Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentation

eXplainable Artificial Intelligence (XAI) has emerged as an essential requirement when dealing with mission-critical applications, ensuring transparency and interpretability of the employed black box AI models. The significance of XAI spans various domains, from healthcare to finance, where understanding the decision-making process of deep learning algorithms is essential. Most AI-based computer vision models are often black boxes; hence, providing explainability of deep neural networks in image processing is crucial for their wide adoption and deployment in medical image analysis, autonomous driving, and remote sensing applications. Recently, several XAI methods for image classification tasks have been introduced. On the contrary, image segmentation has received comparatively less attention in the context of explainability, although it is a fundamental task in computer vision applications, especially in remote sensing. Only some research proposes gradient-based XAI algorithms for image segmentation. This paper adapts the recent gradient-free Sobol XAI method for semantic segmentation. To measure the performance of the Sobol method for segmentation, we propose a quantitative XAI evaluation method based on a learnable noise model. The main objective of this model is to induce noise on the explanation maps, where higher induced noise signifies low accuracy and vice versa. A benchmark analysis is conducted to evaluate and compare performance of three XAI methods, including Seg-Grad-CAM, Seg-Grad-CAM++ and Seg-Sobol using the proposed noise-based evaluation technique. This constitutes the first attempt to run and evaluate XAI methods using high-resolution satellite images

Extending CAM-based XAI methods for Remote Sensing Imagery Segmentation

Current AI-based methods do not provide comprehensible physical interpretations of the utilized data, extracted features, and predictions/inference operations. As a result, deep learning models trained using high-resolution satellite imagery lack transparency and explainability and can be merely seen as a black box, which limits their wide-level adoption. Experts need help understanding the complex behavior of AI models and the underlying decision-making process. The explainable artificial intelligence (XAI) field is an emerging field providing means for robust, practical, and trustworthy deployment of AI models. Several XAI techniques have been proposed for image classification tasks, whereas the interpretation of image segmentation remains largely unexplored. This paper offers to bridge this gap by adapting the recent XAI classification algorithms and making them usable for muti-class image segmentation, where we mainly focus on buildings' segmentation from high-resolution satellite images. To benchmark and compare the performance of the proposed approaches, we introduce a new XAI evaluation methodology and metric based on "Entropy" to measure the model uncertainty. Conventional XAI evaluation methods rely mainly on feeding area-of-interest regions from the image back to the pre-trained (utility) model and then calculating the average change in the probability of the target class. Those evaluation metrics lack the needed robustness, and we show that using Entropy to monitor the model uncertainty in segmenting the pixels within the target class is more suitable. We hope this work will pave the way for additional XAI research for image segmentation and applications in the remote sensing discipline

advanced linear and deep learning based channel estimation techniques in doubly dispersive environments

