Reviewerly/exHarmony huggingface dataset:
Image pre-processing and shadow detection for building extraction from high-resolution satellite images

High resolution satellite imageries frequently contain shadows because of high-rise structures, especially in urban areas. One of the notable flaws in remotely sensed imaging that prevents information extraction is shadow. Building detection depends on the recognition of shadowy areas since they indicate a building's presence. Therefore, we can determine the structures' shapes and heights by utilizing the sun's illumination direction. But more crucially, detecting building shadows in high-resolution images can aid in better building detection. This work presents a shadow detection approach from a single RGB image to reduce the shadow effects on high-resolution images. We used LAB color space, which has one luminance (L) channel and two-color channels (A and B). To generate the buffer zone for building extraction, we transformed the RGB images to the CIELab color space to acquire the shadow masks. The proposed algorithm must be pre-processed to separate each shadow area into a separate data matrix to expand its functionality, evaluate the effectiveness of the method, and produce more accurate results when building shadows are close to one another. Additionally, by grouping the shadow zones into independent matrices, the method enables the detection of two or more distinct shadows of buildings with complex geometries and many parts in separate zones. Pre-processing helps us to prepare the images for further analysis. Using image pre-processing wisely, one can offer advantages and address issues that ultimately improve local and global feature detection. The effectiveness of building extraction and the outcomes of image analysis both dramatically improved results by image pre-processing.
Linking Earth observation data with ground truth from the open web

This paper presents a system for linking Earth Observation with open Web data, into a Linked Open Data architecture. The architecture has two components, one for extracting signals from Earth Observation data, and another for harvesting web sources. Both are linked with spatial objects. Web scraped data, either from APIs or crowd-sourced websites are geo-referenced and thematically annotated with standard vocabularies. The architecture has been demonstrated in two case studies, one for building permits and another for crowd-sourced observations of invasive aquatic plants.
Automated lineament extraction applied to high-resolution imagery Worldview-3 and LiDAR data for pegmatite mineral exploration

Lineament extraction is a commonly used technique in mineral exploration to identify geological structures such as fault scarps, joints, and folds. However, the accuracy of this technique can be limited by factors such as the low spatial resolution of the data. This study aims to address these limitations by exploring the potential of high spatial resolution data for extracting linear structures in Tysfjord, northern Norway. Two types of high-resolution data were utilized for lineament extraction: (i) WorldView-3 (WV3) satellite orbital imagery with a ground sample distance (GSD) of two meters, and (ii) Light Detection and Ranging (LiDAR) point cloud data, with a GSD of one meter. The LiDAR point cloud was utilized to generate a Digital Terrain Model (DTM), and automated lineament extraction was performed on both WV3 and LiDAR data using the lineament algorithm (LINE) available in PCI Geomatics software. A comparison was conducted using Sentinel two images to analyze the impact of utilizing high-resolution images on the final results. The outcomes illustrate that high-resolution images hold substantial potential for extracting lineaments and can aid in identifying mineral deposits and neotectonic activity. In the future, these findings could be integrated with other remote sensing methods to enhance the capabilities of remote sensing for mineral exploration.
A GPU-accelerated algorithm for copy move detection in large-scale satellite images

With the diffusion of advanced image editing software, image manipulation is becoming an impelling aspect also for satellite images. In a Copy-Move (CM) forgery, part of the image is copied and pasted elsewhere into the same image. In the satellite domain, CM can be performed with the intent of propagating misleading information on the geography and morphology of the landscapes pictured in the images. The best algorithms for CM detection rely on a multi-step procedure involving extraction of image descriptors (keypoints), keypoint matching and finally clustering, for the localization of the forged area. The large size of many satellite images and their richness of details, often prevent the adoption of off-the-shelf tools developed for multimedia images. Due to the large number of keypoints typically present in satellite images, in fact, the computational complexity and memory requirements for SIFT keypoints extraction, matching, clustering and forgery localisation is prohibitive. In this paper, we propose a CM detection algorithm that can successfully process very high resolution satellite images, where off-the-shelf alternatives are crashing due to system memory exhaustion. The proposed algorithm is based on three main strategies powered by GPU acceleration: i) multi-threaded tile-based SIFT keypoints extraction, ii) optimised batch-based descriptors matching, iii) clustering and localisation of manipulated pixels exploiting tensors instead of a sliding window approach. Experiments carried out on images belonging to the ESA WorldView-2 European Cities dataset and on a set of hand-made copy-move forgeries with resolution above one Gigapixel, show the good performance of the proposed algorithm in terms of processing time and memory consumption.
Outcomes of continuous monitoring of crucial infrastructure in the framework of “PROION” project

