Reviewerly/exHarmony huggingface dataset:
Multi-Task Generative Adversarial Networks for Semantic-Guided Remote Sensing Image Generation

Semantic layout synthesis based on Generative Adversarial Networks (GANs) has made significant advances, however, the generation of high-quality real remote sensing images through semantic layouts remains a significant challenge. In this paper, we propose a Multi-Task Generative Adversarial Network (MTGAN) that utilizes semantic layouts to generate remote sensing images featuring five common geographic objects. Our approach takes into account the unique characteristics of remote sensing images, such as multiple scales, multiple objects, low inter-class separability, and high intra-class heterogeneity. The MTGAN utilizes a global-local GAN structure, with the global generator producing global context information and the local generator generating specific class information. By combining global macro information and local detail information, the MTGAN is able to generate remote sensing images with close contextual connections and clear details of geographical objects.
Design of Brake Pump Information Management System Based on Feature Extraction Algorithm

Pumping stations and sluice stations in polder areas generally undertake many tasks such as flood control and drainage in polder areas. The topography and topography of polder areas are flat and low-lying, and most of them are located in areas with dense river networks, so water resources are abundant. During the flood period, the water level of the river outside the polder area is often higher than that of the fields, which is more prone to natural disasters such as floods. Due to the aging equipment, many pump gates are poorly managed, which makes the operation and maintenance of pump gates difficult. Therefore, based on the feature extraction algorithm, this paper establishes a gate pump MIS (Management Information System) to monitor the hydrology and water resources of the river. The construction of gate pump MIS is based on database design and information retrieval algorithm, combined with information feature extraction and optimized information scheduling technology. The feature extraction algorithm is used to adaptively fuse and cluster the semantic correlation features of distributed data in large-scale MIS, and the feature compressor is used to reduce the dimension of the storage space of large-scale MIS, which improves the target data mining ability and the adaptive scheduling ability of gate pump MIS.
HGR Maximal Correlation Augmented Cross-Modal Remote Sensing Retrieval

Most existing methods for cross-modal content-based remote sensing image retrieval (CM-CBRSIR) have only focused on implementations by optimizing the projected features in a common space under the Euclidean distance criterion. In this work, to better bridge the heterogeneity gap caused by the modality difference, we propose a Hirschfeld–Gebelein–Rényi (HGR) maximal correlation augmented CM-CBRSIR method by utilizing the HGR maximal correlation between different modalities. Except for optimizing the projected features under the Euclidean distance constraints, another feature projection, which carries the information of the HGR maximal correlation, is learned during the training phase. In the retrieval phase, we combine the information learned by the Euclidean distance criterion and HGR maximal correlation based on the Dempster–Shafer (DS) evidence theory. Experimental results show that the proposed method outperforms the existing state-of-the-art methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Research on Computer Image Intelligent Recognition Algorithm Based on Deep Learning

With the continuous development of social economy and science and technology, the mysteries of the computer has also been gradually opened, people also more and more deep and understanding of computer technology, each based on computer technology is also put forward higher request, and for intelligent computer IR() algorithm based on the deep learning (DL) study also gradually expand, however, the current study depth still exist many problems, There is an urgent need to reform it. Based on this, this paper focuses on the analysis of the DL-based computer image intelligent recognition algorithm research problems, but also based on the DL-based computer image intelligent recognition algorithm research problems are given the construction strategy.
A Fast and Accurate Method for Remote Sensing Image-Text Retrieval Based On Large Model Knowledge Distillation

With the increasing development of remote sensing (RS) technology, remote sensing cross-modal image-text retrieval (RSCMITR) task has gradually attracted wide attention. At present, the large-scale pre-training model is brilliant in the field of natural images cross-modal retrieval, but the current RSCMITR models do not focus on it, resulting in less retrieval performance improvement. This paper proposes a lightweight network structure based on large-scale pre-training model and knowledge distillation, designing a lightweight model based on separable convolution and text convolution. Knowledge distillation technology is used to make the Light model learn the hidden knowledge of large-scale model CLIP-RS, which realizes fast and accurate retrieval. The proposed method achieves state-of-the-art performance on four commonly used RSCMITR datasets.
ISO 9126 Quality Model For Evaluating of Student Portal: Al-Madinah International University (Case study)

E-learning is a teaching system that involves electronic resources such as computers and the Internet, and the student portal is an essential tool that supports learning in universities. However, there is a limited evaluation model for educational websites. Therefore, a framework is required to guide the creation of such a model. The study conducted evaluates the quality of the student portal at Al-Madinah International University based on ISO 9126 quality model from the student's perspective, and the results show a good quality portal. Nonetheless, students suggest improvements to enhance its effectiveness, ease of use, and learning process.
Hyper-Spectral Image Classification Using Adiabatic Quantum Computation

