Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

An attention mechanism assigns different weights to different features to help a model select
the features most valuable for accurate classification. However, the traditional attention …

Hyperspectral image classification (HSIC) methods based on convolutional neural network
(CNN) continue to progress in recent years. However, high complexity, information …

With the increasing maturity of optics and photonics, hyperspectral technology has also
greatly advanced. Hyperspectral images composed of hundreds of adjacent bands and …

Hyperspectral image (HSI) classification has been one of the most important tasks in the
remote sensing community over the last few decades. Due to the presence of highly …

This study presents a deep extraction of localized spectral features and multi-scale spatial
features convolution (LSMSC) framework for spectral-spatial fusion based classification of …

Convolutional neural network (CNN) has been successfully introduced to hyperspectral
image (HSI) classification and achieved effective performance. With the depth of the CNN …

The existing hyperspectral image (HSI) classification encounters the obstacle of improving
the classification accuracy with limited labeled samples. In this context, as a typical …

Deep learning has achieved impressive results on hyperspectral image (HSI) classification,
which generally requires sufficient training samples and a huge number of parameters …