[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 …
Remote sensing image scene classification: Benchmark and state of the art
Remote sensing image scene classification plays an important role in a wide range of
applications and hence has been receiving remarkable attention. During the past years …
applications and hence has been receiving remarkable attention. During the past years …
Hyperspectral image classification—Traditional to deep models: A survey for future prospects
Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …
because it benefits from the detailed spectral information contained in each pixel. Notably …
Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …
provided end users with rich spectral, spatial, and temporal information. They have made a …
Remote sensing image scene classification using CNN-CapsNet
W Zhang, P Tang, L Zhao - Remote Sensing, 2019 - mdpi.com
Remote sensing image scene classification is one of the most challenging problems in
understanding high-resolution remote sensing images. Deep learning techniques …
understanding high-resolution remote sensing images. Deep learning techniques …
Hyperspectral remote sensing data analysis and future challenges
JM Bioucas-Dias, A Plaza… - … and remote sensing …, 2013 - ieeexplore.ieee.org
Hyperspectral remote sensing technology has advanced significantly in the past two
decades. Current sensors onboard airborne and spaceborne platforms cover large areas of …
decades. Current sensors onboard airborne and spaceborne platforms cover large areas of …
Active learning with convolutional neural networks for hyperspectral image classification using a new Bayesian approach
Hyperspectral imaging is a widely used technique in remote sensing in which an imaging
spectrometer collects hundreds of images (at different wavelength channels) for the same …
spectrometer collects hundreds of images (at different wavelength channels) for the same …
Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous
field view in hundreds or thousands of spectral channels with higher spectral resolution than …
field view in hundreds or thousands of spectral channels with higher spectral resolution than …
A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification
W Han, R Feng, L Wang, Y Cheng - ISPRS Journal of Photogrammetry and …, 2018 - Elsevier
High resolution remote sensing (HRRS) image scene classification plays a crucial role in a
wide range of applications and has been receiving significant attention. Recently …
wide range of applications and has been receiving significant attention. Recently …
A new deep generative network for unsupervised remote sensing single-image super-resolution
JM Haut, R Fernandez-Beltran… - … on Geoscience and …, 2018 - ieeexplore.ieee.org
Super-resolution (SR) brings an excellent opportunity to improve a wide range of different
remote sensing applications. SR techniques are concerned about increasing the image …
remote sensing applications. SR techniques are concerned about increasing the image …