With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

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 …

Classification is one of the most popular topics in hyperspectral remote sensing. In the last
two decades, a huge number of methods were proposed to deal with the hyperspectral data …

Hyperspectral data classification is a hot topic in remote sensing community. In recent years,
significant effort has been focused on this issue. However, most of the methods extract the …

With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …

Hyperspectral image classification has become a research focus in recent literature.
However, well-designed features are still open issues that impact on the performance of …

The availability of high-resolution remote sensing (HRRS) data has opened up the
possibility for new interesting applications, such as per-pixel classification of individual …

Convolutional neural networks (CNNs) have strong feature extraction capability, which have
been used to extract features from the hyperspectral image. Local binary pattern (LBP) is a …

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are
increasing, and pixel-oriented classification has attracted much attention. Recently, graph …

Deep learning (DL) has proven to be a promising technique for hyperspectral image (HSI)
classification. However, due to complex network structure and massive parameters, it is …