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The simplest and the most commonly used measure for assess the classification model quality is parameter Q2 = 100 (p + n) / N (%) named the classification accuracy, p, n and N are the total numbers of correctly predicted compounds in the first and in the second class, and the total number of elements of classes (compounds) in data set, respectively. Moreover, the most probable accuracy that can be obtained by a random model is calculated for two-state model by the formulae Q2,rnd = 100 [(p + u) (p + o) + (n + u) (n + o)] / N2 (%), where u and o are the total number of under-predictions (when class 1 is predicted by the model as class 2) and over-predictions (when class 2 is predicted by the model as class 1) in data set, respectively. Finally, the difference between these two parameter ΔQ2 = Q2 – Q2,rnd is introduced, and it is suggested to compute and give ΔQ2 for each two-state classification model to assess its contribution over the accuracy of the corresponding random model. When data set is ideally balanced having the same numbers of elements in both classes, the two-state classification problem is the most difficult with maximal Q2 = 100 % and Q2,rnd = 50 %, giving the maximal ΔQ2 = 50 %. The usefulness of ΔQ2 parameter is illustrated in comparative analysis on two-class classification models from literature for prediction of secondary structure of membrane proteins and on several quanti¬tative structure-property models. Real contributions of these models over the random level of accuracy is calculated, and their ΔQ2 values are compared mutually and with the value of ΔQ2 (= 50 %) for the most difficult two-state classification model.

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

Mapping and tracking invasive alien plant species (IAPS) and their invasiveness can be achieved using remote sensing (RS) and geographic information systems (GIS). Continuous monitoring using RS, GIS and modelling are fundamental tools for informing invasion and management strategies. Using systematic comparisons, we look at three remote sensing imagery platforms and how accurately they can be classified within the Vhembe biosphere reserve, Limpopo Province, South Africa. Supervised classification of National Geospatial Information Colour Digital Aerial Imagery, DigitalGlobe Worldview 2 and CNES SPOT 6 was performed. The Spectral Angle Mapper (SAM) algorithm was used to identify the best satellite for species-level classification. The accuracy of the classifications produced an overall accuracy (OA) of 71% with a Kappa coefficient (KC) of 0.76 for CDA photographs, an OA of 81% and a KC of 0.80 for Worldview 2, and an OA of 89% with a KC of 0.86 for SPOT 6 imagery. Therefore, SPOT 6 imagery came out as the most suitable for species-level classification. The classification results from the SPOT 6 imagery were used as input data for further species distribution modelling of Mauritius Thorn and River Red Gum in the VBR.

Scenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy.

Green vegetation is an essential part of natural resources and is vital to the ecosystem. Simultaneously, with improving people’s living standards, food security and the supply of forage resources have become increasingly the focus of attention. Therefore, timely and accurate monitoring and accurate and timely vegetation classification are significant for the rational utilization of agricultural resources. In recent years, the unmanned aerial vehicle (UAV) platform has attracted considerable attention and achieved great success in the application of remote sensing identification of vegetation due to the combination of the advantages of satellite and airborne systems. However, the results of many studies haven’t yet been synthesized to provide practical guidance for improving recognition performance. This study aimed to introduce the primary classifiers used for UAV remote-sensing vegetation identification and conducted a meta-analysis of relevant research on UAV remote-sensing vegetation identification. This meta-analysis reviewed 79 papers, analyzed the general characteristics of spatial and temporal distribution and journal sources, and compared the relationship between research objectives, sensor types, spatial resolution, research methods, number of target categories, and the overall accuracy of the results. Finally, a detailed review was conducted on how unmanned aerial vehicle remote sensing is applied in vegetation identification, along with the current unresolved issues and prospects.

Background This study aims to identify robust radiomic features for Magnetic Resonance Imaging (MRI), assess feature selection and machine learning methods for overall survival classification of Glioblastoma multiforme patients, and to robustify models trained on single-center data when applied to multi-center data. Methods Tumor regions were automatically segmented on MRI data, and 8327 radiomic features extracted from these regions. Single-center data was perturbed to assess radiomic feature robustness, with over 16 million tests of typical perturbations. Robust features were selected based on the Intraclass Correlation Coefficient to measure agreement across perturbations. Feature selectors and machine learning methods were compared to classify overall survival. Models trained on single-center data (63 patients) were tested on multi-center data (76 patients). Priors using feature robustness and clinical knowledge were evaluated. Results We observed a very large performance drop when applying models trained on single-center on unseen multi-center data, e.g. a decrease of the area under the receiver operating curve (AUC) of 0.56 for the overall survival classification boundary at 1 year. By using robust features alongside priors for two overall survival classes, the AUC drop could be reduced by 21.2%. In contrast, sensitivity was 12.19% lower when applying a prior. Conclusions Our experiments show that it is possible to attain improved levels of robustness and accuracy when models need to be applied to unseen multi-center data. The performance on multi-center data of models trained on single-center data can be increased by using robust features and introducing prior knowledge. For successful model robustification, tailoring perturbations for robustness testing to the target dataset is key.

