Reviewerly/exHarmony huggingface dataset:
Overcoming Language Bias in Remote Sensing Visual Question Answering Via Adversarial Training

The Visual Question Answering (VQA) system offers a user-friendly interface and enables human-computer interaction. However, VQA models commonly face the challenge of language bias, resulting from the learned superficial correlation between questions and answers. To address this issue, in this study, we present a novel framework to reduce the language bias of the VQA for remote sensing data (RSVQA). Specifically, we add an adversarial branch to the original VQA framework. Based on the adversarial branch, we introduce two regularizers to constrain the training process against language bias. Furthermore, to evaluate the performance in terms of language bias, we propose a new metric that combines standard accuracy with the performance drop when incorporating question and random image information. Experimental results demonstrate the effectiveness of our method. We believe that our method can shed light on future work for reducing language bias on the RSVQA task.
RotPointNet: Keypoint based Oriented Object Detection for Aerial Image

Remote sensing object detection is characterized by arbitrary direction, dense targets and variable scale. Like common object detection methods predict horizontal rectangle boxes directly, most of the existing remote sensing oriented object detection methods predict oriented rectangle boxes directly. i.e. Center point position, length, width and rotation angle of the oriented rectangle boxes. These models often need to design complex rotation detection module to adapt to the rotating characteristics of targets, they lack good performance on targets with large aspect ratio changes as well. In this paper, we propose a keypoint based oriented object detection method for aerial images named RotPointNet to improve detection accuracy of rotating targets with large aspect ratio change. RotPointNet first locates the two endpoints of rotating targets using keypoint detection method, and then constructs the remote sensing oriented target based on the endpoints and additional width information. In addition, a new method of matching target key points is proposed in this paper. We conduct experiments to prove, the target detection model RotPoint-Net and key point matching strategy proposed in this paper can achieve good detection results on remote sensing images, especially for the detection of targets with large aspect ratio changes.
Multi-Frame Matching Consistency Decision of SAR Scene Based on SVM_Bagging

Multi-frame matching decision is a key step of high-precision scene matching navigation. The traditional decision algorithms mainly adopts hierarchical filtering method. When dealing with multi-frame sequences with low matching quality, it is hard to meet the precision requirements. In view of this problem, an intelligent multi-frame matching consistency decision algorithm based on SVM_bagging is proposed. Firstly, the features of correlation plane formed by matching are split and combined to form a variety of training datasets and train multiple independent support vector machine (SVM) models based on the bagging idea of ensemble learning. Then, a hard voting mechanism is adopted to combine the decision results of all SVM models for consistency decision. The experimental results on Sentinel_1-2 optical/SAR data show that the proposed algorithm can further reduce the false positive rate and false negative rate by more than 60% on the basis of the traditional decision algorithm.
Contextual Adversarial Attack Against Aerial Detection in The Physical World

Deep Neural Networks (DNNs) have been extensively utilized in aerial detection. However, DNNs are susceptible and vulnerable to adversarial examples Recently, physical attacks have gradually garnered attention due to their effectiveness and practicality, which pose great threats to some security-critical applications. In this paper, we take the first attempt to perform physical attacks in contextual form against aerial detection in the physical world. We propose an innovative contextual attack method against aerial detection in real scenarios, which achieves powerful attack performance and transfers well between various aerial object detectors without smearing or blocking the interested objects. Based on the findings that the targets’ contextual information plays an important role in aerial detection by observing the detectors’ attention maps, we fully use the contextual feature of the interested targets to elaborate background perturbations for the uncovered attacks in physical scenarios. Experiments with proportional scaling are conducted to evaluate the effectiveness of the proposed method, demonstrating its superiority in terms of both attack efficacy and physical practicality.
ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%.
Multimodal Arabic Retrieval and Recommendation System