“PROION” is a research project, focused on the development of a platform for the continuous monitoring of high priority infrastructure (public infrastructure, dams, bridges, etc.) in the broader area of the Hellenic Supersite, named Enceladus. The project started on September 2020 and it was financially supported by the European Union and the Hellenic government. Monitoring is based on the combination of instrumental and remote sensing measurements in conjunction with soft computing algorithms to assess infrastructure stability. The results of the project are presented in the current study. «Acknowledgment: This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EΔK-02396 Μultiparametric monitoring platform with micro-sensors of eNceladus hellenIc supersite)».
Moving ship detection of satellite video based on a weakly supervised detector

Restricted by the limited accessible data resources and the high cost of frame-by-frame labeling, fully supervised object detection is difficult to meet the needs of satellite video applications. In this paper, we propose a weakly-supervised satellite video detector based on salient feature fusion and boundary noise exploitation that enables moving ship detection without relying on object instance labeling. To mitigate pseudo-motion disturbances such as background movement, waves, and illumination changes, we first construct salient fusion features and then use Gaussian background modeling to generate high-quality pseudo-labels. To fully exploit the boundary information of noisy masks in pseudo label, we improve the Mask R-CNN by designing a noise-tolerant branch that fuses low-resolution features to mitigate the interference of inaccurate mask boundaries and guiding the network to learn boundary-related region features through boundary preserving mapping to enable better alignment of the predicted mask with the actual object. The experimental evaluation results show that the proposed method significantly outperforms other weakly supervised moving object detection methods and achieves comparable performance to fully supervised method.
Improved homographic adaptation for keypoint generation in cross-spectral registration of thermal and optical imagery

Autonomous navigation is an important area of research for aerial vehicles. Visual odometry and simultaneous localization and mapping algorithms are critical for the three-dimensional understanding of the environment. For that purpose, consistent multi-spectral maps of the environment should be generated. Existing pixel-based image registration methods are accurate but too slow to operate in real-time. Recently deep learning is used to develop feature-based data-driven methods for generating interest points and associated descriptors for registering multi-spectral image pairs. These methods are fast and perform better than existing methods for optical images. However, the results are less convincing for thermal image registration. In this work, we propose an improved multi-spectral homographic adaptation technique to generate highly repeatable ground truth interest points that are invariant across viewpoint changes in both spectra. These interest points are used to train the MultiPoint image registration network. Simulation results show that our improved model outperforms existing techniques for feature-based image alignment of optical and thermal images.
Mapping NYF pegmatite outcrops through high-resolution Worldview-3 imagery

Several studies on remote sensing and mineral exploration have been developed or improved in the last decade. However, the low spatial resolution of satellites is a recurring problem in many cases where the mineral or rock is much smaller than the pixel size of the satellite images. This phenomenon, called sub-pixel occurrence, generates an extremely mixed pixel that difficult the performance of conventional classification algorithms. Satellites with high spatial resolution, such as the Worldview-3 (2 m Ground Sample Distance), have become an essential tool for mineral exploration studies. In addition to its high spatial resolution, the Worldview-3 has 16 bands, eight in the Visible and Near-infrared (VNIR) and eight in the Shortwave Infrared (SWIR) region, which further increases its potential for mineral exploration. This study applies a spectral unmixing-based method, using the Spectral Hourglass Wizard Workflow (SHW), to extract and select pegmatites endmembers from the WorldView-3 images. Further, these endmembers were used for a subpixel classification, performed through Mixture Tuned Matched Filtering (MTMF), to map possible pegmatite outcrops in the Tysfjord pegmatite field, Norway. After classification a high Digital Terrain Model (DTM) hillshade acquired from LiDAR technology and geological data were used to eliminate false positives. The subpixel classification results were compared with geological data and 17 points of interest for pegmatite exploration were selected for further field validation. This work shows the potential of the Worldview-3 high-resolution images processed with a spectral unmixing-based method for mineral exploration in an area of subpixel occurrence. The results are encouraging and show great value for the scientific field of pegmatite exploration.
Genetic algorithm for feature selection in mammograms for breast masses classification