Supervised machine learning techniques are widely used for hyper-spectral images segmentation. A typical simple scheme of classification of such images probabilistically assigns a label to each individual pixel omitting information about pixel surroundings. In order to achieve better classification results for real world images one has to agree the local label obtained from the classifier with the classes of pixel neighborhood. A popular way to do it is through a probabilistic graphical model, where label distributions for individual pixels are mapped into a graph of neighborhood relations. One way to realize this approach is to use Ising models, where class probability is mapped to spin energy and class-class interaction is mapped to the spins coupling. By finding low energy states of such an Ising model we can perform post-processing of segmented images. In this work we present how this postprocessing can be implemented using a quantum annealer.
Image De-Raining Enhancement Tool

In this fast-paced technological day and age, computer vision systems significantly and positively impact our daily lives. They are the basis of many systems that are now crucial in our everyday lives like healthcare and medical imaging, object recognition and visual search, autonomous vehicles, industrial automation, and robotics, as well as accessibility and assistive technologies. These are just a few examples of how computer vision has become indispensable. These systems primarily depend on the quality of images, where the slightest distortion could lead to catastrophic malfunctions. Therefore, in this research, a developed Spinning Detail Perceptual Generative Adversarial Network (SDP-GAN) was proposed for single image de-raining, aiming to enhance the performance of computer vision-based systems. The presented algorithm is a Generative Adversarial Network (GAN) based network and it is built using two subnetworks: the rain streak generative network G and the discriminative network D. To further enhance the perceived quality of the derained images, modifications have been added to find the most appropriate values for the model's hyperparameters to allow it to generate the most optimal results. The batch size, number of epochs, optimizers, filters, and loss functions have all been altered to ultimately improve the performance of the model; consequently, improving the overall de-raining process. Numerous tests on commonly used synthetic datasets validate that the proposed model significantly outperforms several state-of-the-art approaches.
Novel Deep Learning Framework for Imputing Holes in Orthorectified VHR Images

Many downstream applications, such as agricultural monitoring, damage assessments, and urban planning, have benefited from remote sensing imagery. Ortho image generation from very high-resolution images has unpleasant side effects in places of building occlusions leaving holes in the orthorectified images. As a result, blank pixels caused by orthorectification must be filled in prior to downstream tasks such as machine learning. In this paper, we present a new deep learning architecture based on Wide Activation Super Resolution (WDSR) network combined with an Adaptive Instance Normalization (AdaIN) based style transfer for imputing holes (missing pixels) in orthorectified images. To test and validate the performance of imputation algorithms, we developed a new multi-resolution benchmark dataset consisting of WorldView-3 and Sentinel-2 images. Our experiments show that the WDSR framework outperforms other state-of-the-art (SOTA) deep learning methods and the Ordinary Kriging method. Our proposed method has improved the mean squared error (MSE) by at least 12.54% in comparison to existing SOTA methods.
Research on Ultrasonic Image Compression in Logging while Drilling: An Asymmetric Convolutional Autoencoder

Logging while drilling (LWD) is an efficient technique that provides real-time information about the reservoir while drilling. However, due to the limited transmission rate of the cables, only low-dimensional information can be transferred rather than high-dimensional logging images. This paper proposes an image compression method called the asymmetric convolutional autoencoder (ACAE) that can map raw logging data into low-dimensional embedding vectors for real-time transmission through the cables. The trained ACAE consists of two primary components: an encoder deployed onto the downhole computing module and a decoder deployed onto the ground computer. To decode low-dimensional embedded information with higher quality, attention modules are utilized in the decoder to suppress noise and attain smoother logging images. Experimental results demonstrate that our method can effectively compress original logging data with a small compression rate and reconstruct logging images.
Investigating Model Robustness Against Sensor Variation

Large datasets of geospatial satellite images are available online, exhibiting significant variations in both image quality and content. These variations in image quality stem from the image processing pipeline and image acquisition settings, resulting in subtle differences within datasets of images acquired with the same satellites. Recent progress in the field of image processing have considerably enhanced capabilities in noise and artifacts removal, as well as image super-resolution. Consequently, this opens up possibilities for homogenizing geospatial image datasets by reducing the intra-dataset variations in image quality. In this work, we show that conventional image detection and segmentation neural networks trained on geospatial data are robust neither to noise and artefact removal preprocessing, nor to mild resolution variations.
Despeckling of SAR Images Using CNN and Multiresolution Fusion