La révolution des communications sans fil joue un rôle important dans la facilitation de plusieurs applications mobiles telles que les drones, les trains à grande vitesse et les communications entre véhicules. En particulier, le concept de véhicules connectés apporte une bonne connectivité aux véhicules. En plus des nouvelles technologies de calcul et de détection embarquées, les réseaux de véhicules servent de catalyseur clé aux systèmes de transport intelligents et aux villes intelligentes. Cette nouvelle génération de réseaux a un impact profond sur la société, rendant chaque jour les déplacements plus sûrs, plus écologiques, plus efficaces et plus confortables. Cependant, dans les environnements véhiculaires, le milieu de propagation entre les nœuds du réseau varie fortement dans le temps, ce qui pose des problèmes de fiabilité. En fait, les signaux transmis se propagent sur plusieurs chemins, chacun avec un retard et une atténuation différente, en plus de l'effet de décalage Doppler résultant du mouvement des véhicules et de l'environnement. Il est très important de garantir la fiabilité de la communication au moyen d'une estimation précise des canaux dans de tels environnements. Par conséquent, la précision de l'estimation de canal influence les performances du système, car une réponse de canal estimée avec précision influence les opérations d'égalisation, de démodulation et de décodage au niveau du récepteur. Dans la littérature, il existe plusieurs travaux sur les méthodes classiques d'estimation du canal pour les communications véhiculaires. Cependant, ces estimateurs conventionnels reposent sur de nombreuses hypothèses qui limitent leurs performances dans les canaux hautement dynamiques variant dans le temps. De plus, les estimateurs linéaires conventionnels sont des solutions peu pratiques dans des scénarios de cas réels car ils reposent sur des modèles statistiques qui nécessitent une complexité élevée. Bien qu'il existe des estimateurs linéaires simples avec une complexité abordable, ils manquent de robustesse dans les environnements très dynamiques. Par conséquent, étudier des estimateurs avec un bon compromis complexité/performance est une problématique importante à investir. En tant qu'approche dominante de l'IA, l'apprentissage profond développe des méthodes efficaces pour analyser des données en apprenant efficacement les structures pour plusieurs problèmes rencontrés dans divers domaines scientifiques. La principale raison de l'intégration de l'apprentissage profond dans les communications sans fil est de trouver des solutions aux problèmes de communication lorsque les solutions analytiques sont insolubles ou très complexes. L'apprentissage profond a une forte capacité à relever ce défi grâce à des solutions peu complexes et robustes qui améliorent les performances des systèmes sans fil. De plus, le traitement distribué basé sur GPU permet l'utilisation de l'apprentissage profond dans des applications à temps réel. En conséquence, l'apprentissage automatique s’exploite pour les différentes données générées dans les réseaux véhiculaires. Dans ce contexte, cette thèse vise à étudier comment adapter de tels outils pour tenir compte des caractéristiques des réseaux de véhicules à haute mobilité. Nous montrons que l'intégration d'architectures optimisées d'apprentissage profond apporte des solutions de faible complexité pour l'estimation de canaux des réseaux véhiculaires soit en améliorant les performances par rapport aux estimateurs de canaux linéaires, soit en approchant les performances d'estimateurs robustes tout en réduisant la complexité d'implémentation. Par conséquent, contrairement aux estimateurs conventionnels, les estimateurs basés sur l'apprentissage profond offrent un bon compromis entre la complexité de calcul et les performances du système. De plus, la capacité de généralisation rend le système plus robuste surtout quand il est déployé dans des environnements hautement dynamiques.Wireless communications revolution plays a significant role in facilitating several mobile applications like unmanned aerial vehicles, high-speed railway, and vehicular communications. Particularly, the concept of connected vehicles brings a new level of connectivity to vehicles. Along with novel on board computing and sensing technologies, vehicular networks serve as a key enabler of intelligent transportation systems and smart cities. This new generation of networks have a profound impact on the society, making every day traveling safer, greener, and more efficient and comfortable. However, in vehicular environments, the propagation medium between the network nodes is highly time-varying leading to considerable reliability challenges. In fact, transmitted signals propagate through multiple paths, each with a different delay, attenuation, in addition to Doppler shift effect resulting from the motion of vehicles and the surrounding environment. Ensuring communication reliability by the means of accurate channel estimation in such environments is very important. Therefore, the accuracy of the channel estimation influences the system performance, since a precisely estimated channel response influences the follow-up equalization, demodulation, and decoding operations at the receiver. In literature, there exists an extensive work on conventional channel estimation for vehicular communications. However, these conventional estimators rely on many assumptions that limit their performance in highly dynamic time-varying channels. Moreover, linear conventional estimators are impractical solutions in real case scenarios as they rely on statistical models and require high implementation complexity. Although there exists simple linear estimation with affordable complexity, they lack robustness in highly dynamic environments. Therefore, investigating estimators with a good trade-off complexity vs. performance is a significant task. As a prevailing approach to AI, deep learning (DL) develops efficient methods to analyze data by finding patterns and learning underlying structures and represents an effective data driven approach to problems encountered in various scientific fields. The main reason behind integrating DL in wireless communications is to find solutions to communication problems where analytical solutions are intractable or highly complex. DL has a strong ability to overcome this challenge via low-complexity and robust solutions that improve the performance of wireless systems. Additionally, the GPU-based distributed processing enables the DL employment in real-time applications. As a result, DL can be leveraged to exploit the data generated in vehicular networks. In this context, this thesis aims to investigate how to adapt such tools to account for the characteristics of high mobility vehicular networks. We show that integrating optimized DL architectures brings low-complexity solutions for vehicular channel estimation either by improving the performance compared to the simplified linear channel estimators, or by approaching the performance of complex robust model-based estimators with feasible implementation. Therefore, unlike conventional estimators, DL-based estimators provide a good trade-off between the computational complexity and the system performance. Moreover, the generalization ability gives robustness to the system when deployed in highly dynamic environments

