[HTML][HTML] Current advances in imaging spectroscopy and its state-of-the-art applications
Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single
system and has gained widespread acceptance as a non-destructive scientific instrument for …
system and has gained widespread acceptance as a non-destructive scientific instrument for …
Hyperspectral and lidar data applied to the urban land cover machine learning and neural-network-based classification: A review
Rapid technological advances in airborne hyperspectral and lidar systems paved the way
for using machine learning algorithms to map urban environments. Both hyperspectral and …
for using machine learning algorithms to map urban environments. Both hyperspectral and …
Multimodal fusion transformer for remote sensing image classification
Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …
promising performance when compared with convolutional neural networks (CNNs). As a …
MFFCG–Multi feature fusion for hyperspectral image classification using graph attention network
UA Bhatti, M Huang, H Neira-Molina, S Marjan… - Expert Systems with …, 2023 - Elsevier
Classification methods that are based on hyperspectral images (HSIs) are playing an
increasingly significant role in the processes of target detection, environmental …
increasingly significant role in the processes of target detection, environmental …
Spectral–spatial morphological attention transformer for hyperspectral image classification
In recent years, convolutional neural networks (CNNs) have drawn significant attention for
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the …
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the …
MambaHSI: Spatial-spectral mamba for hyperspectral image classification
Transformer has been extensively explored for hyperspectral image (HSI) classification.
However, transformer poses challenges in terms of speed and memory usage because of its …
However, transformer poses challenges in terms of speed and memory usage because of its …
Hyperspectral unmixing using transformer network
Transformers have intrigued the vision research community with their state-of-the-art
performance in natural language processing. With their superior performance, transformers …
performance in natural language processing. With their superior performance, transformers …
Masked auto-encoding spectral–spatial transformer for hyperspectral image classification
Deep learning has certainly become the dominant trend in hyperspectral (HS) remote
sensing (RS) image classification owing to its excellent capabilities to extract highly …
sensing (RS) image classification owing to its excellent capabilities to extract highly …
A comparative review on multi-modal sensors fusion based on deep learning
The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …
data with characteristics of high volume, wide variety, and high integrity. However, traditional …
Few-shot hyperspectral image classification with self-supervised learning
Recently, few-shot learning (FSL) has been introduced for hyperspectral image (HSI)
classification with few labeled samples. However, existing FSL-based HSI classification …
classification with few labeled samples. However, existing FSL-based HSI classification …