The despeckling of Synthetic Aperture Radar images using two different convolutional neural networks architectures is presented in this paper. The first method presents a dilated convolutional network inserted into Siamese convolutional neural network. Recently, attention mechanism have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a design of convolutional neural network using attention mechanism for encoder-decoder type of network. The framework consists of multi scale spatial attention network to improve modeling of semantic information at different spatial level and additional attention mechanism to optimize feature propagation. Both proposed methods are different in design, but they provided very comparable despeckling results in subjective and objective measurements, when correlated speckle noise was concerned. The experimental results were evaluated on synthetically generated speckled images and real SAR images. Proposed methods in this paper are able to despeckle SAR images and preserve SAR features.
Classification &amp; Clustering of Volcano-Seismic Events Using Supervised and Unsupervised Methods

This study looks into unsupervised and supervised methods for classifying and clustering volcano-seismic time series data. Due to the resource intensive labelling process required to understand volcano-seismic signals it is important to explore unsupervised analysis techniques in this domain. This study offers a comparison between the performance of supervised and unsupervised methods and offers insight into their potential for frame by frame classification of volcano-seismic signals.
Incremental Land Cover Classification via Strategies for Edge Removal and Feature Point Aggregation

Convolutional neural networks will face the problem of catastrophic forgetting in the process of incremental learning. To solve this problem, we propose an incremental learning method called the strategy of edge removal and feature point aggregation, or ERFPA for short. In the cross-entropy loss, we perform edge detection and removal on the labels generated by the old model predictions, and then fuse them with the new labels. We calculate the mean point of different classes, and make the model learn features better by narrowing the distance with similar pixels. As demonstrated by the results of our experiment, on two remote sensing image datasets: CCF and Vaihingen, our method achieves state-of-the-art results.
Karate First Kata Performance Analysis and Evaluation with Computer Vision and Machine Learning

Karate kata is a martial art that involves performing a sequence of movements with accuracy and speed. In this paper, we present a system capable of automatically recognizing and evaluating karate performances using computer vision and machine learning. Our system comprises four main steps: pose estimation, movement detection, feature extraction, and classification. We utilize the ensembling of two models and 14 different binary classification models in the classification step. We have successfully developed a method that compensates for camera shake in movement detection within our karate performance recognition system. Subsequently, we compute features such as skew, skewness, kurtosis, and covariance. Lastly, a machine learning model is employed to classify the first karate kata video. We curate a dataset of karate kata videos and achieve an impressive accuracy of 91.99% in our classification task.
Robust Image Captioning with Post-Generation Ensemble Method

Remote sensing image captioning is a research domain that aims to automatically generate natural language descriptions of the contents within remote sensed images. Providing accurate depictions of image contents holds great significance for downstream applications such as image retrieval and image understanding. While there is a pressing need for reliable results, current research predominantly focuses on single captioning algorithms, striving to enhance their performance on specific target-oriented datasets. Undoubtedly, this research trajectory is highly important. However, we believe that relying solely on the output of a single captioner may introduce a vulnerability from a robustness standpoint. This concern is particularly relevant in remote sensing, where the scarcity of large-scale datasets can limit the robustness and reliability of resulting algorithms. In this paper, we propose an approach that harnesses the advantages of ensembles to enhance accuracy and reliability in the context of image captioning. Our method introduces a novel technique for utilizing an ensemble of diverse captioning algorithms and automatically selecting the most suitable caption from the set of predictions. By decoupling the description generation and selection phases, this approach enables high flexibility of integration of architecturally different captioning algorithms in the pipeline.
Smart library construction and service mode driven by big data

The traditional library service is inefficient and can't meet the needs of readers. "Big data" provides new possibilities for the development of libraries. This paper analyzes the role of big data in smart library, and puts forward the architecture, implementation mode and service mode of smart library under the environment of big data. The service mode can improve service efficiency and is a personalized service mode oriented by readers' needs.
LIT-4-RSVQA: Lightweight Transformer-Based Visual Question Answering in Remote Sensing

Visual question answering (VQA) methods in remote sensing (RS) aim to answer natural language questions with respect to an RS image. Most of the existing methods require a large amount of computational resources, which limits their application in operational scenarios in RS. To address this issue, in this paper we present an effective lightweight transformer-based VQA in RS (LiT-4-RSVQA) architecture for efficient and accurate VQA in RS. Our architecture consists of: i) a lightweight text encoder module; ii) a lightweight image encoder module; iii) a fusion module; and iv) a classification module. The experimental results obtained on a VQA benchmark dataset demonstrate that our proposed LiT-4-RSVQA architecture provides accurate VQA results while significantly reducing the computational requirements on the executing hardware.
SAR Sticker: An Adversarial Image Patch that can Deceive SAR ATR Deep Model

