Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
[HTML][HTML] A review on deep learning in UAV remote sensing
Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …
capability, and brought important breakthroughs for processing images, time-series, natural …
Rotation-invariant attention network for hyperspectral image classification
Hyperspectral image (HSI) classification refers to identifying land-cover categories of pixels
based on spectral signatures and spatial information of HSIs. In recent deep learning-based …
based on spectral signatures and spatial information of HSIs. In recent deep learning-based …
Attention-based adaptive spectral–spatial kernel ResNet for hyperspectral image classification
Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …
Hyperspectral image classification with multi-attention transformer and adaptive superpixel segmentation-based active learning
Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
Spatial-spectral transformer for hyperspectral image classification
Recently, a great many deep convolutional neural network (CNN)-based methods have
been proposed for hyperspectral image (HSI) classification. Although the proposed CNN …
been proposed for hyperspectral image (HSI) classification. Although the proposed CNN …
[HTML][HTML] Deep learning classifiers for hyperspectral imaging: A review
Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …
deep learning (DL) techniques, which have demonstrated promising results in a wide range …
Hyperspectral image classification using group-aware hierarchical transformer
S Mei, C Song, M Ma, F Xu - IEEE Transactions on Geoscience …, 2022 - ieeexplore.ieee.org
Hyperspectral image (HSI) classification is a critical task with numerous applications in the
field of remote sensing. Although convolutional neural networks have achieved remarkable …
field of remote sensing. Although convolutional neural networks have achieved remarkable …
Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks
The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-
flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods …
flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods …
Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices
Background The development of techniques and methods for rapidly and reliably detecting
and analysing food quality and safety products is of significance for the food industry …
and analysing food quality and safety products is of significance for the food industry …