Advanced spectral classifiers for hyperspectral images: A review
Hyperspectral image classification has been a vibrant area of research in recent years.
Given a set of observations, ie, pixel vectors in a hyperspectral image, classification …
Given a set of observations, ie, pixel vectors in a hyperspectral image, classification …
An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges
Hyperspectral images (HSIs) have a cube form containing spatial information in two
dimensions and rich spectral information in the third one. The high volume of spectral bands …
dimensions and rich spectral information in the third one. The high volume of spectral bands …
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 …
Hyperspectral image classification with convolutional neural network and active learning
Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …
classification recently. However, its success is greatly attributed to numerous labeled …
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 …
Hyperspectral image classification with deep feature fusion network
Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and
achieved good performance. In general, deep models adopt a large number of hierarchical …
achieved good performance. In general, deep models adopt a large number of hierarchical …
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 …
Spectral–spatial hyperspectral image classification with edge-preserving filtering
The integration of spatial context in the classification of hyperspectral images is known to be
an effective way in improving classification accuracy. In this paper, a novel spectral-spatial …
an effective way in improving classification accuracy. In this paper, a novel spectral-spatial …
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 …
Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels
For the classification of hyperspectral images (HSIs), this paper presents a novel framework
to effectively utilize the spectral-spatial information of superpixels via multiple kernels, which …
to effectively utilize the spectral-spatial information of superpixels via multiple kernels, which …