In recent years, the appearance of the broad learning system (BLS) is poised to
revolutionize conventional artificial intelligence methods. It represents a step toward building …

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 …

Convolutional neural network (CNN) has demonstrated impressive ability to represent
hyperspectral images and to achieve promising results in hyperspectral image classification …

In this paper, we propose a new method for hyperspectral images (HSI) classification,
aiming to take advantage of both manifold learning‐based feature extraction and neural …

Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to
hyperspectral images (HSIs) have been found very effective in characterizing significant …

In recent years, deep learning based methods have attracted broad attention in the field of
hyperspectral image classification. However, due to the massive parameters and the …

The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral
imagery has led to significant improvements in terms of classification performance. The task …

Images are fused to produce a composite image by combining key characteristics of the
source images in image fusion. It makes the fused image better for human vision and …

In critical applications, such as medical diagnosis, security related systems, and so on, the
cost or risk of action taking based on incorrect classification can be very high. Hence …

In recent years, the hyperspectral image (HSI) classification based on generative adversarial
networks (GANs) has achieved great progress. GAN-based classification methods can …