Reviewerly/exHarmony huggingface dataset:
Design of remote inspection system for high-level canal of hydropower station based on cluster centralized control of quadrotor UAV

With the gradual popularization of smart power plants, the demand for comprehensive monitoring and perception of power plant production is gradually increasing through advanced sensor measuring devices and network communication technology. Among all kinds of UAV (unmanned aerial vehicle), quadrotor helicopter is an excellent tool to perform this task because of its unique characteristics of landing/taking off without runway, hovering and free flight, easy to carry and portability. In this paper, a remote inspection system for high-level canals of hydropower stations is designed, which is based on automatic hangar UAV and uses AI algorithm to automatically identify dangerous entities. The platform integrates interface display, user role authority management, inspection task flow, alarm flow, flood discharge inspection flow, inspection history review, intelligent analysis and other functions. And the algorithm development and design of flocking control for quadrotor aircraft group are realized from mathematical derivation. The test results show that the system can receive all the data in time, so that it can respond to possible equipment accidents in time, and the practical application effect is good. The reliability of this system is verified.
Semantic Segmentation for Remote Sensing Images Based on Swin-Transformer and Multiscale Feature Refinement

Thanks to the development of deep learning, semantic segmentation of remote sensing images has made great advances. However, because of the widespread complex background and variable imaging conditions, the feature consistency of the same classes and the variability of different classes are more difficult to capture, leading to the existing methods still have the space for improvement. Thus, we combine the local convolution operation and swin transformer to design a novel semantic segmentation method (STFR). The proposed STFR can improve the multiscale feature representation and capture the global context information, which consists of three steps. Firstly, the multiscale features of the image is extracted by swin transformer; then, a parallel structure is constructed based on depth-wise separable convolution to refine the deep semantic information; finally, the semantic segmentation result can be obtained. Experimental results on a public remote sensing semantic segmentation dataset indicate the the proposed STFR can obtain a great performance.
Simulation of Econometric Dynamic Simulation And Prediction Model Based on Particle Swarm Optimization Neural Network Algorithm

In real economic life, in addition to sample information, we may also have some prior information about the parameter vector of the model. Using this prior information can improve the efficiency and accuracy of parameter estimation. Econometric models have been widely used in forecasting and policy analysis of various economic variables. The prediction of the future is closely related to the policy evaluation, which is closely related to two aspects in a feedback system. Using the original prediction method, after completing the modeling work, the prediction value of the exogenous variable at a certain time point is taken as the known number and substituted into the model to calculate the value of the endogenous variable at that time point to obtain the prediction result. Therefore, this paper establishes an artificial neural network (ANN) model to predict the future economic situation by analyzing the relationship between GDP and its influencing factors. In the process of modeling, particle swarm optimization (PSO) algorithm is used to optimize the weights and thresholds of neural network model. Through the calculation of the relevant sample data, a satisfactory result is obtained, which shows that the method proposed in this paper is feasible.
First Results of Vessel Detection with Onboard Processing of Sentinel-2 Raw Data by Deep Learning

Nowadays, the use of Artificial Intelligence on board Earth Observation satellites is under investigation for applications having strict bandwidth and latency requirements, such as vessel detection. However, many of the on-ground current computing pipelines rely on data post-processing techniques whose applications onboard satellites are tricky because of their limited computing power. To enable the analysis and the research of lightweight onboard data processing techniques, we provide VDS2Raw, the first Sentinel-2 Raw dataset for vessel detection applications. Finally, we also compared different object detection Deep Learning techniques in terms of detection performance and inference time to perform a feasibility analysis of performing onboard vessel detection on raw multi-spectral data.
Efficient Semantic Image Synthesis with Normalization Layers and Depth Estimation

One of the most important tasks in image-to-image translation is semantic image synthesis. The success of Generative Adversarial Network (GAN) models for the synthesis of semantic images has increased, but the synthesized images produced by these models are of insufficient quality since they lack structural and spatial information. The unique approach we suggest in this paper is built on normalization layers and depth maps, which provide the textures and structure required for each object on the semantic map. First, we present a depth generator that generates a depth image map from the semantic image and then feeds the generated depth map into the normalization layers. Then, we develop a depth discriminator that distinguishes between the depth map of the real image and the depth-translated image generated by the global generator. We run our experiments on three benchmark datasets and achieve high robustness against state-of-the-art evaluation protocols used in semantic image synthesis tasks.
A Noise-Model-Free Hyperspectral Image Denoising Method Based on Diffusion Model

