Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …

By considering the spectral signature as a sequence, recurrent neural networks (RNNs)
have been successfully used to learn discriminative features from hyperspectral images …

A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction
(FE) method is presented for hyperspectral image (HSI) classification using a convolutional …

In this paper, we propose a spectral–spatial feature based classification (SSFC) framework
that jointly uses dimension reduction and deep learning techniques for spectral and spatial …

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 …

As an unsupervised dimensionality reduction method, the principal component analysis
(PCA) has been widely considered as an efficient and effective preprocessing step for …

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