We propose a three-player spectral generative adversarial network (GAN) architecture to afford GAN the ability to manage minority classes under imbalanced conditions. A class-dependent mixture generator spectral GAN (MGSGAN) was developed to force generated samples to remain within the actual distribution of the data. MGSGAN was able to generate minority classes, even when the imbalanced ratio of majority to minority classes was high. A classifier based on lower features was adopted along with a sequential discriminator to develop a three-player GAN game. The generative networks performed data augmentation to improve the classifier ’ s performance. The proposed method was validated using two hyperspectral image data sets and compared with state-of-the-art methods in two class-imbalanced settings corresponding with real data distributions.

Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for image-based remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, few-shot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of few-shot learning methods on satellite-based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from Unmanned Aerial Vehicles (UAVs), which are increasingly used in remote sensing studies. In this review, we provide an up-to-date overview of both existing and newly proposed few-shot classification techniques, along with appropriate datasets that are used for both satellite-based and UAV-based data. We demonstrate few-shot learning can effectively handle the diverse perspectives in remote sensing data. As an example application, we evaluate state-of-the-art approaches on a UAV disaster scene dataset, yielding promising results. Furthermore, we highlight the significance of incorporating explainable AI (XAI) techniques into few-shot models. In remote sensing, where decisions based on model predictions can have significant consequences, such as in natural disaster response or environmental monitoring, the transparency provided by XAI is crucial. Techniques like attention maps and prototype analysis can help clarify the decision-making processes of these complex models, enhancing their reliability. We identify key challenges including developing flexible few-shot methods to handle diverse remote sensing data effectively. This review aims to equip researchers with an improved understanding of few-shot learning’s capabilities and limitations in remote sensing, while pointing out open issues to guide progress in efficient, reliable and interpretable data-efficient techniques.

While evolving neuro-fuzzy systems have shown promise for learning from non-stationary streaming data with concept drift, most existing models lack transparency due to the limited interpretability of Takagi-Sugeno fuzzy architecture’s linear rule consequents. The lack of transparency limits the reliability of crucial applications. To address this limitation, this paper proposes a new evolving neuro-fuzzy system called X-Fuzz that enhances interpretability by integrating the LIME technique to provide local explanations and evaluates them using faithfulness and monotonicity metrics. X-Fuzz is rigorously tested on streaming datasets with diverse concept drifts via prequential analysis. Experiments demonstrate X-Fuzz’s capabilities in mining insights from large and dynamic data streams exhibiting diverse concept drifts including abrupt, gradual, recurring contextual, and cyclical drifts. In addition, for online runway exit prediction using real aviation data, X-Fuzz achieved 98.04% accuracy, significantly exceeding recent methods. With its balance of efficiency and transparency, X-Fuzz represents a promising approach for trustworthy evolving artificial intelligence that can handle complex, non-stationary data streams in critical real-world settings. We have made the X-Fuzz source code available in https://github.com/m-ferdaus/X Fuzz for reproducibility and facilitating future research.

Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems. Yet, the fusion encounters difficulties with extended distance detection due to the contrast between LiDAR’s sparse data and the dense resolution of cameras. Besides, discrepancies in the two data representations further complicate fusion methods. We introduce AYDIV, a novel framework integrating a tri-phase alignment process specifically designed to enhance long-distance detection even amidst data discrepancies. AYDIV consists of the Global Contextual Fusion Alignment Transformer (GCFAT), which improves the extraction of camera features and provides a deeper understanding of large-scale patterns; the Sparse Fused Feature Attention (SFFA), which fine-tunes the fusion of LiDAR and camera details; and the Volumetric Grid Attention (VGA) for a comprehensive spatial data fusion. AYDIV’s performance on the Waymo Open Dataset (WOD) with an improvement of 1.24% in mAPH value(L2 difficulty) and the Argoverse2 Dataset with a performance improvement of 7.40% in AP value demonstrates its efficacy in comparison to other existing fusion-based methods. Our code is publicly available at https://github.com/sanjay-810/AYDIV2