ABSTRACTThis paper introduces a Computer-Aided Detection (CAD) system for categorizing breast masses in mammogram images from the DDSM database as Benign, Malignant, or Normal. The CAD process involves Pre-processing, Segmentation, Feature Extraction, Feature Selection, and Classification. Three feature selection methods, namely the Genetic Algorithm (GA), t-test, and Particle Swarm Optimization (PSO) are used. In the classification phase, three machine learning algorithms (kNN, multiSVM, and Naive Bayes) are explored. Evaluation metrics like accuracy, AUC, precision, recall, F1-score, MCC, Dice coefficient, and Jaccard coefficient are used for performance assessment. Training and testing accuracy are assessed for the three classes. The system is evaluated using nine algorithm combinations, producing the following AUC values: GA+kNN (0.93), GA+multiSVM (0.88), GA+NB (0.91), t-test+kNN (0.91), t-test+multiSVM (0.86), t-test+NB (0.89), PSO+kNN (0.89), PSO+multiSVM (0.85), and PSO+NB (0.86). The study shows that the GA and kNN combination outperforms others.KEYWORDS: Mammogramsbreast massfeature selectionGenetic algorithm Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingNo funding is used to complete this project.Notes on contributors G Vaira SuganthiDr. Vaira Suganthi G has 20 years of teaching experience. Her area of interest includes Image Processing and Machine Learning. J SuthaDr. Sutha J has more than 25 years of teaching experience. Her area of interest includes Image Processing and Machine Learning. M ParvathyDr. Parvathy M has more than 20 years of teaching experience. Her area of interest include Image Processing, Data Mining, and Machine Learning.N Muthamil SelviMs. Muthamil Selvi N has 1 year of teaching experience. Her area of interest is Machine Learning.
MARTSIA: Enabling Data Confidentiality for Blockchain-Based Process Execution

Multi-party business processes rely on the collaboration of various players in a decentralized setting. Blockchain technology can facilitate the automation of these processes, even in cases where trust among participants is limited. Transactions are stored in a ledger, a replica of which is retained by every node of the blockchain network. The operations saved thereby are thus publicly accessible. While this enhances transparency, reliability, and persistence, it hinders the utilization of public blockchains for process automation as it violates typical confidentiality requirements in corporate settings. In this paper, we propose MARTSIA: A Multi-Authority Approach to Transaction Systems for Interoperating Applications. MARTSIA enables precise control over process data at the level of message parts. Based on Multi-Authority Attribute-Based Encryption (MA-ABE), MARTSIA realizes a number of desirable properties, including confidentiality, transparency, and auditability. We implemented our approach in proof-of-concept prototypes, with which we conduct a case study in the area of supply chain management. Also, we show the integration of MARTSIA with a state-of-the-art blockchain-based process execution engine to secure the data flow.
Singled Out and Mocked: Intersection of (Hetero)Sexism and Ableism and Mobilization of Anti-Discourses in Online Hatred towards Hypervisibilized Youth Activists

This article contributes to the relatively scarce research on the intersection of various anti-discourses in online hatred by focusing on online verbal attacks on publicly active, nonmature actors of diverse genders. It reveals that patterns of the discursive rejection of youth political actors are similar to the more extensively described hatred against activist women. It also documents that these violent expressions are no longer limited to the realm of extreme or far-right political circles, the typical focus of previous studies, but have penetrated mainstream civic discussions across the media sphere. Youth actors are vulnerable, the article argues, because their individual characteristics are singled out, made hypervisible and mocked as abnormal in the online sphere, or because they are associated with ideologies which the discussants reject as dangerous in their construction of imagined collective identities and mobilization of anti-discourses.
RAW2HSI: Learning-Based Hyperspectral Image Reconstruction from Low-Resolution Noisy Raw-RGB

