Deep learning for remote sensing image classification: A survey
Remote sensing (RS) image classification plays an important role in the earth observation
technology using RS data, having been widely exploited in both military and civil fields …
technology using RS data, having been widely exploited in both military and civil fields …
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 …
Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network
Recent research has shown that using spectral–spatial information can considerably
improve the performance of hyperspectral image (HSI) classification. HSI data is typically …
improve the performance of hyperspectral image (HSI) classification. HSI data is typically …
Category-specific prototype self-refinement contrastive learning for few-shot hyperspectral image classification
Deep learning (DL) has been extensively used for hyperspectral image classification (HSIC)
with significant success, but the classification of high-dimensional hyperspectral image (HSI) …
with significant success, but the classification of high-dimensional hyperspectral image (HSI) …
A framework for evaluating land use and land cover classification using convolutional neural networks
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has …
for many environmental and social applications. The increase in availability of RS data has …
Hyperspectral images classification based on dense convolutional networks with spectral-wise attention mechanism
Hyperspectral images (HSIs) data that is typically presented in 3-D format offers an
opportunity for 3-D networks to extract spectral and spatial features simultaneously. In this …
opportunity for 3-D networks to extract spectral and spatial features simultaneously. In this …
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 …
Local Manifold Learning-Based -Nearest-Neighbor for Hyperspectral Image Classification
Approaches to combine local manifold learning (LML) and the k-nearest-neighbor (k NN)
classifier are investigated for hyperspectral image classification. Based on supervised LML …
classifier are investigated for hyperspectral image classification. Based on supervised LML …
Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale
The requirements for hydrological models have increased considerably during the previous
decades to cope with the resolution of extensive remotely sensed data sets and a number of …
decades to cope with the resolution of extensive remotely sensed data sets and a number of …
A review of modern approaches to classification of remote sensing data
This chapter presents an extensive review on the techniques proposed in the recent
literature for the classification of remote sensing (RS) images. Automatic classification …
literature for the classification of remote sensing (RS) images. Automatic classification …