Automatic detection of potential buried archaeological sites in Saruq Al-Hadid, United Arab Emirates

The use of remote sensing in archaeological research allows the prospection of sub-surfaces in arid regions non- intrusively before the on-site investigation and excavation. While the actual detection method of expected buried archaeological structures is based on visual interpretation, this work provides a supporting archaeological guidance using remote sensing. The aim is to detect potential archaeological remains underneath the sand. This paper focuses on Saruq Al-Hadid surroundings, which is an archaeologist site discovered in 2002, located about 50 km south-east of Dubai, as archaeologists believe that other archaeological sites are potentially buried in the surroundings. The input data is derived from a combination of wavelength L-band Synthetic Aperture Radar (ALOS PALSAR), which is able to penetrate the sand, and multispectral optical images (Landsat 7). This paper develops a new strategy to help in the detection of suspected buried structures. The data fusion of surface roughness and spectral indices enables tackling the well-known limitation of SAR images and offers a set of pixels having an archaeological signature different from the manmade structures. The potential buried sites are then classified by performing a pixel-level unsupervised classification algorithm such as K-means cluster analysis. To test the performance of the proposed method, the results are compared with those obtained by visual interpretation

Autonomous palm tree detection from remote sensing images-UAE dataset

Autonomous detection and counting of palm trees is a research field of interest to various countries around the world, including the UAE. Automating this task saves effort and resources by minimizing human intervention and reducing potential errors in counting. This paper introduces a new High Resolution (HR) remote sensing dataset for autonomous detection of palm trees in the UAE. The dataset is collected using Unmanned Aerial Vehicles (UAV), and it is labeled properly in PASCAL VOC and YOLO formats after preprocessing and visually inspecting its quality. A comparative evaluation between Faster-RCNN and YOLOv4 networks is then conducted to observe the usability of the dataset in addition to the strengths and weaknesses of each network. The dataset is publicly available at https://github.com/Nour093/Palm-Tree-Dataset

Spatio-temporal analysis and machine learning for traffic accidents prediction

Traffic accidents impose significant problems in our daily life due to the huge social, environmental, and economic expenses associated with them. The rapid development in data science, geographic data collection, and processing methods encourage researchers to evaluate, delineate traffic accident hotspots, and to effectively predict and estimate traffic accidents. In this study, traffic accidents dataset that covers United Kingdom for the time period between 2012-2014 is investigated. The methodology consists of extracting features weights, and then using these weights with statistical methods provided in ArcGIS in order to classify accidents according to severity and perform hotspot analysis and severity prediction. The proposed method can be effectively used by different authorities to implement an improved planning and management approaches for traffic accident reduction. Moreover, it can identify and locate road risk segments where immediate action should be considered

Skin cancer classification model based on VGG 19 and transfer learning

Skin cancer is a concerning health issue with yearly increasing numbers. Detecting and classifying cancer type is problematic, especially since patients have to undergo several diagnosis over lengthy periods of time, which hinders early treatment and survival chances. With the aid of digital image processing, features can be extracted to identify skin cancer and its different types. Convolutional Neural Networks (CNNs) recently emerged as powerful autonomous feature extractors, and they have high potential to achieve high accuracy with skin cancer diagnosis. In this paper, two cancer types in addition to one non-cancer type taken from Human Against Machine (HAM10000) dataset are classified using CNN model based on VGG 19 and Transfer Learning technique. The training strategy is explained, tested, and evaluated by calculating the network's overall accuracy and loss

Protection and authentication of Dubai digital elevation model using hybrid watermarking technique

Nowadays, with the availability of digital images and models at no cost on the World Wide Web, the need to provide copyright protection of multimedia data arises. Hence, digital watermarking products have been in high demand. Digital watermarking essentially embeds information into data in such a way that data usage is not affected, and it simultaneously protects and authenticates the data. This research paper deals with the development and evaluation of a watermarking technique for protection and authentication of Dubai Digital Elevation Model (DEM) provided by United States Geological Survey (USGS). The technique uses a hybrid combination of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), and it is implemented for the protection of DEM by embedding the ownership information in hybrid DCT-DWT domain and for checking the integrity of the elevation model by embedding hash-key information in the spatial domain. The proposed watermarking technique causes minimal distortion to the DEM and the performance is assessed by using Peak Singal-to-Noise Ratio (PSNR), Wavelet Signal-to-Noise Ratio (WSNR), and Structural Similarity Index Measurement (SSIM). The results show promising performance with strong robustness of watermark information ownership for many intentional and non-intentional attacks, in addition to precise detection of localized modified areas on tampered DEM

Dimensionality reduction techniques with HydraNet framework for HSI classification

Hyperspectral Imagery (HSI) classification is an important research area in remote sensing community due to its high efficiency in accurately analyzing ground features by assigning a class label to each pixel. This paper explores the use of Band Subset selection (BSS) methods as Dimensionality Reduction (DR) pre-processing stage for HSI classification, and compares them to Principal Component Analysis (PCA) approach. BSS is the problem of selecting the most independent bands in HSI cube. Classification is then performed using a proposed multi-branch HydraNet model that combines 1D, 2D, and 3D convolution. HydraNet is trained and tested using the benchmark Pavia University dataset, and the results are evaluated using Kappa and Overall Accuracy. Experimental results show positive indications of the network's performance, especially when compared to other state-of-the-art CNN networks

A hybrid rexception network for COVID-19 classification from chest X-ray images

Nowadays, with the rapid spread of Coronavirus disease (COVID-19) across the globe, the necessity to develop an intelligent system for early diagnosis and detection the COVID-19 infectious disease increases. In recent researches, Chest Xray (CXR) of individual lungs became a common method to identify COVID-19 virus. Manual interpretation of the CXR images can be a lengthy process and subjective to human errors. In this paper, a hybrid Deep Learning model called ReXception is implemented, trained, and evaluated using two types of datasets; Mutliclass and Binary. The network is evaluated based on its overall accuracy, loss, precision, and recall, in addition to the running time and network size. The results show positive indications of the network's performance, especially when compared to other state-of-the-art networks

Tri-CNN : a three branch model for hyperspectral image classification

Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven to be effective in the recent years. In particular, Convolutional Neural Networks (CNNs) have demonstrated extremely powerful performance in such tasks. However, The lack of training samples is one of the main contributors to low classification performance. Traditional CNN-based techniques under-utilize the inter-band correlations of HSI because they primarily use 2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial information using the same operation. While this overcomes the limitation of 2D-CNNs, it may lead to insufficient extraction of features. In order to overcome this issue, we propose an HSI classification approach named Tri-CNN which is based on a multi-scale 3D-CNN and three-branch feature fusion. We first extract HSI features using 3D-CNN at various scales. The three different features are then flattened and concatenated. To obtain the classification results, the fused features then traverse a number of fully connected layers and eventually a softmax layer. Experimental results are conducted on three datasets, Pavia University (PU), Salinas scene (SA) and GulfPort (GP) datasets, respectively. Classification results indicate that our proposed methodology shows remarkable performance in terms of the Overall Accuracy (OA), Average Accuracy (AA), and Kappa metrics when compared against existing methods

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

S

withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

n/