Land-use/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery
Due to the abundant features of high spatial resolution (HSR) remote sensing images,
change detection of these images is crucial to understanding the land-use and land-cover …
change detection of these images is crucial to understanding the land-use and land-cover …
[HTML][HTML] Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review
Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …
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 …
Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made …
Sudden-onset natural and man-made disasters represent a threat to the safety of human life
and property. Rapid and accurate building damage assessment using bitemporal high …
and property. Rapid and accurate building damage assessment using bitemporal high …
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 …
WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional …
Unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral
imagery with a high spatial resolution (which we refer to here as H 2 imagery). As a result of …
imagery with a high spatial resolution (which we refer to here as H 2 imagery). As a result of …
Perceiving spectral variation: Unsupervised spectrum motion feature learning for hyperspectral image classification
Y Sun, B Liu, X Yu, A Yu, K Gao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …
achieved significant development. The superior capability of feature extraction from these …
Central attention network for hyperspectral imagery classification
In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two
principles for spectral–spatial feature extraction of HSI are built, including the foundation of …
principles for spectral–spatial feature extraction of HSI are built, including the foundation of …
A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification
Deep learning techniques have been widely applied to hyperspectral image (HSI)
classification and have achieved great success. However, the deep neural network model …
classification and have achieved great success. However, the deep neural network model …
Few-shot learning with class-covariance metric for hyperspectral image classification
Recently, embedding and metric-based few-shot learning (FSL) has been introduced into
hyperspectral image classification (HSIC) and achieved impressive progress. To further …
hyperspectral image classification (HSIC) and achieved impressive progress. To further …