Choosing desired videos from an extensive collection based on features extracted from the videos is known as content based video retrieval (CBVR). The extracted features are then indexed, categorized, and used to retrieve relevant and desired videos while excluding irrelevant ones. Videos can be represented by the audio, texts, faces, and objects in their frames. Some aspects could be improved regarding retrieval systems. One of these aspects is the need for an Arabic retrieval system. This study presents an advanced Arabic multimodal video retrieval system. The retrieval system uses the Apache Solr search engine to retrieve the videos using a text query. State-of-the-art techniques are used to retrieve videos using images and audio. The system presents a Mean Average Precision at 1 (MAP@1) score of 0.9333 and a MAP@3 0.8129 in the context of audio-based retrieval. Similarly, for image-based retrieval, the scores are 0.9800 and 0.8666 for the MAP@1 and MAP@3, respectively. Additionally, a novel hybrid recommendation system is presented, which combines content-based and collaborative filtering. The proposed model exhibits robust performance on several key metrics. With a MAP@10 of 0.3246 and a Normalized Discounted Cumulative Gain at 10 (NDCG@10) score of 0.4939. The model's Item Coverage score of 0.6905 for the top 100 recommendations, showcases its capacity to offer a broad range of recommendations. The hybrid system also solves the popularity bias and the ‘new item’ startup problem in collaborative filtering. Popularity-based recommendations are used as a fallback to handle the ‘new user’ startup problem in both collaborative filtering and content-based systems.
ALNASIKH: An Arabic OCR System Based on Transformers

Recognizing Arabic text from images is a challenging task due to the intricate nature of the Arabic script, which incorporates a large number of characters and intricate diacrit-ical marks. The unique characteristics of the script, including ligatures and overlapping characters, add complexity to the OCR process. To address these challenges, we propose utilizing the TrOCR architecture, which employs deep learning techniques, specifically transformers, for text recognition tasks and has shown superior performance in text recognition tasks. In this research, we evaluate the effectiveness of the TrOCR model architecture for recognizing Arabic text by proposing a setup for its encoder and decoder tailored specifically for Arabic script. Our proposed setup takes into account the unique characteristics and complexities of Arabic text, allowing the TrOCR model to effectively capture and interpret the intricacies of the script. Through extensive experimentation and evaluation, our results demonstrate the efficacy of the proposed approach. When applied to the task of recognizing text from scanned Arabic documents at the word-level, our approach achieves remarkable accuracy. The average character error rate (CER) is measured at 0.8, indicating a high level of precision in recognizing individual characters. Similarly, the word error rate (WER) is measured at 2.3, indicating accurate transcription of complete words. These results highlight the potential of the TrOCR model architecture, in conjunction with our proposed setup, to significantly improve the accuracy of OCR systems for Arabic text recognition.
Master GAN: Multiple Attention is all you Need: A Multiple Attention Guided Super Resolution Network for Dems

The task of transforming low-resolution remote sensing images to high-resolution has consistently presented a formidable challenge in the field. The use of Generative Adversarial Networks (GANs) has led to tremendous development in the field. In this study, a novel super resolution architecture Multiple Attention Swin Transformer Enhanced Residual GAN (MASTER GAN) has been introduced, that uses multiple attention techniques in a neural network trained in an adversarial training environment. The introduced MASTER GAN acheives state-of-the-art results in super resolution tasks, when compared to existing mechanism. The paper also introduces an open source database of low resolution and counter high resolution imagery, generated using Kernel GAN. The training code has been provided at: https://github.com/sheikhazhanmohammed/MASTERGAN.git
Features Matter: Cross-Domain Mutual Centralized Learning for Few Shot Remote Sensing Object Detection

We propose a novel approach for few-shot object detection by integrating weight generation and a feature memory library. To overcome the challenge of limited labeled data, our method assigns adaptive weights to the support set during training, focusing on informative samples and enabling better adaptation to unseen classes. Additionally, a feature memory library facilitates knowledge transfer by allowing the model to access and compare feature embeddings across different categories. Our approach achieves state-of-the-art results on few-shot detection benchmarks, outperforming existing methods in accuracy and robustness. Experimental results demonstrate the effectiveness of weight generation and the feature memory library in handling the few-shot detection scenario. Furthermore, our method exhibits strong generalization capabilities, adapting well to new object categories with limited labeled data. This framework provides valuable insights for remote sensing applications with limited labeled data, advancing the field of few-shot object detection.
Hydrothermal Alteration and Lithological Mapping Using Landsat-9 and Aster Remote Sensing Spectral Imagery in the Northeast Mojave Desert, California

