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 …
Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review
M Sheykhmousa, M Mahdianpari… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …
classification during the past two decades. Among these machine learning algorithms …
Spectral–spatial transformer network for hyperspectral image classification: A factorized architecture search framework
Neural networks have dominated the research of hyperspectral image classification,
attributing to the feature learning capacity of convolution operations. However, the fixed …
attributing to the feature learning capacity of convolution operations. However, the fixed …
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 …
Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox
Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …
Hyperspectral anomaly detection: A survey
Hyperspectral imagery can obtain hundreds of narrow spectral bands of ground objects. The
abundant and detailed spectral information offers a unique diagnostic identification ability for …
abundant and detailed spectral information offers a unique diagnostic identification ability for …
Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review
Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …
images, we have witnessed a growing interest of the remote sensing community in …
Deep learning for hyperspectral image classification: An overview
Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …
sensing. In general, the complex characteristics of hyperspectral data make the accurate …
Cascaded recurrent neural networks for hyperspectral image classification
By considering the spectral signature as a sequence, recurrent neural networks (RNNs)
have been successfully used to learn discriminative features from hyperspectral images …
have been successfully used to learn discriminative features from hyperspectral images …