Hyperspectral Image (HSI) denoising is a crucial preprocessing step to ensure the accuracy of the subsequent HSI analysis and interpretation. Neural network methods recently achieve state-of-the-art performance in HSI denoising. Nevertheless, these methods are typically trained on specific noise models, which could limit their performance as noise models in HSI may vary across different spectral bands. To mitigate this problem, we introduce an HSI denoising method based on the Diffusion Model (DM) whose training process is independent of the noise model, noted as a noise-model-free method. In this method, we first introduce a DM for HSI by increasing the input and output dimensions to incorporate spectral and spatial information. We then derive a DM-based HSI denoising process for a common noise model. Moreover, to address the issue of overfitting during training, we have introduced a stochastic sampling method to more effectively balance the significance of both spatial and spectral information. Experimental results on in-distribution and out-of-distribution samples demonstrate the efficacy of our approach.
A Code-Switched Arabic-English Sentiment Analysis Approach Based on Deep-Learning

Sentiment analysis within Online Social Networks (OSNs) becomes a major challenge. Mainly, because of the large amount of data on social networks and the mix of different languages that can be used in these environments. Due to the lack of existing research and datasets on code-switched Arabic-English sentiment analysis, this study creates a new dataset collected from various online social networks. The data is subjected to several preprocessing steps before being analyzed using various deep learning architectures. Moreover, a thorough evaluation of the preprocessing techniques and the performance of the deep learning models have been conducted. This is achieved using comprehensive Twitter datasets in Arabic and English. The study reveals that individual deep learning architectures, specifically, Convolutional Neural Networks (CNNs), Bidirectional Long Short Term Memory (Bi-LSTM), and LSTM demonstrated superior results compared to their hybrid counterparts. Notably, CNN emerged as the most accurate model with an accuracy of 83%, followed closely by Bi-LSTM at 82.3%. Additionally, the paper presents the deployment of an automated machine learning tool, which is Auto-Keras. Furthermore, it designs an optimized sentiment analysis model that aims to enhance accuracy. This approach leads to an increase in the model accuracy to 86%.
Class-Incremental Learning for Remote Sensing Images Based on Knowledge Distillation

In real-world recognition of remote sensing (RS) targets, large amount of RS data is hard to be acquired at once, but arrives in batches, which means the constantly adaption of models for new data and new classes. However, training old and new data together from scratch has certain requirements on data storage space and retraining time, so incremental learning come to be desirable for future RS recognition systems. In this article, a class incremental learning method based on knowledge distillation is proposed for RS image classification. In order to better retain the knowledge of old tasks, a relation-based loss is used as a new matching manner in distillation loss, which frees the student model from the burden of matching the exact output of the teacher model. Experiment results based on UC Merced 21, NWPU-RESISC45 and plane objects of FAIR1M demonstrate the advantages of the proposed method.
LS-SEM-Katsev Analytical Modeling of Lidar Underwater Transfer

The modeling of the LiDAR underwater echo signal is mainly divided into analytical and statistical methods. In this paper, the radiation transfer equation of LiDAR underwater transfer is established according to the QSSA approximate analytical method theory. For Katsev, the Green function is directly used to solve the radiation transfer equation, and the spectral element method is proposed to calculate the LiDAR radiation flux in the radiation transfer equation. To verify the accuracy of the LS-SEM-Katsev analytical model, the simulation results of the LS-SEM-Katsev analytical model and Semi-MC statistical model are verified under three typical seawater optical parameters. The experimental results show that the decision coefficients of the three simulation models are all above 0.99, which is highly consistent. A comparison between the computational times of LS-SEM-Katsev and Semi-MC simulation models showed that the computational efficiency of the proposed analytical model was 8 orders of magnitude higher than that of the Semi-MC model.
Group-Wise Shuffle Attention R-CNN for Ship Detection in Dual-Polarization SAR Images

Ship detection in synthetic aperture radar (SAR) images is a hot pot. However, most existing convolution neural network (CNN)-based research is limited to single polarization ship detection and neglects the utilize of the rich polarization information to further improve detection performance. Thus, to address the problem, in this paper, a group-wise shuffle attention R-CNN (GWSA R-CNN) is proposed for ship detection in dual-polarization SAR images. Based on the raw Faster R-CNN, GWSA R-CNN embeds a group-wise shuffle attention module (GWSA module) in the detection subnetwork to capture enriched organic fusion polarization information. Finally, the experimental results on the dual-polarization SAR ship detection dataset (DSSDD) show the state-of-the-art (SOTA) performance of our GWSA R-CNN, outperforming than other 7 competitive models. Specifically, GWSA R-CNN surpasses the second-best model 1.82% average precision (AP).
Amodal Segmentation Considering Visible and Non-Visible Elements of Urban Surfaces

