Ensemble learning approaches based on covariance pooling of CNN features for high resolution remote sensing scene classification
Deep ensemble learning model based on covariance pooling of multi-layer cnn features
Compared to standard deep convolutional neural networks (CNN) which include a global
average pooling operator, second-order neural networks have a global covariance pooling …
average pooling operator, second-order neural networks have a global covariance pooling …
Remote sensing scene classification based on covariance pooling of multi-layer cnn features guided by saliency maps
The new generation of remote sensing imaging sensors enables high spatial, spectral and
temporal resolution images with high revisit frequencies. These sensors allow the …
temporal resolution images with high revisit frequencies. These sensors allow the …
Hyperspectral image classification using neural networks with effect of feature optimization on fused convolutional features
Analysis of data and synthesis for hyper spectral imaging (HSI) is a new branch of remotely
sensed data and planet surveillance technologies. Classification techniques with the help of …
sensed data and planet surveillance technologies. Classification techniques with the help of …
Ensemble learning methods on the space of covariance matrices: application to remote sensing scene and multivariate time series classification
S Akodad - 2021 - theses.hal.science
In view of the growing success of second-order statistics in classification problems, the work
of this thesis has been oriented towards the development of learning methods in manifolds …
of this thesis has been oriented towards the development of learning methods in manifolds …