In this paper, the problem of generating (hallucinating) a high-resolution hyperspectral image from a single low-resolution raw-RGB image is considered. To solve this problem, a general learning-based framework is proposed. It consists of two modules: a data adaptation module, and a backbone, deep feature extraction module. The data adaptation module is a shallow network consisting of pixel shuffling/unshuffling and shallow feature extraction. The deep feature extraction module which is an inherent part of many spectral reconstruction networks, aims at spectral super-resolution. Different spectral reconstruction networks have been studied as the backbone modules in the proposed framework. As a result of extensive simulations, it has been demonstrated that the proposed solution significantly outperforms the sequential approach of combining several state-of-the-art methods of image demosaicing, denoising, spatial and spectral super-resolution (by up to 6 dB in PSNR), and has large savings in the computational complexity (by over 5 times) with respect to the sequential method.
On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data

Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.
Monitoring and spatial distribution of lava and volcanic eruptions from Cumbre Vieja ridge on island La Palma in the Canary Islands occured in 2021 by methods of remote sensing

In the present research the eruption at the Cumbre Vieja volcanic ridge, lasted from 19 September to 13 December 2021, and the lava effusions were monitored. Multispectral images from satellites are widely exploited for mapping lava flows and lava fields. Satellite data contain reflective and emissive information in the visible, infrared, and thermal portions of the electromagnetic spectrum about territories covered by lava flow. An estimation of the area covered by the lava flow and the newly formed peninsula were made by use of Copernicus data. Satellite images from Multi Spectral Imager (MSI) onboard Sentinel-2 satellite and Landsat eight to nine (OLI) mission were chosen during the period of volcano activity and are processed in order to visualize and estimate the extent of the natural hazard and to evaluate its negative consequences. A set of different band combinations of optical satellite data and lava indices were applied. An attempt to apply Tasseled Cap Transformation of optical data was made in order to obtain more precise and accurate information about lava flow’s dynamics and to distinguish recently cooled from older lava fields.
Automated method for generating datasets of infrared or visible images for context-specific training of deep neural network-based object detectors

Performing object detection and recognition at the imaging sensor level, raises many technical and scientific challenges. Today’s state-of-the-art detection performances are obtained with deep Convolutional Neural Network (CNN) models. However, reaching the expected CNN behavior in terms of sensitivity and specificity require to master the training dataset. We explore in this paper a fast and automated method to acquire images of vehicles in infrared and visible range employing a commercial inspection drone equipped with thermal and visible range cameras, associated to a dedicated data-augmentation method for automated generation of context-specific machine learning datasets. The purpose is to successfully train a CNN to recognize the vehicles in realistic outdoor situations in infrared or visible range images, while reducing mandatory access to the vehicles of interest and the needs of complex and long outdoor image acquisition. First results demonstrate the feasibility of our approach for training a deep neural network-based object detector for vehicle detection and recognition applications in aerial images.
Some Further Properties of the Lucas Sequences

This chapter is a compilation of some miscellaneous results involving the Lucas sequences. We begin with a discussion of the relationship of the Lucas sequences to the sine and cosinesCosine functions and then describe the periodicity properties of U and V modulo an integer m. This is followed by a section devoted to the relationship of the Lucas sequences to the Chebyshev and the Dickson polynomials. We then review some additional arithmetic properties of the Lucas sequences that are not covered in Chap. 2 and establish what Lucas called his fundamental theorem. Subsequently, we provide some insights on the factorization of the terms of U. Next, we describe Lehmer’s extension of Lucas’ sequences and examine some of their properties. The following section makes use of our earlier factorization results to prove an old theorem of Carmichael on primitive divisors of (Un) and of the Lehmer sequences. We conclude with a brief treatment of the topic of when the Lucas sequence U can contain a complete set of residues modulo an integer m.
Applications

The purpose of this chapter is to review several diverse applications of the Lucas functions, particularly in the area of computational number theory. We will begin with a largely historical description of the Mersenne primes and then examine how the Lucas functions have been applied to the problem of primality testing. As this requires that we be able to compute Un and/or Vn for large values of n, we provide in Sect. 3.3 some algorithms by which this can be done. We next describe several subjects, such as solving congruences, integer factorization, Diophantine equations, and cryptography in which the Lucas functions have been applied.