Techniques avancées d'estimation linéaire et d'apprentissage profond du canal dans des environnements doublement dispersifs

Wireless communications revolution plays a significant role in facilitating severalmobile applications like unmanned aerial vehicles, high-speed railway, and vehicularcommunications. Particularly, the concept of connected vehicles brings a new level ofconnectivity to vehicles. Along with novel on board computing and sensing technologies,vehicular networks serve as a key enabler of intelligent transportation systems andsmart cities. This new generation of networks have a profound impact on the society,making every day traveling safer, greener, and more efficient and comfortable. However,in vehicular environments, the propagation medium between the network nodes ishighly time-varying leading to considerable reliability challenges. In fact, transmittedsignals propagate through multiple paths, each with a different delay, attenuation, inaddition to Doppler shift effect resulting from the motion of vehicles and the surroundingenvironment. Ensuring communication reliability by the means of accurate channelestimation in such environments is very important. Therefore, the accuracy of thechannel estimation influences the system performance, since a precisely estimatedchannel response influences the follow-up equalization, demodulation, and decodingoperations at the receiver. In literature, there exists an extensive work on conventionalchannel estimation for vehicular communications. However, these conventional estimatorsrely on many assumptions that limit their performance in highly dynamic time-varyingchannels. Moreover, linear conventional estimators are impractical solutions in real casescenarios as they rely on statistical models and require high implementation complexity.Although there exists simple linear estimation with affordable complexity, they lackrobustness in highly dynamic environments. Therefore, investigating estimators with agood trade-off complexity vs. performance is a significant task. As a prevailing approachto AI, deep learning (DL) develops efficient methods to analyze data by finding patternsand learning underlying structures and represents an effective data driven approach toproblems encountered in various scientific fields. The main reason behind integratingDL in wireless communications is to find solutions to communication problems whereanalytical solutions are intractable or highly complex. DL has a strong ability to overcomethis challenge via low-complexity and robust solutions that improve the performance ofwireless systems. Additionally, the GPU-based distributed processing enables the DLemployment in real-time applications. As a result, DL can be leveraged to exploit thedata generated in vehicular networks. In this context, this thesis aims to investigatehow to adapt such tools to account for the characteristics of high mobility vehicularnetworks. We show that integrating optimized DL architectures brings low-complexitysolutions for vehicular channel estimation either by improving the performance comparedto the simplified linear channel estimators, or by approaching the performance ofcomplex robust model-based estimators with feasible implementation. Therefore, unlikeconventional estimators, DL-based estimators provide a good trade-off between thecomputational complexity and the system performance. Moreover, the generalizationability gives robustness to the system when deployed in highly dynamic environments.La révolution des communications sans fil joue un rôle important dans la facilitationde plusieurs applications mobiles telles que les drones, les trains à grande vitesse et lescommunications entre véhicules. En particulier, le concept de véhicules connectés apporteune bonne connectivité aux véhicules. En plus des nouvelles technologies de calcul et dedétection embarquées, les réseaux de véhicules servent de catalyseur clé aux systèmesde transport intelligents et aux villes intelligentes. Cette nouvelle génération de réseauxa un impact profond sur la société, rendant chaque jour les déplacements plus sûrs,plus écologiques, plus efficaces et plus confortables. Cependant, dans les environnementsvéhiculaires, le milieu de propagation entre les nœuds du réseau varie fortement dans letemps, ce qui pose des problèmes de fiabilité. En fait, les signaux transmis se propagent surplusieurs chemins, chacun avec un retard et une atténuation différente, en plus de l’effet dedécalage Doppler résultant du mouvement des véhicules et de l’environnement. Il est trèsimportant de garantir la fiabilité de la communication au moyen d’une estimation précisedes canaux dans de tels environnements. Par conséquent, la précision de l’estimationde canal influence les performances du système, car une réponse de canal estimée avecprécision influence les opérations d’égalisation, de démodulation et de décodage au niveaudu récepteur. Dans la littérature, il existe plusieurs travaux sur les méthodes classiquesd’estimation du canal pour les communications véhiculaires. Cependant, ces estimateursconventionnels reposent sur de nombreuses hypothèses qui limitent leurs performancesdans les canaux hautement dynamiques variant dans le temps. De plus, les estimateurslinéaires conventionnels sont des solutions peu pratiques dans des scénarios de cas réelscar ils reposent sur des modèles statistiques qui nécessitent une complexité élevée.Bien qu’il existe des estimateurs linéaires simples avec une complexité abordable, ilsmanquent de robustesse dans les environnements très dynamiques. Par conséquent, étudierdes estimateurs avec un bon compromis complexité/performance est une problématiqueimportante à investir. En tant qu’approche dominante de l’IA, l’apprentissage profonddéveloppe des méthodes efficaces pour analyser des données en apprenant efficacementles structures pour plusieurs problèmes rencontrés dans divers domaines scientifiques.La principale raison de l’intégration de l’apprentissage profond dans les communicationssans fil est de trouver des solutions aux problèmes de communication lorsque les solutionsanalytiques sont insolubles ou très complexes. L’apprentissage profond a une forte capacitéà relever ce défi grâce à des solutions peu complexes et robustes qui améliorent lesperformances des systèmes sans fil. De plus, le traitement distribué basé sur GPUpermet l’utilisation de l’apprentissage profond dans des applications à temps réel. Enconséquence, l’apprentissage automatique s’exploite pour les différentes données généréesdans les réseaux véhiculaires. Dans ce contexte, cette thèse vise à étudier comment adapterde tels outils pour tenir compte des caractéristiques des réseaux de véhicules à hautemobilité. Nous montrons que l’intégration d’architectures optimisées d’apprentissageprofond apporte des solutions de faible complexité pour l’estimation de canaux desréseaux véhiculaires soit en améliorant les performances par rapport aux estimateursde canaux linéaires, soit en approchant les performances d’estimateurs robustes tout enréduisant la complexité d’implémentation. Par conséquent, contrairement aux estimateursconventionnels, les estimateurs basés sur l’apprentissage profond offrent un bon compromisentre la complexité de calcul et les performances du système. De plus, la capacitéde généralisation rend le système plus robuste surtout quand il est déployé dans desenvironnements hautement dynamiques

