Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Recently, the rapid development of deep learning has greatly improved the performance of
image classification. However, a central problem in hyperspectral image (HSI) classification …

In this paper, we designed an end-to-end spectral-spatial residual network (SSRN) that
takes raw 3-D cubes as input data without feature engineering for hyperspectral image …

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

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 …

Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …

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 …

Feature extraction is of significance for hyperspectral image (HSI) classification. Compared
with conventional hand-crafted feature extraction, deep learning can automatically learn …

Hyperspectral image (HSI) contains a large number of spatial-spectral information, which
will make the traditional classification methods face an enormous challenge to discriminate …