Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport–Beach–Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

Research supporting improved anomaly detection performance benefits a wide range of technical applications, and thus, the definition of what anomalies are and the subsequent means to detect them are wide ranging. In this treatment, an overview of the development of an anomaly detection approach based on spectral signatures obtained with hyperspectral unmixing is presented. The algorithm is designed to address some of the shortcomings of current techniques whose functionality is dependent upon normalized differences between discrete frequencies or spectral components, or those based on estimated distances between background spectra and pixels under test. Details about the extracted endmembers and their use for effective anomaly detection will be presented as well as, some thoughts on the expected requirements for future machine learning based implementations.

Machine Learning (ML) systems are currently used widely. While the requirements elicitation and management processes are well understood and standardized for software systems, they have not been adopted for ML systems. These processes pose additional complexity due to the multitude of stakeholders, their understanding of ML concepts, and their varied requirement orientations. Communication among the stakeholders is a must to generate an appropriate requirements document to guide the development of the system. Another aspect of ML systems is their black box mode of operation. It is essential to know the reasoning behind the predictions of these systems, especially for critical applications. These explainability requirements also must be taken into consideration. This paper reviews state of the art in these two areas to form a basis for developing a framework for stakeholder interaction to facilitate the requirement management of ML systems.

Complex machine learning (ML) algorithms consistently outperform traditional ML models, yielding significantly improved results. Furthermore, these complex system uses large amounts of data with many features to train, increasing their predictive power but reducing our ability to explain them. Thus, complex ML models primarily operate like black boxes. Explaining their inference processes and predictions poses a challenge due to the necessity to account for millions of interacting weights in a complicated way. Still, we need to clarify their inner workings and procedures, as understanding a model can improve transparency and promote trustworthiness. Therefore, ‘Explainability’ assumes pivotal importance, as it enhances accuracy and broadens the scope of applicability for complex ML methods within critical domains. Thus, in this review paper, we have investigated the complete ML system development lifecycle, compiled recommendations to cover explainability gaps from diverse literature sources, listed different methods of interpretability and explainability, and improved our explainability-augmented ML system development life cycle for all stakeholders to enhance trustworthiness.

Accurate crop mapping—identifying both the location and types of crops—is crucial for effective agricultural planning and informed decision-making. Advances in remote sensing, notably hyperspectral imagery from unmanned aerial vehicles (UAV), greatly enhance the efficiency and accuracy of crop mapping, reducing the reliance on traditional, labor-intensive field surveys. However, applying deep classifiers directly to hyperspectral data can lead to overfitting. Conversely, deep semantic segmentation models may struggle due to limited annotated hyperspectral imagery. To address this problem, we propose enhancing a U-Net-style model—originally trained on RGB imagery—by incorporating task adaptation, a custom loss function, and a spectral attention mechanism to better optimize it for hyperspectral data and improve crop mapping performance. Our proposed segmentation network achieved 76.6% accuracy and a 74.9% Dice score on a UAV-acquired hyperspectral agricultural dataset, which is competitive and well-rounded compared to other state-of-the-art methods while offering significantly lower computational complexity.