The northeast Mojave Desert in California has a high potential for rare earth element (REE) mining because of its unique geodynamic circumstances and the presence of vast carbonate platforms. This region hosts the Sulfide Queen rare-earth element mine at Mountain Pass, California, which is the only significant producer of REE ore in the United States. Evaluation of the Landsat-9 Operational Land Imager (OLI-2) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image of the northeast Mojave Desert in California indicates that several important lithologic groups and hydrothermal alteration zones can be mapped in areas with good exposure by using image-based techniques. Image processing methods, including band ratios (BR) and minimum noise fraction (MNF) transform, were used to generate thematic layers of each satellite's data. The results of this investigation demonstrate the capability of these approaches in distinguishing various lithological units from the multispectral satellite datasets.
Scene-Level Matching Between Remote Sensing Optical and SAR Images

Due to the relative complementarity between optical images and Synthetic Aperture Radar (SAR) images, the method of SAR-optical matching is widely used in auxiliary navigation, disaster monitoring, rescue and other fields. However, there are huge geometric and radiometric differences between SAR and optical images, which pose serious challenges for multi-modal image matching. To solve this problem, this paper proposes a Refined Subdivision Processing Network (RSP-Net) for SAR-optical matching. Firstly, to extract representative features of images, we propose to employ the pseudo-Siamese network structure with dual-branch partial weight sharing in RSPNet. Then, to preserve the detailed information in the image, the method of subdividing the features to generate part-level features of the image is proposed. Finally, to remove modality-specific but task-independent information, part-level features are refined using information bottleneck methods. Experiments show that our proposed method has an excellent performance in the Scene-level matching between optical and SAR images.
Ship Detection in Complex Scenes Considering Both Global and Local Information Perception for SAR Images

In the problem of ship detection in complex scenes, in addition to the characteristics of ship targets, there is rich semantic information in the global and local background of the whole scenes, which provides more valuable inference information for ship detection. Therefore, in this paper, we propose a ship detection method in complex scenes considering both global and local information perception for SAR images. Firstly, the proposed method detects the globally stable region and the locally significant region respectively, and then designs a judgment method combining the two to eliminate false alarms, so as to ensure that the detected target has both globally stable characteristics and locally significant characteristics. The detection performance of the proposed method is verified by the spaceborne SAR images covering the coastal areas. The result shows that the proposed method can effectively detect ships in complex scenes, especially eliminating most false alarms in land areas.
Enhancing Extractive Text Summarization of Long Documents Using Features Dimensionality Reduction

Automatic Text Summarization (ATS) task is considered one of the most challenging tasks in Natural Language Processing field, especially for long documents. There are many techniques for ATS, including Artificial Intelligence (AI) approaches, statistical-based approaches, graph-based approaches, etc., but the generated summaries are still less accurate than human summaries. This research presents an extractive Neural-based model to summarize long documents using both attentive global and local contexts, Word2Ve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> embeddings, and a bidirectional Gated Recurrent Unit (GRU) as a Sentence Encoder. The proposed model uses a Multi-Layer Perceptron (MLP) that consists of two linear layers with Rectified Linear Activation Units (RELUs) to reduce features dimensionality. The proposed model is evaluated on PubMed and arXiv datasets using ROUGE metrics, and the evaluation results show remarkable and promising improvements for ROUGE-1, ROUGE-2, and ROUGE-L.
Design of Adaptive Learning Quantitative Training Algorithm Based on Deep Neural Network Algorithm

In the differentiated machine learning method, the number of constraints imposed by the training sample on the parameters is equal to the number of bits required for it to specify the label. Labels usually contain very little bit information, and if the number of parameters exceeds the number of training samples, it will usually lead to serious over-fitting. In this paper, the adaptive learning quantization training algorithm based on DNN (Deep Neural Network) algorithm is designed. For our algorithm, first, we train the floating-point model on the training set. When the floating-point model meets the precision requirement, we regard the floating-point model weight as the pre-training model of quantization convolution training, and train on the same training set. Finally, the quantization convolution model is used as a pre-training model to train the quantization activation layer. The results show that the accuracy of the proposed quantization model is 94.359% on the CIFAR-10 data set and 81.204% on the CIFAR-100 data set. The parameter of the quantization model in this paper is 6.159 MB. Experiments show that the algorithm proposed in this paper can achieve real-time reasoning performance on edge embedded devices with extremely limited computing and memory resources.
Despeckling of Multifrequency SAR Data Using Electromagnetic Scattering Models