This study addresses the challenge of amodal segmentation in computer vision, a change in basic assumptions towards perceiving objects holistically, even when partially occluded, deviating from the traditional modal perspective that predominantly focuses on visible elements. Thus, we propose a new approach for the amodal segmentation of top-view aerial images, with particular attention to the first layer of elements, constituted by asphalt and natural soils, normally occluded by different objects (trees, buildings, and vehicles). This proposed methodology is data-centric, assigning weights to specific image sections and distinguishing non-visible elements. The best model used the U-Net architecture with Efficient-net-B7 as the backbone and can accurately classify occluded segments, achieving an Intersection over Union (IoU) greater than 80% for most classes. The developed method provides a basis for exploring amodal segmentation based on data-centric models, impacting our understanding of complex and occlusion-prone environments, such as urban environments.
Evaluation of a Meta-Transfer Approach for Few-Shot Remote Sensing Scene Classification

Large numbers of labeled data are necessary for the success of modern deep-learning techniques. Despite a large amount of satellite image data now available, their ground truth labels are inadequate due to the complexity of the real world. It is true for many remote sensing tasks including scene classification, target classification, and target detection. This study explores and evaluates a state-of-the-art pipeline that combines transfer learning and meta-learning methods with voluminous external data for few-shot remote sensing scene classification. The experimental findings demonstrate that using this pipeline, both in-domain and out-of-domain data can lead to equivalent performance as the base data during training. Additionally, the study explores the impact of various N-way-K-shot tasks in the meta-training stage and finds that the model trained with 5-way-5-shot tasks achieves the highest level of performance.
Text as a Richer Source of Supervision in Semantic Segmentation Tasks

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction with semantic embeddings provided by a language model. By leveraging contrastive learning approaches, we learn an alignment between the visual and the (fixed) textual representations. In addition to producing standard semantic segmentation outputs, our model enables interactive queries with RS images using natural language prompts. The experimental results obtained on 50cm resolution aerial data from Switzerland show that TACOSS performs similarly to a standard semantic segmentation model while allowing the flexible usage of in- and out-of-vocabulary terms for the interactions with the image.
Poissonian Hyperspectral Image Denoising without Using Anscombe Transform

In the most existing Hyperspectral Image (HSI) denoising methods, Poisson noise is first transformed into Gaussian noise through Anscombe transform and then remove it. However, transform errors may occur that affect the final denoising results when using Anscombe transform. In this paper, we propose a Poissonian hyperspectral image denoising method without using Anscombe transform (WUAT) to directly remove the noise of Poissonian HSI under the maximum a posteriori (MAP) model by finding the minimum value of the negative logarithmic Poisson log-likelihood combined with the total variation (TV). The experimental results show that the proposed method can acquire better performance than most state-of-the-art denoising methods.
Multi-key Homomorphic Encryption Algorithm Based on Fuzzy Clustering

Due to the rapid growth of internet technology, big data has been widely used, and the leakage of sensitive data has also occurred, and network security has gradually attracted people's attention. Homomorphic encryption algorithm is one of the important technologies for privacy protection of big data. Homomorphic encryption algorithm can process the ciphertext, and correctly decrypt the processed new text to recover the plaintext, which reduces the exposure frequency of sensitive information in the network. In this paper, a multi-key homomorphic encryption strategy based on fuzzy C-means clustering (FCM) algorithm is proposed. By introducing polynomials to calculate public key elements, the way of generating keys is changed, and the number of encryption parties is expanded. The simulation results show that the accuracy of this algorithm is above 90%, and the real-time wavelength tends to be stable. The result proves the correctness and feasibility of the new scheme. It can perform arbitrary operation on ciphertext without knowing the key, and the result of the operation is the same as that of the plaintext after decryption. Without the decryption key, neither the chip nor the hardware Trojan embedded in it can obtain the true meaning of the data, and the hardware Trojan cannot attack these data.
Scaling Automatic Vector Data Alignment to Satellite Imagery

Given the tremendous volume of accessible Earth Observation (EO) data, there is a need to develop scalable Geospatial Artificial Intelligence (GeoAI) solutions for time-sensitive applications. Scalability in this context refers to rapidly processing large-scale EO data using high performance computing resources. Accurate mapping of the built environment from remote sensing (RS) imagery has been one of the crucial components in GeoAI workflows for a wide spectrum of humanitarian applications. Derived vector data of built environment is often leveraged for disaster preparedness and response activities. However, factors such as differences in ortho-rectification, atmospheric conditions and human error, results in spatial misalignment between vector data and the timely available RS imagery. Model training for downstream tasks such as object detection, change analysis, etc., is negatively impacted due to such spatial misalignment. Although there has been progress towards automatic alignment of vector data, the lack of scalability remains an open research challenge. This paper proposes to leverage parallel computing to optimize an automatic vector data alignment workflow. It further employs CPU-level multi-core parallelism for improving the performance of the workflow for scalable built environment mapping. We report observations and discuss findings from the preliminary experiments performed on the Summit Supercomputer.
Credible Recognition of Radar Images: Interpretability Metric and Classification Score

