Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
Domain adaptation for the classification of remote sensing data: An overview of recent advances
The success of the supervised classification of remotely sensed images acquired over large
geographical areas or at short time intervals strongly depends on the representativity of the …
geographical areas or at short time intervals strongly depends on the representativity of the …
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 …
Few-shot hyperspectral image classification with unknown classes using multitask deep learning
Current hyperspectral image classification assumes that a predefined classification system
is closed and complete, and there are no unknown or novel classes in the unseen data …
is closed and complete, and there are no unknown or novel classes in the unseen data …
A new deep convolutional neural network for fast hyperspectral image classification
Artificial neural networks (ANNs) have been widely used for the analysis of remotely sensed
imagery. In particular, convolutional neural networks (CNNs) are gaining more and more …
imagery. In particular, convolutional neural networks (CNNs) are gaining more and more …
Domain adaptation in remote sensing image classification: A survey
Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …
samples for model training. When labeled samples are unavailable or labeled samples have …
Deep learning-based classification of hyperspectral data
Classification is one of the most popular topics in hyperspectral remote sensing. In the last
two decades, a huge number of methods were proposed to deal with the hyperspectral data …
two decades, a huge number of methods were proposed to deal with the hyperspectral data …
Spectral–spatial classification of hyperspectral data based on deep belief network
Hyperspectral data classification is a hot topic in remote sensing community. In recent years,
significant effort has been focused on this issue. However, most of the methods extract the …
significant effort has been focused on this issue. However, most of the methods extract the …
Spectral–spatial unified networks for hyperspectral image classification
In this paper, we propose a spectral–spatial unified network (SSUN) with an end-to-end
architecture for the hyperspectral image (HSI) classification. Different from traditional …
architecture for the hyperspectral image (HSI) classification. Different from traditional …