RNN Based Channel Estimation in Doubly Selective Environments

Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems. Due to the impact of multi-path propagation and Doppler interference in dynamic environments, doubly-selective channel estimation becomes challenging. Conventional symbol-by-symbol (SBS) and frame-by-frame (FBF) channel estimation schemes encounter performance degradation in high mobility scenarios due to the usage of limited training pilots. Recently, deep learning (DL) has been utilized for doubly-selective channel estimation, where long short-term memory (LSTM) and convolutional neural network (CNN) networks are employed in the SBS and FBF, respectively. However, their usage is not optimal, since LSTM suffers from long-term memory problem, whereas, CNN-based estimators require high complexity. For this purpose, we overcome these issues by proposing an optimized recurrent neural network (RNN)-based channel estimation schemes, where gated recurrent unit (GRU) and Bi-GRU units are used in SBS and FBF channel estimation, respectively. The proposed estimators are based on the average correlation of the channel in different mobility scenarios, where several performance-complexity trade-offs are provided. Moreover, the performance of several RNN networks is analyzed. The performance superiority of the proposed estimators against the recently proposed DL-based SBS and FBF estimators is demonstrated for different scenarios while recording a significant reduction in complexity.Comment: This paper has been submitted to the IEEE Transactions on Machine Learning in Communications and Networking (TMLCN). arXiv admin note: text overlap with arXiv:2305.0020