Automatic target recognition (ATR) is one of the most demanding applications of synthetic aperture radar (SAR) in the field of radar reconnaissance and surveillance. Convolutional neural networks (CNNs) have been extensively employed for SAR-ATR and obtained remarkable accuracy. However, regarding the black-box nature and non-transparency in decision-making, CNN’s reliability is unsatisfactory. Recently, some progress has been made toward providing a visual explanation of CNN’s classification procedure. In this paper, we employ the Local Interpretable Model-agnostic Explanation (LIME) algorithm to propose an interpretability metric that can be helpful to evaluate the overall robustness of CNNs. Using the proposed framework, the user can infer what proportion of the results are based on target features, while the remainder is based on irrelevant correlations from the background clutter. The theoretical findings are validated by the public MSTAR database.
Spectral Evaluation of Hyperspectral Remote Sensing Data Using UAV-derived VIS/NIR/SWIR Imaging Data and Polygon Grids

In 2022, the hyperspectral mission EnMap was successfully brought into orbit with some delay, but is currently the only remote sensing mission providing hyperspectral image data in 30 m spatial resolution. More hyperspectral satellites with a similar spatial resolution are expected. Planet has announced the launch of two hyperspectral satellites for 2023. Without doubt, a new hyperspectral remote sensing area began asking for validation and calibration workflows. In this context, we propose to focus within the full range between 350 nm and 2,500 nm on selected 18 bands. These bands represent characteristic reflectance and absorption feature, especially of vegetation. In this study, we present the proposed workflow for one band (910 nm) which was obtained from a newly developed VIS/NIR/SWIR imaging system for UAVs. The UAV-acquired NIR data has a very high resolution (< 0.1 m) and was downscaled to 30 m using polygon grids and zonal statistics. The UAV data was then plotted against downscaled Planet SuperDoveSat and SkySat NIR bands. The SkySat data correspond well (R2 = 0,72) with the UAV NIR data while the Planet SuperDoveSat data performed moderate (0.57).
Generative Adversarial Networks for the Satellite Data Super Resolution Based on the Transformers with Attention

In recent years, free access to high and medium resolution data has become available, providing researchers with the opportunity to work with low resolution satellite images on a global scale. Sentinel-1 and Sentinel-2 are popular sources of information due to their high spectral and spatial resolution. To obtain a final product with a resolution of 10 meters, we have to use bands with a resolution of 10 meters. Other satellite data with lower resolution, such as Landsat-8 and Landsat-9, can improve the results of land monitoring, but their harmonization requires a process known as super-resolution. In this study, we propose a method for improving the resolution of low-resolution images using advanced deep learning techniques called Generative Adversarial Networks (GANs). The state-of-the-art neural networks, namely transformers, with the combination of channel attention and self-attention blocks were employed at the base of the GANs. Our experiments showed that this approach can effectively increase the resolution of Landsat satellite images and could be used for creating high resolution products.
Dehazing Method Based On Gaussian Weighted Image Fusion for Outdoor and Remote Sensing Images

Target detection based on outdoor and remote sensing (RS) images are used in many fields [1] , such as civil and military [2] , traffic surveillance [3] and disaster forecast [4] . However, in harsh environmental conditions, as air contaminants increase, light is scattered in the air and the image becomes hazy [5] . Therefore, image dehazing is an important issue in target detection for outdoor images and RS images. Therefore, image dehazing is an important issue in target detection for outdoor images and RS images. It has recently been proposed a variety of methods to dehaze images, which can be broadly categorized into three types: image enhancement-based, image restoration-based, and deep learning-based methods, respectively. Image enhancement-based dehazing methods highlight useful information without considering the cause of haze. Image restoration-based dehazing methods analyze the effect of atmospheric scattering model (ASM) on the image. Deep learning-based dehazing methods learn the intrinsic relationships between images and obtain clear images by training.
Integration of Vehicle Target Detection and Recognition in Large-Scene SAR Images Based on YOLOv5

In response to the problems existing in traditional SAR image target detection methods, such as complicated processes, long detection times, and poor detection effects in complex backgrounds, this paper proposes an integration of detection and recognition method based on deep learning for large-scene SAR images with vehicle targets. The paper introduces the issues of target identification through three stages of target detection, identification, and classification in traditional methods. To address these problems, this paper introduces a one-stage detection network based on YOLOv5 to construct a SAR image vehicle target detection and recognition algorithm. To verify the performance of the algorithm, this paper generated a dataset containing 10 different vehicle targets in large-scene SAR images and applied it to experiments. The results demonstrate that the algorithm has good performance and fast detection speed. The research results of this paper can provide important references for large-scale SAR image target detection.