Interpretation and processing of synthetic aperture radar (SAR) imagery is negatively affected by speckle noise, that can be faced by proper despeckling pre-processing. In this work, we propose a simple approach for the despeckling of multi-frequency SAR data under the hypotheses that they are relevant to bare soil surfaces and that are acquired by the same sensor, e.g., AIRSAR. The proposed approach is based on a frequency-compensation step, where the dependence of the signal strength upon operating frequency is compensated by means of surface scattering model suited to natural surfaces. Once such a pre-processing step is carried out, any despeckling filter suited to SAR time series filtering can be applied. Quantitative indicators evaluated on both simulated and actual SAR data reveal the benefits of the proposed compensation step w.r.t. pure multitemporal and single-channel filtering.
SCV-UNet: Saliency-Combined Complex-Valued U-Net for SAR Ship Target Segmentation

Since synthetic aperture radar (SAR) can observe all-weather, it is widely used in ship target detection and segmentation tasks. However, SAR images have complex backgrounds and clutter interference, which affect the segmentation accuracy. This paper proposes a saliency-combined complex-valued U-Net. The network consists of two parts, namely complex-valued U-Net(CV-UNet) and original U-Net. The CV-UNet is used to process the measured data of SAR images which contains amplitude and phase information. The original U-Net is used to process the saliency map generated by the SAR image, and the results of two parts of the network output are connected. The experiment uses the measured data of HISEA-1 to make a target segmentation dataset, and uses the trained network for testing. The results show that the performance of the proposed method is better than that of the original U-Net and CV-UNet.
Unsupervised Domain Adaption for Remote Sensing Semantic Segmentation with Self-Attention Mechanism

The domain shift between the source and target domains limits the performance of traditional convolutional neural networks (CNNs) for feature extraction in remote sensing tasks. We propose an image translation network that uses generative adversarial networks (GANs) to transfer spectral distributions from training to test data, enhancing cross-domain semantic segmentation. Our approach fine-tunes the DeepLab-V3 framework on synthetic training data generated by the proposed network. Experimental results show improved performance in cross-domain semantic segmentation tasks for remote sensing images.
Design of Robot Path Planning Control Algorithm Based on Wavelet Neural Network

At present, there are many optimization algorithms in the research of path planning for mobile robots, but all of them have certain limitations. How to make mobile robots make full use of existing constraints and choose the appropriate algorithm for path planning is an important research topic. In this paper, a robot path planning control algorithm based on WNN (Waveform Neural Network) is proposed. The algorithm describes the dynamic changing environment of the robot by describing the topographic map of the neural network, and can find out the optimal traveling route of the robot in real time only by describing the topographic map of the neural network, without any previous information of the dynamic environment. Its more unique feature is that although it is based on neural network algorithm, it does not need self-learning process. The simulation results show that the convergence speed of the improved algorithm is obviously faster than that of the original algorithm, and the shortest path is broken and collision-free. The simulation experiment shows that the overall path planning scheme is feasible and effective, and has certain practical value.
Benchmarking Adversarial Attacks and Defenses in Remote Sensing Images

Deep neural networks are prone to being fooled by adversarial examples, which are created by adding imperceptible perturbations to deceive the classifier and induce incorrect predictions. Several defense methods have been proposed to address this issue. However, little attention has been given to the application of these methods in the field of remote sensing image (RSI) area. In this research, we have developed a comprehensive open-source benchmark that aims to evaluate the effectiveness of adversarial attacks and defenses in the context of RSI scene classification <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Extensive experiments have been conducted on both SAR and optical image datasets to thoroughly analyze white-box attacks, black-box attacks, and defense scenarios. We anticipate that our benchmark will assist researchers in constructing deep neural networks with enhanced resistance against adversarial attacks in the RSI field.
DNN Acceleration: A High-Accuracy Implementation for Base-2 Softmax Layer

This paper introduces a high-accuracy implementation of the softmax layer in Deep Neural Networks (DNN) used in multi-category classification applications. Calculation of the exponentials or logarithms in the traditional base-e softmax is mathematically complex, which makes it hard to have a high-accuracy hardware implementation without being resource-consuming, so instead of using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$e$</tex> as the exponential base, this paper presents a hardware implementation of the base-2 softmax function that makes use of 2 as the exponential base. Thus, the complex operations in base-e softmax will be replaced by simple shift and addition operations, with simpler LUTs and higher accuracy. The implemented hardware model relies on single-precision floating-point arithmetic cores, it achieves classification accuracy equal to 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> relative to a reference software model and has an area of 0.0802 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with a power consumption of 8.93 mW when synthesized under TSMC 28nm CMOS technology at the frequency of 1 GHz.