Trainable Noise Model as an Explainable Artificial Intelligence Evaluation Method: Application on Sobol for Remote Sensing Image Segmentation

eXplainable Artificial Intelligence (XAI) has emerged as an essential requirement when dealing with mission-critical applications, ensuring transparency and interpretability of the employed black box AI models. The significance of XAI spans various domains, from healthcare to finance, where understanding the decision-making process of deep learning algorithms is essential. Most AI-based computer vision models are often black boxes; hence, providing the explainability of deep neural networks in image processing is crucial for their wide adoption and deployment in medical image analysis, autonomous driving, and remote sensing applications. Existing XAI methods aim to provide insights about the methodology used by the black-box model in making decisions by highlighting the most relevant regions within the input image that contribute to the model’s prediction. Recently, several XAI methods for image classification tasks have been introduced. In contrast, image segmentation has received comparatively less attention in the context of explainability, although it is a fundamental task in computer vision applications, especially in remote sensing. Only some research proposes gradient-based XAI algorithms for image segmentation. This paper adapts the recent gradient-free Sobol XAI method for semantic segmentation. To measure the performance of the Sobol method for segmentation, we propose a quantitative XAI evaluation method based on a learnable noise model. The main objective of this model is to induce noise on the explanation maps, where a higher induced noise signifies low accuracy and vice versa. A benchmark analysis is conducted to evaluate and compare the performances of three XAI methods, Seg-Grad-CAM, Seg-Grad-CAM++ and Seg-Sobol, using the proposed noise-based evaluation technique. This constitutes the first attempt to run and evaluate XAI methods using high-resolution satellite images. Our code is publicly available at GitHub

Performance Comparison of IEEE 802.11p, 802.11bd-draft and a Unique-Word-based PHY in Doubly-Dispersive Channels

International audienceIn this paper, we evaluate and make a comparison of the channel estimation performance for three different frame structures of IEEE 802.11p, IEEE 802.11bd-draft and a unique-word (UW)-based physical layer (PHY). As in vehicle-toeverything communication the wireless channel conditions may vary significantly depending on the environment and vehicle velocity, severe fading in both time and frequency domains may occur. Through simulation results, we show that the UWbased PHY achieves an interference-free performance of channel estimation via a low complexity technique, whereas the 802.11bd would need to employ a high complexity approach in order to achieve a comparable estimation performance

Performance Comparison of IEEE 802.11p, 802.11bd-draft and a Unique-Word-based PHY in Doubly-Dispersive Channels

International audienceIn this paper, we evaluate and make a comparison of the channel estimation performance for three different frame structures of IEEE 802.11p, IEEE 802.11bd-draft and a unique-word (UW)-based physical layer (PHY). As in vehicle-toeverything communication the wireless channel conditions may vary significantly depending on the environment and vehicle velocity, severe fading in both time and frequency domains may occur. Through simulation results, we show that the UWbased PHY achieves an interference-free performance of channel estimation via a low complexity technique, whereas the 802.11bd would need to employ a high complexity approach in order to achieve a comparable estimation performance

Temporal Averaging LSTM-based Channel Estimation Scheme for IEEE 802.11p Standard

International audienceIn vehicular communications, reliable channel estimation is critical for the system performance due to the doubly-dispersive nature of vehicular channels. IEEE 802.11p standard allocates insufficient pilots for accurate channel tracking. Consequently, conventional IEEE 802.11p estimators suffer from a considerable performance degradation, especially in high mobility scenarios. Recently, deep learning (DL) techniques have been employed for IEEE 802.11p channel estimation. Nevertheless, these methods suffer either from performance degradation in very high mobility scenarios or from large computational complexity. In this paper, these limitations are solved using a long short term memory (LSTM)-based estimation. The proposed estimator employs an LSTM unit to estimate the channel, followed by temporal averaging (TA) processing as a noise alleviation technique. Moreover, the noise mitigation ratio is determined analytically, thus validating the TA processing ability in improving the overall performance. Simulation results reveal the performance superiority of the proposed schemes compared to the recently proposed DL-based estimators, while recording a significant reduction in the computational complexity