Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition

Vehicle recognition is a fundamental problem in SAR image interpretation. However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations. Additionally, the lack of large datasets further complicates the task. Inspired by the analysis of target signature variations and deep learning explainability, this paper proposes a novel domain alignment framework named the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve robustness under various operating conditions. Concisely, HDANet integrates feature disentanglement and alignment into a unified framework with three modules: domain data generation, multitask-assisted mask disentanglement, and domain alignment of target features. The first module generates diverse data for alignment, and three simple but effective data augmentation methods are designed to simulate target signature variations. The second module disentangles the target features from background clutter using the multitask-assisted mask to prevent clutter from interfering with subsequent alignment. The third module employs a contrastive loss for domain alignment to extract robust target features from generated diverse data and disentangled features. Lastly, the proposed method demonstrates impressive robustness across nine operating conditions in the MSTAR dataset, and extensive qualitative and quantitative analyses validate the effectiveness of our framework

Monaural speech separation with deep learning using phase modelling and capsule networks

The removal of background noise from speech audio is a problem with high practical relevance. A variety of deep learning approaches have been applied to it in recent years, most of which operate on a magnitude spectrogram representation of a noisy recording to estimate the isolated speaking voice. This work investigates ways to include phase information, which is commonly discarded, firstly within a convolutional neural network (CNN) architecture, and secondly by applying capsule networks, to our knowledge the first time capsules have been used in source separation. We present a Circular Loss function, which takes into account the periodic nature of phase. Our results show that the inclusion of phase information leads to an improvement in the quality of speech separation. We also find that in our experiments convolutional neural networks outperform capsule networks at speech separation

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Primjena neuronskih mreža za otkrivanje i klasifikaciju topničkih ciljeva

Neural networks have been in use since the 1950s and are increasingly prevalent in various domains of human activity, including military applications. Notably, GoogLeNet and convolutional neural networks, when appropriately trained, are instrumental in identifying and detecting individual objects within a complex set. In military scenarios, neural networks play a crucial role in the fire support process, especially when receiving target descriptions from forward observers. These networks are trained on image datasets to recognize specific features of individual elements or military objects, such as vehicles. As a result of this training, when presented with a new image, the network can accurately determine the type of vehicle, expediting the targeting process and enhancing the ability to provide a suitable response. This paper describes the application of neural networks for detecting and classifying artillery targets. It presents a specific problem and proposes a scientific solution, including explaining the methodology used and the results obtained.Neuronske mreže u uporabi su od pedesetih godina prošlog stoljeća i sve su zastupljenije u različitim područjima ljudske aktivnosti, uključujući vojne primjene. Posebno, GoogleNet i konvolucijske mreže, kada su odgovarajuće utrenirane, ključne su u prepoznavanju i otkrivanju pojedinih objekata unutar složenog skupa. U vojnim scenarijima neuronske mreže imaju ključnu ulogu u postupku pružanja potpore vatrom, posebno kada primaju opise ciljeva od prednjih topničkih motritelja. Ove mreže trenirane su na slikovnim skupovima podataka kako bi prepoznale specifičnosti pojedinih elemenata ili vojnih objekata, kao što su vozila. Kao rezultat treniranja, kada se prikaže nova slika, mreža može točno odrediti tip vozila, ubrzati postupak ciljanja i poboljšati sposobnost pružanja prikladnog odgovora. U radu se opisuje primjena neuronskih mreža za otkrivanje i klasifikaciju topničkih ciljeva. Rad predstavlja poseban problem i predlaže rješenje primjenom znanstvenog pristupa, uključujući objašnjenje korištene metodologije i dobivene rezultate

Analysis of Deep Neural Networks for Military Target Classification using Synthetic Aperture Radar Images

Target detection and classification in the military is an area that is very significant in modern battlefields. Using Synthetic Aperture Radar images for classifying targets adds to its significance, as these images are high-resolution images of the surface of the earth created using microwave radiation and they can be used anytime, anywhere, and in any weather conditions. A target classification system using deep learning to classify military vehicles from Synthetic Aperture Radar images is proposed in this study. The system uses a baseline Convolutional Neural Network to classify the images of military vehicles from the MSTAR dataset, achieving a baseline accuracy of 90%. Further transfer learning was applied to the system by using 5 different pre-trained networks, namely the InceptionV3, VGG16, VGG19, ResNet50, and MobileNet. These models were analysed and evaluated using 3 different evaluation metrics, the Confusion matrix, Classification report, and Mean Average Precision to discover the most accurate and efficient model for this task. The models VGG16 and MobileNet displayed the best performance on the dataset achieving accuracies of 98% and 97%, respectively. The ResNet50 model displayed the worst performance among the models, achieving an accuracy of 82%. While the other models, InceptionV3 and VGG19, achieved accuracies of 92% and 96% respectively

Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that occurred in November 2000 in Gloucester, UK. Experimental results show that the domain adaptation-based approach, semi-supervised domain adaptation (SSDA) with 20 labeled data samples, achieved slightly better values of the area under the precision-recall (PR) curve (AUC) of 0.9173 and F1 score of 0.8846 than those by traditional machine approaches. However, SSDA required much less labor for ground-truth labeling and should be recommended in practice

Causal SAR ATR with Limited Data via Dual Invariance

Synthetic aperture radar automatic target recognition (SAR ATR) with limited data has recently been a hot research topic to enhance weak generalization. Despite many excellent methods being proposed, a fundamental theory is lacked to explain what problem the limited SAR data causes, leading to weak generalization of ATR. In this paper, we establish a causal ATR model demonstrating that noise $N$ that could be blocked with ample SAR data, becomes a confounder with limited data for recognition. As a result, it has a detrimental causal effect damaging the efficacy of feature $X$ extracted from SAR images, leading to weak generalization of SAR ATR with limited data. The effect of $N$ on feature can be estimated and eliminated by using backdoor adjustment to pursue the direct causality between $X$ and the predicted class $Y$. However, it is difficult for SAR images to precisely estimate and eliminated the effect of $N$ on $X$. The limited SAR data scarcely powers the majority of existing optimization losses based on empirical risk minimization (ERM), thus making it difficult to effectively eliminate $N$'s effect. To tackle with difficult estimation and elimination of $N$'s effect, we propose a dual invariance comprising the inner-class invariant proxy and the noise-invariance loss. Motivated by tackling change with invariance, the inner-class invariant proxy facilitates precise estimation of $N$'s effect on $X$ by obtaining accurate invariant features for each class with the limited data. The noise-invariance loss transitions the ERM's data quantity necessity into a need for noise environment annotations, effectively eliminating $N$'s effect on $X$ by cleverly applying the previous $N$'s estimation as the noise environment annotations. Experiments on three benchmark datasets indicate that the proposed method achieves superior performance