The method of target classification known as hyperspectral imaging (HSI) is commonly used in the field of remote sensing. However, recent research has shown that categorizing HSI can be problematic due to the limited availability of labelled data. There is significant interest in applying this technique to hyperspectral data. Previous graph neural network (GNN)-based methodologies often used a graph filter to obtain HSI properties, but the potential advantages of various graph neural networks and graph filters have not been fully exploited. GNNs often operate under the assumption that a node’s neighbours are independent of each other, neglecting potential interactions among them. To overcome these limitations, graph attention neural network-based remote target classification (GANN-RTC) has been proposed. It has the ability to handle both the labelled and unlabelled datasets. To evaluate the performance of GANN-RTC, we compared it with existing methods using performance measures such as individual class accuracy, overall accuracy, and the Kappa coefficient. The findings indicate that the GANN-RTC yields enhancements in OA, ICA, and KC by 2.32, 7.89, and 2.47% for the Cuprite dataset and 4.79, 11.85, and 2.82% for the Pavia University dataset. Research highlights The research focuses on remote target classification in hyperspectral imaging using a Graph Attention Neural Network. Previous methods in this field have not fully utilized the potential advantages of graph filters and graph neural networks. The proposed approach overcomes limitations by considering interactions between neighbouring nodes and can handle both labelled and unlabelled datasets. Performance evaluation shows significant improvements in overall accuracy, individual class accuracy, and the Kappa coefficient compared to existing state-of-the-art methods.

Background Current prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction. Methods The training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements. Results In internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery. Conclusions Prediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.

Reference data collected to validate land-cover maps are generally considered free of errors. In practice, however, they contain errors despite best efforts to minimize them. These errors propagate during accuracy assessment and tweak the validation results. For photo-interpreted reference data, the two most widely studied sources of error are systematic incorrect labeling and vigilance drops. How estimation errors, i.e., errors intrinsic to the response design, affect the accuracy of reference data is far less understood. In this paper, we analyzed the impact of estimation errors for two types of classification systems (binary and multiclass) as well as for two common response designs (point-based and partition-based) with a range of sub-sample sizes. Our quantitative results indicate that labeling errors due to proportion estimations should not be neglected. They further confirm that the accuracy of response designs depends on the class proportions within the sampling units, with complex landscapes being more prone to errors. As a result, response designs where the number of sub-samples is predefined and fixed are inefficient. To guarantee high accuracy standards of validation data with minimum data collection effort, we propose a new method to adapt the number of sub-samples for each sample during the validation process. In practice, sub-samples are incrementally selected and labeled until the estimated class proportions reach the desired level of confidence. As a result, less effort is spent on labeling univocal cases and the spared effort can be allocated to more ambiguous cases. This increases the reliability of reference data and of subsequent accuracy assessment. Across our study site, we demonstrated that such an approach could reduce the labeling effort by 50% to 75%, with greater gains in homogeneous landscapes. We contend that adopting this optimization approach will not only increase the efficiency of reference data collection, but will also help deliver more reliable accuracy estimates to the user community.

The marshes of southern Iraq are among the most important and largest wetlands in the Middle East and the world are characterized by the freshness of their waters and their environmental diversity. The marshes have undergone many changes during the past decades and to discover and study these changes, remote sensing data will be used as sources of information and data in this study represented by the Landsat 8 satellite images (OLI), the images were collected for years from 2013 to 2019, and by applying remote sensing techniques and geographic information systems techniques, changes in Al-Hammar marsh were detected during the past seven years, the supervised classification method (maximum likelihood) was applied to classify the region Were identified six main categories of the land cover (deep water, shallow water, small cane, large cane, plant, soil) using the software (ENVI 5.3), the final maps were produced for classification using (ArcGIS 10.4.1) software, the results showed Significant change in the water content of Al-Hammar marsh and the increase in the proportion of flooding during the year 2019 to the highest rate since 2013. In addition, the results showed the accuracy and success of the supervised classification method (maximum likelihood) in the classification of images as they are considered the best classification methods, the fastest and high accuracy.