The use of deep learning technology in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has attracted significant attention. However, it has been noted that deep learning is vulnerable to adversarial attacks, and of course SAR ATR is no exception. Notably, several studies in the past two years have generated SAR adversarial examples, which can successfully deceive a specific well-trained SAR ATR deep network. Considering that research on optical adversarial attacks has progressed toward adversarial examples in the physical world, this paper aims to examine the feasibility of SAR adversarial examples in the physical world. Inspired by Google Sticker, we propose a SAR ATR adversarial-patch-based deception method, namely SAR sticker. The images crafted by our SAR sticker exhibit marked resemblance to their corresponding original images, yet they possess great potential in launching potent attacks on state-of-the-art SAR ATR models, achieving a 76% fooling rate.
Semantic Segmentation of Oil Well Sites Using Sentinel-2 Imagery

The number and geographical location of oil well sites can reflect the local oil production situation and there is a growing interest in automatically identifying oil well sites from remote sensing images. Traditionally, visual interpretation was employed to extract oil well sites locations from remotely sensing images. However, this approach is time-consuming and heavily dependent on domain experts. Advancements in remote sensing satellite technology and the widespread use of deep learning algorithms have enabled the automated extraction of oil well sites from remote sensing images. In this paper, we established the Northeast Petroleum University Oil Well Sites Dataset Version 1.0 (NEPU-OWS V1.0), and to evaluate its usability by comparing several different deep learning models based on semantic segmentation algorithms for optical remote sensing images. Experimental results show that current advanced deep learning models achieve high accuracy on this dataset, demonstrating great potential for remote sensing detection in oil well sites.
Lithotype Classification in Geothemal Area by the Use of Hyperspectral Data

This work aims to characterize the surface of an Italian geothermal field, Parco Naturalistico delle Biancane (PNB), by using hyperspectral data and define the main diagnostic spectral features of lithotypes affected by mineral alteration due to geothermal activity. Hyperspectral data acquired by PRISMA (Hyperspectral Precursor of the Application Mission) and AVIRIS-NG (Airborne Visible / Infrared Imaging Spectrometer – Next Generation), coupled with a spectral library of the main lithotype of the area, represent the dataset used for the analysis. All the spectral data cover the VNIR (Visible and Near InfraRed) and SWIR (Short-Wave InfraRed) spectral range. The Material Identification and Characterization Algorithm (MICA) has been used to perform the comparison between the spectral features from the spectral library and the PRISMA and AVIRIS hyperspectral images in order to obtain an automatic lithotype classification map.
Target Partial-Occlusion: An Adversarial Examples Generation Approach Against SAR Target Recognition Networks

Synthetic aperture radar (SAR) target recognition networks performance has been remarkably improved, posing serious exposure risks to our high-value targets. Researches have shown that it is valid to protect our high-value targets by generating adversarial examples. However, most existing SAR adversarial examples generation approaches are based on the premise that irregularly global perturbation data can be directly added to SAR images, which is difficult to implement in practice. To this end, a target partial-occlusion SAR adversarial examples generation approach is proposed in this paper. First, the target region in SAR image is extracted using the combination of OTSU algorithm and morphology operations. Then, the random search (RS) algorithm is introduced to optimize the occlusion position in the extracted target region with the constraint of occlusion area and value, so as to misclassify the SAR target recognition networks. Experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown the effectiveness of the proposed method.
A Systematic Spatio-Temporal Analysis of Relevant Meteorological Variables for Solar Irradiance Forecasting

Over the past few years, photovoltaic systems are replacing traditional sources of energy. These photovoltaic systems are powered by solar energy, which depends on the amount of incident solar irradiance. However, solar irradiance has a large amount of variability due to various spatio-temporal factors including region, cloud cover, humidity, and rainfall. In this paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , we identify the most relevant spatio-temporal features for estimating solar irradiance. Such an accurate identification of weather variables is useful for developing accurate data-driven solar irradiance estimation models. This leads to an improved forecasting performance, thus creating a stable photovoltaic system. We base our analysis on the dataset of three countries, Ireland, Singapore and India. We identify the cloud type as the most relevant weather variables in all regions. The other relevant features holding differential importance across regions include time, relative humidity, solar zenith angle, and pressure.