Hyperspectral Image
Hyperspectral imaging (HSI) captures detailed spectral information across numerous bands, enabling precise material identification and scene analysis beyond the capabilities of traditional RGB or multispectral imaging. Current research heavily focuses on improving HSI classification and reconstruction using advanced deep learning architectures, such as transformers and state-space models (SSMs), often incorporating spatial context and addressing challenges like computational efficiency and data scarcity through techniques like self-supervised learning and test-time training. These advancements have significant implications for diverse fields, including remote sensing, precision agriculture, medical imaging, and environmental monitoring, offering enhanced capabilities for material identification, object detection, and scene understanding.
Papers
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier
Asma Elmaizi, Elkebir Sarhrouni, Ahmed Hammouch, Nacir Chafik
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
Hasna Nhaila, Asma Elmaizi, Elkebir Sarhrouni, Ahmed Hammouch
Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information
Hasna Nhaila, Asma Elmaizi, Elkebir Sarhrouni, Ahmed Hammouch
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images
Asma Elmaizi, Hasna Nhaila, Elkebir Sarhrouni, Ahmed Hammouch, Chafik Nacir
A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification
A. Elmaizi, E. Sarhrouni, A. Hammouch, C. Nacir
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images
Asma Elmaizi, Elkebir Sarhrouni, Ahmed hammouch, Chafik Nacir