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 …

Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed
images. Hyperspectral imagery includes varying bands of images. Convolutional neural …

Currently, the different deep neural network (DNN) learning approaches have done much for
the classification of hyperspectral images (HSIs), especially most of them use the …

An accurate identification of objects from the acquisition system depends on the clear
segmentation and classification of remote sensing images. With the limited financial …

Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …

Deep learning has achieved great successes in conventional computer vision tasks. In this
paper, we exploit deep learning techniques to address the hyperspectral image …

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 …

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 …

To improve the accuracy and generalization ability of hyperspectral image classification, a
feature extraction method integrating principal component analysis (PCA) and local binary …