysis and detection of changes in land cover patterns and land uses, especially forests in Amadiya district in Dohuk Governorate. It carred out in northern of Iraq by area is (2775.21) km2 and the district is located astronomically between longitudes (01/04 ° 43), (17/08 ° 44), it extends between two circles of latitude, which are (16/50 ° 36) and ('30.'21 ° 37) north, during the periods (1999-2006-2013-2019). Application of the Supervised Classification and the detection of change over time in a comparative manner and by relying on the satellite images of the Land sat ETM satellite were used. The Landsat OLI satellite with a distinctive capacity of 30 meters in the Arc map 10.6.1 program, and one of the indicators of environmental degradation in the land cover patterns, which is the NDVI index for all study periods, was used to reveal the role of natural and human factors that lead to changes in the land cover patterns in the study area. The classification revealed the existence of five types of common land cover, which included dense forests, open forests, urban areas, bare soil and water, which showed clear changes in these land coverings during the period from 1999 to 2019, which were represented by a decrease in forests, bare soil and water by a percentage of (54.76601%), (5.212329%), (2.149469%) respectively, while the Dense and urban areas by (16.35919%) and (21.51301%) in 2019, respectively. The classification accuracy of the Spatial indication was estimated based on the error matrix from there we found that the accuracy was (93.29%) this indicates that the classification accuracy is very good It is acceptable and can relied upon and recommended for classification.

Land Cover Classification Using Remote Sensing in Amadiyah Province

This study was conducted on the vegetative and non-vegetative land cover spread in the Amadiya District of Dohuk Governorate, northern Iraq, located between longitudes (43 ° 25'24.309 "- 43 ° 11'6.839") to the east and latitudes (37 ° 12'36.359 "- 37 7'25.484") north. They rely on a spatial indication of accuracy (10 m) and are reduced to (5 m) from Sentinel -2. Using unsupervised classifications, to form a general perception of the items in the studied area. As the number of varieties and the number of spectral bands used were determined, then the Supervised Classification to classify the spatial indication at the site to determine the plant and non-plant ground targets. These two classifications resulted, using the (Arc GIS) program, we obtained 12 types when classifying the space declaration for the Amadiyah district. We noticed that the area occupied by the terrestrial targets of the site are (water, medium-density forests (sloping lands), medium-density forests (flatlands), low-density forests (sloping lands), low-density forests (flatlands), limestone rocky areas, dense forests. (Sloping lands), limestone and paved roads, barren lands, residential areas, pastures, dense forests (flatlands) and their areas respectively are (283.9 - 408.6 - 556.2 - 829.2 - 983.6 - 1022.8 - 1066.4 - 1138.8 - 1148.5 - 1172.2 - 1218.4. - 1272.4) km2. The classification accuracy of the spatial indication was estimated based on the error matrix and the Kappa test. From there we found that the accuracy was (84.6%) for the error matrix and (83.34%) for the Kappa test, and this indicates that the classification accuracy is very good It is acceptable and can be relied upon and recommended for classification.

Detection of Land Use Land Cover Changes Using Remote Sensing and GIS Techniques in a Secondary City in Bangladesh

This study aims at classifying land use land cover (LULC) patterns and detect changes in a 'secondary city' (Savar Upazila) in Bangladesh for 30 years i.e., from 1990 to 2020. Two distinct sets of Landsat satellite imagery, such as Landsat Thematic Mapper (TM) 1990 and Landsat 7 ETM+ 2020, were collected from the United States Geological Survey (USGS) website. Using ArcMap 10.3, the maximum likelihood algorithm was used to perform a supervised classification methodology. The error matrix and Kappa Kat were done to measure the mapping accuracy. Both images were classified into six separate classes: Cropland, Barren land, Built-up area, Vegetation, Waterbody, and Wetlands. From 1990 to 2020, Cropland, Barren land, Waterbody, and Wetlands have been decreased by 30.63%, 11.26%, 23.54%, and 21.89%, respectively. At the same time, the Built-up area and Vegetation have been increased by 161.16% and 5.77%, respectively. The research revealed that unplanned urbanization had been practiced in the secondary city indicated by the decreases in Cropland, Barren land, Wetland, and Waterbody, which also showed direct threats to food security and freshwater scarcity. An increase in Vegetation (mostly homestead vegetation) indicates some environment awareness programs that encourage people to maintain homestead and artificial gardens. The study argues for the sustainable planning of a secondary city for a developing country's future development.

Solving Fuzzy Error Matrix Equation based on Runge Kutta Method

Fuzzy error logic represents the object in the real world with (u, x) as <inline-formula><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M2"><mml:mrow><mml:mfenced close="}" open="{"><mml:mrow><mml:mfenced close="]" open="["><mml:mrow><mml:mi>U</mml:mi><mml:mo>,</mml:mo><mml:mi>S</mml:mi><mml:mfenced><mml:mi>t</mml:mi></mml:mfenced><mml:mo>,</mml:mo><mml:mtext> </mml:mtext><mml:mover><mml:mi>p</mml:mi><mml:mi>r</mml:mi></mml:mover><mml:mo>,</mml:mo><mml:mi>T</mml:mi><mml:mfenced><mml:mi>t</mml:mi></mml:mfenced><mml:mo>,</mml:mo><mml:mi>L</mml:mi><mml:mfenced><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:mfenced><mml:mo>,</mml:mo><mml:mfenced close="]" open="["><mml:mrow><mml:mi>x</mml:mi><mml:mfenced><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mi>f</mml:mi><mml:mfenced><mml:mrow><mml:mi>u</mml:mi><mml:mfenced><mml:mi>t</mml:mi></mml:mfenced><mml:mo>,</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:mfenced><mml:mo>,</mml:mo><mml:mi>G</mml:mi><mml:mi>u</mml:mi><mml:mfenced><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced></mml:mrow></mml:math></inline-formula>, Fuzzy error transformation matrix can be used to express six transformation methods, such as decomposition, similarity, addition, replacement, destruction and unit transformation. Based on solving equation XA=B and decomposition of p, this paper studies the solution of error matrix equation based on Runge Kutta method, in order to explore the law of error transformation from the perspective of solving matrix equation.

A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.